arXiv:2402.14658v2 [cs.SE] 28 Feb 2024

OpenCodeInterpreter: Integrating Code Generation with

Execution and Reﬁnement

Tianyu Zheng

, Ge Zhang

1,2

, Tianhao Shen

, Xueling Liu

Bill Yuchen Lin

, Jie Fu

1,4

, Wenhu Chen

1,2

, Xiang Yue

1,5†

Multimodal Art Projection Research Community,

University of Waterloo,

Allen Institute for Artiﬁcial Intelligence,

HKUST,

IN.AI Research

{zhengtianyu0428, xiangyue.work}@gmail.com, [email protected]

https://opencodeinterpreter.github.io

Abstract

The introduction of large language models has

signiﬁcantly advanced code generation. How-

ever, open-source models often lack the execu-

tion capabilities and iterative reﬁnement of ad-

vanced systems like the GPT-4 Code Interpreter.

To address this, we introduce OpenCodeIn-

terpreter, a family of open-source code sys-

tems designed for generating, executing, and

iteratively reﬁning code. Supported by Code-

Feedback, a dataset featuring 68K multi-turn in-

teractions, OpenCodeInterpreter integrates ex-

ecution and human feedback for dynamic code

reﬁnement. Our comprehensive evaluation of

OpenCodeInterpreter across key benchmarks

such as HumanEval, MBPP, and their enhanced

versions from EvalPlus reveals its exceptional

performance. Notably, OpenCodeInterpreter-

33B achieves an accuracy of 83.2 (76.4) on the

average (and plus versions) of HumanEval and

MBPP, closely rivaling GPT-4’s 84.2 (76.2) and

further elevates to 91.6 (84.6) with synthesized

human feedback from GPT-4. OpenCodeInter-

preter brings the gap between open-source code

generation models and proprietary systems like

GPT-4 Code Interpreter.

1 Introduction

Code generation has been a pivotal challenge

within computer science for several decades. Re-

cently, the landscape of code generation has been

revolutionized by the advent of large language

models (LLMs) pre-trained on extensive code cor-

pora (Nijkamp et al., 2022; Christopoulou et al.,

2022; Zheng et al., 2023; Li et al., 2023a; Wang

et al., 2023c; Roziere et al., 2023; Guo et al., 2024).

These models have showcased remarkable capabil-

ities in generating code that accurately aligns with

Equal Contributions.

†

Corresponding Author.

-------------------------------------------------

error Traceback (most recent call last)

Input In [1], in <cell line: 11>()

error: nothing to repeat at position 21

Execution Result: True

Sure, here is a Python function using Regular

Expression (regex) to check if a string is a valid

IPv6 address:

```python

def is_valid_ipv6(ip):

... ...

print(is_valid_ipv6("2404:6800:4003:c02::8a"))#True

```

I apologize for the confusion. It seems like there

was a mistake in the regular expression pattern.

Let's correct that. Here is the corrected function:

```python

def is_valid_ipv6(ip):

... ...

print(is_valid_ipv6("2404:6800:4003:c02::8a"))#True

```

Great! The function appears to be working as

expected now. It correctly identifies the strings

“2404:6800:4003:c02::8a”…

Let me know if there's anything else I can assist

you with!

Sure, let me break down the regular expression for

you:

- ([0-9a-fA-F]{1,4}:){7,7}[0-9a-fA-F]{1,4}: This

pattern matches an IPv6 address that consists of …

Implement a Python function to check if a string

is a valid IPv6 address with regular expressions.

Can you explain the generated regular expression

in detail and make this function more maintainable?

76.2

79.3

81.1

82.9

87.2

88.4

89.7

92.7

100

OpenInterpreter-6.7B OpenInterpreter-33B

HumanEval Pass@1

Single Turn w/ Exec Feedback

w/ Human Feedback w/ H.F. (Oracle)

GPT4 CI

GPT3.5 CI

GPT4

Figure 1: Overview of the OpenCodeInterpreter and its

pass@1 accuracy on the HumanEval. With appropriate

feedback, OpenCodeInterpreter-33B achieves perfor-

mance comparable to that of the GPT-4 Code Interpreter.

user intents, thus providing substantial support for

software development (GitHub, 2023).

To unleash the capabilities of pre-trained code

models, instruction-tuning methods have been de-

arXiv:2402.14658v2 [cs.SE] 28 Feb 2024

veloped. For instance, CodeAlpaca (Chaudhary,

2023) comprises 20K code instructions automat-

ically generated by applying self-instruct (Wang

et al., 2023b) to ChatGPT, utilizing 21 seed tasks as

the foundation. To further reﬁne the coding proﬁ-

ciency of LLMs, Luo et al. (2023) introduces Code

Evol-Instruct, a method that applies a variety of

heuristics to enrich the complexity of initial code

instructions, building upon the dataset provided

by CodeAlpaca. Meanwhile, MagicCoder (Wei

et al., 2023) employs a robust LLM to generate

novel coding challenges, sourcing inspiration from

a diverse range of open-source code snippets. Ad-

ditionally, WaveCoder (Yu et al., 2023) implements

an LLM generator-discriminator framework for cre-

ating code instruction data, offering customization

and control over the data generation process.

Despite these advancements, current code mod-

els are constrained by their capacity to utilize feed-

back for reﬁnement. Essentially, feedback can have

two forms: (1) execution feedback, which includes

execution outputs and diagnostics, and (2) human

feedback, comprising follow-up guidance or in-

structions from users. Execution feedback plays a

vital role in enabling models to rectify syntactic and

logical errors, and human feedback aids models in

better understanding user instructions, facilitating

the generation of solutions that more closely align

with user expectations.

To address these challenges, we propose Open-

CodeInterpreter, a family of open-source code sys-

tems designed for generating, executing, and it-

eratively reﬁning code. OpenCodeInterpreter is

trained on our constructed Code-Feedback dataset,

which features 68K multi-turn interactions between

users, code models, and compilers. OpenCodeIn-

terpreter uniquely integrates both execution and

human feedback, employing compiler diagnostics

to rectify errors and human insights to reﬁne code

generation. This approach allows OpenCodeInter-

preter to produce solutions that are both technically

sound and closely matched to user requirements,

signiﬁcantly boosting its overall performance.

Our thorough evaluation of OpenCodeInter-

preter on widely recognized benchmarks, such as

HumanEval (Chen et al., 2021), MBPP (Austin

et al., 2021), and their augmented counterparts

from EvalPlus (Liu et al., 2023), highlights its su-

perior ability to generate and iteratively reﬁne code,

achieving exemplary standards of quality and func-

tionality. Remarkably, OpenCodeInterpreter-33B

secures an impressive accuracy of 83.2 (76.4) on

the average (and plus versions) of HumanEval and

MBPP, showcasing performance on par with GPT-

4’s 84.2 (76.2). Furthermore, when augmented with

synthesized human feedback from GPT-4, Open-

CodeInterpreter’s performance notably increases

to 91.6 (84.6). OpenCodeInterpreter thereby es-

tablishes a new benchmark in code generation, ef-

fectively narrowing the performance gap between

open-source models and sophisticated proprietary

systems like the GPT-4 Code Interpreter.

2 Code-Feedback

In this section, we detail the creation of our code in-

struction tuning dataset, Code-Feedback (Figure 2),

designed to train OpenCodeInterpreter. Code-

Feedback is crafted to meet speciﬁc criteria: 1)

Diverse and challenging real-world queries: The

dataset should encompass a wide range of queries

derived from real-world coding tasks, presenting

both diversity and complexity. 2) Multi-turn di-

alogue structure: Code-Feedback is structured

as multi-turn dialogues, incorporating two types

of feedback: execution feedback, which includes

outputs and diagnostics from compilers, and hu-

man feedback, consisting of additional guidance

or instructions from users. 3) Interleaved text

and code responses: Each response is expected

to provide responses that blend natural language

explanations with code snippets, offering a holistic

approach to solving coding queries.

To assemble a dataset that fulﬁlls these desider-

ata, we have employed ﬁve distinct methods. Ex-

amples of these ﬁve categories can be found in Ap-

pendix E. The sources of our queries fall into two

main categories: a variety of open-source datasets

and coding challenges from LeetCode. In the next

subsections, we will discuss how we develop data

construction methods to meet the three aforemen-

tioned criteria from the two data sources.

2.1 Coding Queries from Open-source Data

We have aggregated 287k queries from four dis-

tinguished open-source code instruction tuning

datasets: Magicoder-OSS-Instruct

, Python code

subset of ShareGPT

, Magicoder-Evol-Instruct

and Evol-Instruct-Code

. To reﬁne this extensive

hf.co/datasets/HuggingFaceH4/CodeAlpaca_20K

hf.co/datasets/ise-uiuc/Magicoder-OSS-Instruct-75K

hf.co/datasets/ajibawa-2023/Python-Code-23k-

ShareGPT

hf.co/datasets/ise-uiuc/Magicoder-Evol-Instruct-110K

hf.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1

Open-source

Code Instruction

Tuning Datasets

Execute

Challenging

Query Pool

Similar Query

Packing

Code

Correction

Human Feedback

Simulation

Similar Problem

LeetCode

Follow-Up

Query Filtering

Dataset #Sample #Turn M.T E.F H.F

CodeAlpaca

20k 20K ✗ ✗ ✗

Magicoder-OSS-Instruct

75K 75K ✗ ✗ ✗

Python-Code-ShareGPT

23K 23K ✗ ✗ ✗

Magicoder-Evol-Instruct

111K 111K ✗ ✗ ✗

EvolInstruct-Code

80k 80K ✗ ✗ ✗

Code-Feedback (Ours) 68K 192K ✔ ✔ ✔

Single-turn Packing 16K 33.5K ✔ ✗ ✔

Interaction Simulation 51K 155.5K ✔ ✔ ✔

Code Correction 0.5K 1.2K ✔ ✔ ✗

LeetCode Similar Problem 0.3K 0.65K ✔ ✗ ✔

LeetCode Follow-Up 0.2K 0.76K ✔ ✗ ✔

Figure 2: Summary of our proposed dataset Code-Feedback construction and comparison with existing code

instruction tuning datasets. M.T: Multi Turn, E.F: Execute Feedback, H.F: Human Feedback.

collection and isolate the most intricate and in-

formative instructions, we employ a very capa-

ble open-source chat model, Qwen-72B-Chat (Bai

et al., 2023), for a selective ﬁltering process. This

involves the LLM assessing each code query and

its corresponding response within the compiled

datasets on a complexity score from 1 to 5. Only

the most challenging queries, with ratings of 4 or

5, were retained for our seed set, ensuring a focus

on the most difﬁcult instructions. To guarantee the

robustness of our selection, this ﬁltering operation

is repeated with two distinct prompts (detailed in

Appendix A), thereby solidifying the complexity of

our ﬁnal query selection. This meticulous process

resulted in 156k high-quality single-turn code in-

structions as the challenging query pool. Detailed

statistics of this data compilation are provided in

Appendix A.

Subsequently, we describe three methods em-

ployed to transform this curated single-turn data

into multi-turn dialogues enriched with both execu-

tion and human feedback.

Singe-turn Packing. A direct approach to craft-

ing multi-turn data is to group single-turn query-

response pairs into multi-turn formats. Inspired

by in-context pre-training techniques (Shi et al.,

2023), which consolidate similar sequences to fos-

ter model learning of dependencies among related

documents, we merge similar single-turn query-

response pairs to form multi-turn dialogues.

Utilizing the BERT-base embedding (Devlin

et al., 2019), we convert queries into vectorized rep-

resentations. For each query, the

-nearest neigh-

bors algorithm is employed to identify its four clos-

est counterparts. From these, we randomly select

two or three to assemble multi-turn sequences. To

maintain data uniqueness, once a query is chosen

as a neighbor, it is exempt from future selections as

a neighboring query, ensuring no single instruction

is repeated across the dataset. Should a query’s

potential neighbors have been previously utilized,

that query is bypassed. This method results in the

creation of 16.6K multi-turn instances derived from

105K single-turn instances.

Interaction Simulation. Gathering authentic hu-

man interaction data poses signiﬁcant challenges.

To replicate a realistic code interpreter usage sce-

nario, we developed a simulator using GPT-3.5 and

GPT-4. For each selected query, GPT-3.5 ﬁrst gen-

erates a preliminary response from which we ex-

tract the code snippet and execute it. The outcome

of this execution, along with any compiler diagnos-

tics, is then fed into GPT-4 to elicit a follow-up

response. This cycle is repeated until GPT-4 de-

livers what it deems a correct solution or until a

maximum of three iterations is reached.

Subsequently, we introduce simulated human

feedback into the interaction. We predeﬁne ten

common feedback categories, including issues re-

lated to syntax and formatting, efﬁciency, func-

tionality, clarity, bugs, security, compatibility, re-

source use, scalability, and best practices, with each

category detailed in Appendix B. GPT-4 is then

prompted to select the most relevant feedback for

the scenario and generate appropriate responses

within that feedback category. By incorporating

this simulated feedback into the dialogue history,

GPT-4 is encouraged to reﬁne its solutions fur-

ther, mimicking intricate user-model exchanges

and demonstrating self-correction in response to

human input. Through this simulation approach,

we have constructed 51K examples, effectively cap-

turing the nuanced dynamics of user interactions

and feedback-driven solution reﬁnement.

Code Correction. To boost the model’s error-

handling capabilities, we include a focused stage

in our data compilation that generates 500 speciﬁc

error correction interactions. We initiate this by

prompting GPT-4 to intentionally produce incor-

rect code snippets, as outlined in Appendix B. The

model then uses the error messages from executing

these snippets as cues for corrections. This ap-

proach mirrors the real-life coding cycle, where de-

velopers continuously debug and reﬁne their code,

thus enriching our dataset with a broad spectrum

of error correction examples. Following this, we

replace the initial prompts that resulted in incorrect

code with the ones that encourage the generation

of correct code outputs. This method ensures the

model learns from both successful code genera-

tion and error identiﬁcation and correction, signif-

icantly enhancing its problem-solving skills and

understanding of the debugging process.

2.2 Coding Challenges from LeetCode

LeetCode Similar Problem. Drawing inspiration

from the practice among programmers of honing

their skills through LeetCode challenges, we gather

similar LeetCode questions and their solutions

from the TACO dataset (Li et al., 2023b). Leet-

Code

categorizes related questions through tags,

facilitating the extraction of connected problems.

TACO ensures the LeetCode dataset is cleansed

to prevent any unintended impact on other task

datasets, such as HumanEval and MBPP. By amal-

gamating associated LeetCode questions, we com-

pile 303 multi-turn instances, enriching the dataset

with varied coding challenges.

LeetCode Follow-up Question. We further delve

into the LeetCode dataset to isolate solutions to

identical questions that differ in time or space com-

plexity or are implemented in various programming

languages. This process of aggregating diverse

solutions to the same LeetCode questions yields

200 multi-round instances, showcasing alternative

problem-solving approaches.

Given the original LeetCode solutions often lack

comprehensive natural language explanations, we

engage GPT-4 to enhance these solutions with in-

tegrated text explanations and code snippets, stan-

dardizing all instances into a consistent format. The

speciﬁc prompts used to guide GPT-4 in this enrich-

https://leetcode.com/problemset/

ment process are detailed in Appendix C, ensuring

clarity and educational value in the responses.

3 Experimental Setup

Training Setup. We select two capable base

models CodeLlama (Roziere et al., 2023) and

DeepSeekCoder (Guo et al., 2024) varying capaci-

ties to illustrate the dataset’s universal applicability

and beneﬁts across different scales (7B, 13B, 34B,

70B). We maintain uniform hyperparameter conﬁg-

urations across all models. We ﬁne-tune the base

models for 3 epochs. The learning rate is set as

2e-5 with a 0.05 warm-up ratio and a cosine sched-

uler. We impose a token cutoff length of 4096 to

maintain consistency in the input size.

To optimize the ﬁne-tuning process, we strategi-

cally combine high-quality single-turn data from

the WizardCoder 110k dataset with our Code-

Feedback at a ratio of 2:1. Blending with single-

turn high-quality data may further boost the coding

ability. This blend is carefully selected and more

details are discussed in Table 2.

Evaluation Setup. Our evaluation framework pri-

marily leverages HumanEval (Chen et al., 2021)

and MBPP (Austin et al., 2021), two benchmarks

renowned for their rigorous testing of code gener-

ation capabilities. Acknowledging the limitations

of their original test suites in covering all edge

cases (Liu et al., 2023), we further incorporate their

extended versions, HumanEval+ and MBPP+, uti-

lizing the EvalPlus framework (Liu et al., 2023) for

a more comprehensive assessment.

In line with best practices outlined in recent

studies (Liu et al., 2023; Chen et al., 2023),

OpenCodeInterpreter’s solutions are generated via

greedy decoding. For comparisons involving GPT-

3.5 Turbo (OpenAI, 2022) and GPT-4 Turbo (Ope-

nAI, 2023), we maintain a temperature setting of

0. EvalPlus’s uniﬁed sanitizer tool post-processes

these solutions, which are then evaluated across the

four benchmarks using EvalPlus’s toolset.

For single-turn code generation, we craft a sim-

ple instruction to encapsulate the original prompt,

forming a new input for the model. The exact

prompts are detailed in Appendix D, and we assess

the model’s performance using the pass@1 metric,

as per EvalPlus’s guidelines.

Our analysis extends to multi-turn pass rates

to explore OpenCodeInterpreter’s proﬁciency in

reﬁning code through iterative feedback. This as-

pect of the evaluation draws on execution results

Model Size Type

Open-source

HumanEval (+) MBPP (+) Average (+)

Model Data

GPT-4 Turbo 85.4 (81.7) 83.0 (70.7) 84.2 (76.2)

+ Execution Feedback

- - ◦ ◦

88.0 (84.2) 92.0 (78.2) 90.0 (81.2)

GPT-3.5 Turbo 72.6 (65.9) 81.7 (69.4) 77.2 (67.7)

+ Execution Feedback

- - ◦ ◦

76.8 (70.7) 87.0 (73.9) 81.9 (72.3)

Gemini Pro (Google et al., 2023) - - ◦ ◦ 63.4 (55.5) 72.9 (57.9) 68.2 (56.7)

∼7B Scale

StarCoder (Li et al., 2023a) 7B Base • • 24.4 (20.7) 33.1 (28.8) 28.8 (24.8)

CodeT5+ (Wang et al., 2023c) 6B Base • • 29.3 (23.8) 51.9 (40.9) 40.6 (32.4)

CodeGen-Mono (Nijkamp et al., 2022) 6B Base • • 29.3 (25.6) 49.9 (42.1) 39.6 (33.9)

Mistral (Jiang et al., 2023) 7B Base • ◦ 28.7 (23.2) 50.1 (40.9) 39.4 (32.1)

OpenChat (Wang et al., 2023a) 7B Instruct • • 72.0 (67.1) 62.7 (52.9) 67.4 (60.0)

CodeLlama-Python (Roziere et al., 2023) 7B Base • ◦ 37.8 (34.1) 57.6 (45.4) 47.7 (39.8)

WizardCoder-CL (Luo et al., 2023) 7B Instruct ◦ ◦ 48.2 (40.9) 56.6 (47.1) 52.4 (44.0)

Magicoder-CL (Wei et al., 2023) 7B Instruct • • 60.4 (55.5) 64.2 (52.6) 62.3 (54.1)

Magicoders-S-CL (Wei et al., 2023) 7B Instruct • • 70.7 (66.5) 68.4 (56.6) 69.6 (61.6)

OpenCodeInterpreter-CL 72.6 (67.7) 66.4 (55.4) 69.5 (61.6)

+ Execution Feedback

7B Instruct • •

75.6 (70.1) 69.9 (60.7) 72.8 (65.4)

DeepseekCoder (Guo et al., 2024) 6.7B Base • ◦ 47.6 (39.6) 70.2 (56.6) 58.9 (48.1)

DeepseekCoder-Instruct 73.8 (70.1) 73.2 (63.4) 73.5 (66.8)

+ Execution Feedback

6.7B Instruct • ◦

80.5 (75.6) 79.9 (70.4) 80.2 (73.0)

Magicoder-DS (Wei et al., 2023) 6.7B Instruct • • 66.5 (60.4) 75.4 (61.9) 71.0 (61.2)

Magicoder-S-DS (Wei et al., 2023) 76.8 (70.7) 75.7 (64.4) 76.3 (67.6)

+ Execution Feedback

6.7B Instruct • •

77.4 (72.0) 73.2 (62.4) 75.3 (67.2)

OpenCodeInterpreter-DS 76.2 (72.0) 76.2 (72.0) 75.1 (67.9)

+ Execution Feedback 81.1 (78.7) 82.7 (72.4) 81.9 (75.6)

+ Synth. Human Feedback 87.2 (86.6) 86.2 (74.2) 86.7 (80.4)

+ Synth. Human Feedback (Oracle)

6.7B Instruct • •

89.7 (86.6) 87.2 (75.2) 88.5 (80.9)

∼13B Scale

CodeGen-Mono (Nijkamp et al., 2022) 16B Base • • 32.9 (27.4) 52.6 (43.6) 42.8 (35.5)

StarCoder (Li et al., 2023a) 15B Base • ◦ 34.1 (29.3) 55.1 (46.1) 44.6 (37.7)

CodeT5+ (Wang et al., 2023c) 16B Base • ◦ 31.7 (26.2) 54.6 (44.4) 43.2 (35.3)

CodeLlama-Python (Roziere et al., 2023) 13B Base • ◦ 42.7 (36.6) 61.2 (50.9) 52.0 (43.8)

OpenCodeInterpreter-CL 77.4 (73.8) 70.7 (59.2) 74.1 (66.5)

+ Execution Feedback

13B Instruct • •

81.1 (76.8) 78.2 (67.2) 79.7 (72.0)

∼34B Scale

CodeLlama-Python (Roziere et al., 2023) 34B Base • ◦ 51.8 (43.9) 67.2 (52.9) 59.5 (48.4)

Speechless-CL-v2.0 (speechless, 2023) 34B Instruct • • 77.4 (71.3) 72.4 (59.1) 74.9 (65.2)

XwinCoder-CL (Xwin-LM, 2023) 34B Instruct • • 75.6 (67.7) 76.2 (62.4) 75.9 (65.1)

Phind-CL-v2 (Phind, 2023) 34B Instruct • ◦ 71.3 (67.1) - -

WizardCoder-CL (Luo et al., 2023) 34B Instruct • ◦ 73.2 (64.6) 73.2 (59.9) 73.2 (62.3)

OpenCodeInterpreter-CL 78.0 (72.6) 73.4 (61.4) 75.7 (67.0)

+ Execution Feedback

34B Instruct • •

81.7 (78.7) 80.2 (67.9) 81.0 (73.3)

DeepSeekCoder (Guo et al., 2024) 33B Base • ◦ 51.2 (44.5) - -

DeepSeekCoder-Instruct 81.1 (75.0) 78.7 (66.7) 79.9 (70.9)

+ Execution Feedback

33B Instruct • ◦

81.1 (76.2) 82.7 (73.4) 81.9 (74.8)

WizardCoder-V1.1 (Luo et al., 2023) 79.9 (73.2) 78.9 (66.9) 79.4 (70.1)

+ Execution Feedback

33B Instruct • ◦

74.4 (69.5) 79.9 (68.2) 77.2 (68.9)

OpenCodeInterpreter-DS 79.3 (74.3) 78.7 (66.4) 79.0 (70.4)

+ Execution Feedback 82.9 (80.5) 83.5 (72.2) 83.2 (76.4)

+ Synth. Human Feedback 88.4 (86.0) 87.5 (75.9) 88.0 (81.0)

+ Synth. Human Feedback (Oracle)

33B Instruct • •

92.7 (89.7) 90.5 (79.5) 91.6 (84.6)

∼70B Scale

CodeLlama-Python (Roziere et al., 2023) 70B Base • ◦ 55.5 (50.0) 65.4 (53.4) 60.5 (51.7)

CodeLlama-Instruct 70B Instruct • ◦ 72.0 (65.2) 75.4 (61.7) 73.7 (63.5)

OpenCodeInterpreter-CL 76.2 (70.7) 73.0 (61.9) 74.6 (66.3)

+ Execution Feedback

70B Instruct • •

79.9 (77.4) 81.5 (69.9) 80.7 (73.7)

Table 1: Pass@1 accuracy of different code models on HumanEval (+), MBPP (+) and their average (+). ‘CL’:

based on CodeLlama; ‘DS’: based on DeepseekCoder. Baseline results are copied from the EvalPlus Leaderboard

or replicated by running the ofﬁcial checkpoints. We highlight strong baselines and our methods for each scale.

and synthetic human feedback, generated by GPT-

4 (OpenAI, 2023), to simulate real-world coding

scenarios and interactions. Speciﬁcally, the multi-

turn evaluation encompasses three scenarios, of-

fering a holistic view of OpenCodeInterpreter’s

capabilities in dynamic code reﬁnement:

•

Execution Feedback: Here, OpenCodeInter-

preter independently leverages execution out-

comes and compiler diagnostics to pinpoint and

correct errors, mirroring a developer’s process of

reﬁning code based on direct execution feedback.

•

Synthetic Human Feedback: In this scenario,

GPT-4 generates feedback that mimics human

input by considering the task description, ini-

tial model response, and any execution feedback.

This tests OpenCodeInterpreter’s adaptability to

nuanced, human-like feedback, reﬂecting real-

world developer or user interactions.

•

Synthetic Human Feedback (Oracle): Building

on the previous scenario, GPT-4 also accesses the

ground-truth solution, offering insight into Open-

CodeInterpreter’s optimal performance in code

reﬁnement when guided by precise feedback.

For each task, the code generation and evalu-

ation process concludes either when the model’s

solution successfully passes the evaluation or when

it reaches the set maximum of two rounds. If a

code sample fails the evaluation, both the solu-

tion and the test results are reincorporated into the

prompt for reﬁnement. The evaluation identiﬁes

three principal scenarios for non-passing outcomes:

1) Exception Handling: Captures and relays any

exceptions or errors encountered during execution

as error messages, providing direct feedback for

correction. 2) Not-Expected: In instances where

outputs deviate from expected results, the model

receives feedback including test inputs, expected

outputs, and actual outputs, highlighting the dis-

crepancy. 3) Timeout Handling: Implements a time-

out threshold to prevent evaluation delays from

solutions with excessive or inﬁnite runtimes. Ex-

ceeding this threshold triggers an "Execution timed

out" notiﬁcation.

4 Main Results

This section reports OpenCodeInterpreter and base-

lines in single-turn and multi-turn code generation

settings. The results are in Table 1.

4.1 Results of Single-turn Code Generation

We compare OpenCodeInterpreter’s single-turn

code generation performance against premier mod-

els such as GPT-3.5/4-Turbo (OpenAI, 2022, 2023),

CodeLlama-Python (Roziere et al., 2023), Wizard-

Coder (Luo et al., 2023), Deepseek-Coder (Guo

et al., 2024), CodeT5+ (Wang et al., 2023c) across

different scales. Leveraging data from the EvalPlus

leaderboard as of February 10th, 2024, we exam-

ine OpenCodeInterpreter’s achievements on the

HumanEval and MBPP benchmarks, as well as

their advanced versions, HumanEval+ and MBPP+.

For straightforward comparisons, we consolidate

results across different model scales into one ta-

ble, facilitating direct performance comparisons

between each model scale and the respective vari-

ants of OpenCodeInterpreter.

Our experimental analysis reveals OpenCodeIn-

terpreter’s strong performance, with several con-

ﬁgurations matching or surpassing leading bench-

marks. The OpenCodeInterpreter-DS 33B vari-

ant achieves the highest scores among open-source

models. This accomplishment is remarkable, espe-

cially considering the signiﬁcant presence of low-

quality or incorrect data in the initial training set.

4.2 Results of Multi-turn Code Generation

This section evaluates the proﬁciency of Open-

CodeInterpreter in multi-turn interactions through

iterative reﬁnement, leveraging interpreter diagnos-

tics and human insights.

Our experimental evaluation imposes a two-

round limit on iterations to maintain fairness and

consistency across tasks. While some issues may

beneﬁt from multiple reﬁnements, others require

fewer. This limitation offers clear insights into the

model’s iterative capabilities. In the execution feed-

back scenario, our models across all scales exhib-

ited superiority over state-of-the-art (SOTA) bench-

marks, with the OpenCodeInterpreter 33B model

achieving parity with GPT-4 Turbo’s single-round

score, thus establishing a new SOTA benchmark

among the evaluated code models.

Due to budget constraints, our Human Feedback

and Human Feedback (Oracle) assessments concen-

trate on the OpenCodeInterpreter 6.7B and Open-

CodeInterpreter 33B models. The outcomes re-

veal that with Human Feedback, the OpenCodeIn-

terpreter 6.7B model signiﬁcantly outperformed

GPT-4 Turbo’s single-round score, while in the Hu-

man Feedback (Oracle) scenario, the OpenCodeIn-

Ratio E.F HumanEval (+) MBPP (+) Average (+)

✗ 76.2 (72.0) 73.9 (63.7) 75.1 (67.9)

2:1

✔ 81.1 (78.7) 82.7 (72.4) 81.9 (75.6)

✗ 77.3 (72.6) 74.6 (62.6) 76.0 (67.6)

1:1

✔ 78.0 (72.6) 78.4 (65.9) 78.2 (69.3)

✗ 75.7 (71.9) 72.9 (62.9) 74.3 (67.4)

1:2

✔ 78.7 (75.6) 77.9 (65.9) 78.3 (70.8)

✗ 76.2 (72.0) 75.4 (65.4) 75.8 (68.7)

1:3

✔ 78.0 (75.0) 79.2 (69.9) 78.6 (72.5)

✗ 70.7 (67.0) 73.4 (63.1) 72.1 (65.1)

1:5

✔ 75.6 (70.7) 79.2 (67.9) 77.4 (69.3)

✗ 73.8 (68.9) 73.9 (62.9) 73.9 (65.9)

0:1

✔ 76.2 (71.3) 66.7 (76.6) 71.5 (74.0)

Table 2: Performance of OpenCodeInterpreter with data

mixed ratios of single-turn data and Code-Feedback.

“E.F” indicates the use of execution feedback.

terpreter 33B model’s average score notably ex-

ceeded the 90 benchmark in the HumanEval/MBPP

benchmarks. These results highlight the signiﬁ-

cant role of iterative feedback and reﬁnement in

advancing code generation models, establishing

OpenCodeInterpreter as a leader in software de-

velopment tools. Through this reﬁned approach,

OpenCodeInterpreter not only demonstrates its re-

markable adaptability and code reﬁnement based

on diverse feedback but also sets a new benchmark

for future code generation technologies.

4.3 Ablations of Data Sources

This section systematically explores the impact of

various data sources on the performance of Open-

CodeInterpreter. We conduct a series of ablation

studies to evaluate the inﬂuence of high-quality

single-turn data and diverse multi-turn feedback

mechanisms on the model’s code generation, de-

bugging, and reﬁnement capabilities.

Impact of High-Quality Single-Turn Data. To

evaluate the effect of high-quality single-turn data

on OpenCodeInterpreter’s efﬁcacy, we incorporate

the WizardCoder 110K

dataset, renowned for its

syntactic accuracy and logical coherence, into our

extensive multi-turn dataset. This integration seeks

to identify the optimal mix of precise, single-turn

code generation and the advanced, iterative reﬁne-

ment enabled by multi-turn interactions.

Our experiments employ a soft-target ﬁne-tuning

strategy across six conﬁgurations, varying the pro-

portion of WizardCoder 110K data in our multi-

turn dataset. These conﬁgurations span from full

incorporation to total exclusion of the WizardCoder

dataset, assessing the performance of the model

in two versions: DeepSeekCoder-Base-6.7B and

Datasets E.F Average (+)

✗ 75.0 (66.9)

Single-turn Packing

✔ 77.5 (69.5)

✗ 75.1 (66.9)

Interaction Simulation

✔ 78.5 (69.6)

Single-turn Packing ✗ 74.7 (66.5)

+ Interaction Simulation ✔ 78.2 (70.1)

Single-turn Packing + Interaction ✗ 75.2 (65.4)

Simulation + Code Correction ✔ 79.1 (71.3)

✗ 75.1 (67.9)

Code-Feedback (Full)

✔ 81.9 (75.6)

Table 3: Performance comparison of the model across

different settings with incremental data source integra-

tion. “E.F” indicates the use of execution feedback.

DeepSeekCoder-Base-33B.

Our ﬁndings are illustrated in Table 2. It shows

that incorporating high-quality single-turn data

(e.g., WizardCoder dataset) signiﬁcantly improves

our model’s multi-turn performance. This strategic

incorporation ensures that the model beneﬁts from

the syntactic accuracy and logical coherence inher-

ent in single-turn tasks, thereby enriching its capac-

ity for nuanced, iterative reﬁnement in subsequent

turns. It reveals the critical role of high-quality

single-turn inputs in setting the stage for more ef-

fective multi-turn code generation and reﬁnement.

Beneﬁts of Diverse Multi-Turn Data Sources.

Following the enhanced baseline established by

fully integrating the WizardCoder dataset, this sub-

section investigates the advantages of different data

sources on the model’s reﬁnement and debugging

efﬁcacy. We add diverse data sources to our train-

ing regimen, including Single-turn Packing, Inter-

action Simulation, and Code Correction Data, both

individually and in combination.

The use of these multi-turn data sources, includ-

ing Single-turn Packing, Interaction Simulation,

and Code Correction Data, individually and in com-

bination, demonstrably enhances OpenCodeInter-

preter ’s debugging and reﬁnement functions. No-

tably, the inclusion of Code Correction Data signif-

icantly elevates the model’s efﬁciency in correcting

errors. This underscores the profound impact of a

varied and targeted training approach on advancing

the capabilities of sophisticated code generation

models. Such an approach enables these models

to more effectively address complex coding chal-

lenges, correct errors, and reﬁne outputs via exten-

sive feedback mechanisms.

4.4 Case Study: Coding Queries in the Wild

This section delves into three distinct case studies

to demonstrate OpenCodeInterpreter’s operational

dynamics when faced with “wild” user queries. The

motivation behind these case studies is to showcase

the practical applications of OpenCodeInterpreter.

In a notable success story (Figure A8), we tasked

OpenCodeInterpreter with developing a function

to calculate all prime numbers within the 1-100

range, later extending the solution to any arbitrary

range x-y. Another commendable instance (Fig-

ure A9) involved OpenCodeInterpreter implement-

ing a Python function to validate IPv6 addresses us-

ing regular expressions. Demonstrating its capabil-

ity to iteratively reﬁne its approach, OpenCodeIn-

terpreter not only identiﬁed and corrected errors

but also enhanced the solution based on human

feedback. These two cases exemplify OpenCodeIn-

terpreter’s strength in understanding mathematical

logic and dynamically adjusting algorithms to meet

speciﬁed criteria.

A challenging case (Figure A10) arose when

OpenCodeInterpreter was asked to design a func-

tion identifying the intersection of two input lists,

returning tuples of distinct elements present in both

lists alongside their occurrence frequencies. De-

spite OpenCodeInterpreter’s attempts at correction,

it addressed errors incrementally, ultimately ex-

ceeding the maximum number of attempts (three).

This case sheds light on OpenCodeInterpreter’s

limitations in simultaneously tackling multiple

challenging errors.

Through these case studies, we gain invaluable

insights into OpenCodeInterpreter’s capabilities

and limitations. These insights are crucial for guid-

ing future enhancements to OpenCodeInterpreter.

5 Related Work

LLMs for Code. It becomes a common prac-

tice to include code data for pre-training LLMs.

For example, 5% of PaLM’s (Chowdhery et al.,

2023) pre-training data is code, and this ratio for

LaMDA (Thoppilan et al., 2022), Galactica (Taylor

et al., 2022), LLaMA (Touvron et al., 2023), Go-

pher (Rae et al., 2021), GPT-NeoX (Black et al.,

2022) is 13%, 7%, 5%, 3%, and 8%, respectively.

Additionally, specialized LLMs have been pre-

trained for generating code, e.g., CodeGen (Ni-

jkamp et al., 2022), PanGu-Coder (Christopoulou

et al., 2022), CodeGeeX (Zheng et al., 2023),

CodeFuse (Di et al., 2023), CodeT5+ (Wang

et al., 2023d), AlphaCode (Li et al., 2022), In-

Coder (Fried et al., 2022), StarCoder (Li et al.,

2023a), DeepSeek-Coder (Guo et al., 2024). On

the other hand, code LLMs can be ﬁne-tuned from

general-purpose LLMs, e.g., CodeLlama (Roziere

et al., 2023), WizardCoder (Luo et al., 2023), which

is the approach we take here. Compared to special-

ized LLMs, the ﬁne-tuning paradigm enables us

to explore ways to improve code generation capa-

bilities by leveraging pre-trained general-purpose

LLMs, especially because these LLMs have already

been trained on an extensive amount of code data.

Iterative Code Generation and Reﬁnement. For

many sequence generation tasks, iterative ap-

proaches are often taken to improve the generation

quality, e.g., script generation (Tandon et al., 2021),

summarization (Scheurer et al., 2022), and other

tasks as shown in (Madaan et al., 2022; Saunders

et al., 2022). Notably, in Self-Reﬁne (Madaan et al.,

2023), an LLM generates feedback after generat-

ing initial outputs, and the LLM iteratively updates

the outputs with the feedback. Whereas it focuses

on a general-purpose LLM setting, we focus on

code generation tasks. As for code generation with

LLMs, DebugBench (Tian et al., 2024) observes

that incorporating runtime feedback improves code

LLMs’ debugging performance. A most recent

and relevant work is StepCoder (Dou et al., 2024),

where, following the paradigm of relying on re-

inforcement learning with compiler feedback (Le

et al., 2022; Shojaee et al., 2023), the authors fur-

ther divide the original exploration problems into

a sequence of easier sub-tasks. However, our ap-

proach does not rely on reinforcement learning and

has access to the intermediate generation, which

makes the training easier and more stable.

6 Conclusion

In conclusion, OpenCodeInterpreter represents a

signiﬁcant leap forward in the ﬁeld of code genera-

tion, bridging the previously identiﬁed gap between

open-source models and the advanced capabilities

of proprietary systems like the GPT-4 Code Inter-

preter. By integrating compiler diagnostics and hu-

man feedback into an iterative reﬁnement process,

OpenCodeInterpreter not only surpasses traditional

one-off generation approaches but also introduces

a level of adaptability and precision previously un-

seen in open-source models. The introduction of

Code-Feedback, with its extensive multi-turn in-

teractions, further empowers OpenCodeInterpreter

to dynamically reﬁne code in response to evolving

user intents and complex coding tasks.

Ethics Statement

The development and deployment of OpenCodeIn-

terpreter, alongside the use of Code-Feedback, take

ethical considerations to ensure responsible usage.

We have made efforts to ensure that the dataset rep-

resents a diverse range of coding styles, problem

domains, and user scenarios to prevent the propaga-

tion of biased or unfair outcomes. Given that Open-

CodeInterpreter can generate and reﬁne code based

on user inputs, we strictly check out the dataset to

ensure that it does not expose sensitive information

or create security vulnerabilities. OpenCodeInter-

preter has the potential to democratize coding by

lowering the barrier to entry for non-experts and

developers. We open-source all our code, models,

and datasets to maximize accessibility.

Limitations

While OpenCodeInterpreter introduces signiﬁcant

advancements in automated code generation, it is

important to acknowledge the limitations inherent

in the system and the Code-Feedback that supports

it. Although OpenCodeInterpreter is designed to

support multi-language code generation and under-

stand a wide range of programming contexts, its

performance may vary across different languages

and speciﬁc domains. While OpenCodeInterpreter

excels at interpreting and responding to a variety

of coding tasks, it may struggle with extremely

complex or ambiguous user intents. The ability

to accurately capture and address such intents is

limited by the model’s current understanding and

the speciﬁcity of the data in Code-Feedback.

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A Source Data Filtering

Here, we outline the prompts used for source data ﬁltering.

Query Filtering Prompt 1

Rate the following code queries on a scale of 1 to 5 based on their complexity, where 1 is the easiest and 5 is the most

difﬁcult. Consider the complexity of the query

Query: [{query}]

You are obliged to choose only from the following list.

Scoring Criteria:

1 Point - Very Basic: The query involves simple operations or common issues

2 Points - Basic: The query involves fundamental programming concepts or commonly used functions

3 Points - Intermediate: The query requires some programming experience, possibly involving multiple steps

4 Points - Difﬁcult: The query involves advanced programming skills, including complex logic, algorithms, or data

structures

5 Points - Very Difﬁcult: The query requires extensive expertise, potentially involving innovative problem-solving

approaches or unique algorithm design

Please give the score ﬁrst then explain why

Query Filtering Prompt 2

Rate the following code queries on a scale of 1 to 5 based on their complexity, where 1 is the easiest and 5 is the most

difﬁcult. Consider the complexity of the query

Query: [{query}]

You are obliged to choose only from the following list.

Scoring Criteria:

1 Point - Moderately Difﬁcult: Involves understanding speciﬁc programming concepts or libraries, and may include

medium complexity algorithms or data structures like basic sorting algorithms or tree structures.

2 Points - Challenging: Requires handling more complex logic or algorithms such as advanced sorting algorithms,

recursive logic, or intermediate data structures like hash tables and heaps.

3 Points - Highly Challenging: Demands deeper knowledge in algorithms and data structures, potentially including

graph algorithms, dynamic programming, or complex string manipulation techniques.

4 Points - Advanced: Focuses on proﬁciency in programming and algorithm design, dealing with complex system

architecture issues, performance optimization, or solving advanced algorithmic challenges like NP-hard problems.

5 Points - Expert Level: The highest difﬁculty level, requiring innovative problem-solving approaches or unique

algorithm design, possibly involving interdisciplinary knowledge or the application of cutting-edge technologies.

Please give the score ﬁrst then explain why

Below is an overview of the data ﬁltering process applied to the initial seed dataset, with Figure A1

summarizing the data quantity after each ﬁltering stage.

Figure A1: Overview of Data Filtering Process and Corresponding Data Quantities

The pie chart in Figure A2 illustrates the distribution of programming languages in our dataset after

ﬁltering.

Figure A2: Distribution of Programming Languages in Filtered Dataset

B Simulating Interactions for Data Collection

We illustrate the prompts used in multi-turn execution feedback and multi-turn human feedback respec-

tively.

System prompt for multi-turn execution feedback

You are an AI code interpreter.

Your goal is to help users do a variety of jobs by executing Python code.

You should:

1. Comprehend the user’s requirements carefully & to the letter.

2. Give a brief description for what you plan to do & call the provided function to run code.

3. Provide results analysis based on the execution output.

4. If error occurred, try to ﬁx it.

5. Response in the same language as the user.

System prompt for multi-turn human feedback

You are a user who gives feedback to the latest generated code. If no available code is found in the conversation, you

should give a feedback to encourage assistant to generate code. NOTE: your feedback should be WITHIN 2 SHORT

SENTENCES.

You can refer to the following types of feedback:

1. **Syntax and Formatting**: Checking for syntax errors, inconsistent formatting, and suggesting adherence to standard

coding styles for readability and maintainability.

2. **Efﬁciency**: Identifying parts of the code that can be optimized for better performance, such as reducing time

complexity, optimizing loops, or suggesting more efﬁcient data structures.

3. **Functionality Enhancements**: Suggesting additional features or enhancements that could make the code more

functional or user-friendly.

4. **Code Clarity and Documentation**: Recommending improvements in code comments and documentation to make

the code more understandable and easier to maintain.

5. **Bug Identiﬁcation**: Pointing out any potential bugs or logical errors in the code and suggesting ways to ﬁx them.

6. **Security Improvements**: Highlighting any security vulnerabilities in the code and suggesting best practices to

enhance security.

7. **Compatibility and Testing**: Advising on making the code more compatible with different environments or

platforms and suggesting more comprehensive testing scenarios.

8. **Resource Optimization**: Identifying areas where the code might be using more resources than necessary (like

memory or CPU) and suggesting optimizations.

9. **Scalability**: Providing insights on how the code can be made more scalable to handle larger data sets or more

users.

10. **Adherence to Best Practices**: Ensuring the code follows the best practices speciﬁc to the language or framework

being used.

Your output MUST be in a json format like this:

{

"satisﬁed": "The points that have been achieved in generated code",

"not_satisﬁed": "The points that have not yet been achieved in generated code",

"feedback": "The actual feedback. Your feedback should be WITHIN 2 SHORT SENTENCES. Feedback must come

from a point included in ’not_satisﬁed’ ﬁeld. You can ask the assistant here to generate code if no available code is

found in previous conversations."

}

System prompt for deliberately generating incorrect code

You are an AI code interpreter.

Your goal is to generate and execute Python code.

Your code MUST contain at least one of the following types of errors:

1. Syntax Error: This type of error occurs when the code violates the grammar rules of the programming

language. For example, forgetting to close a parenthesis or a quotation mark, or misspelling a keyword.

2. Logical Error: These errors sneak into your code when there’s a misunderstanding of the problem you’re solving,

leading to incorrect results despite the code running without crashing. For example, calculating the average of a list of

numbers by summing them up but forgetting to divide by the count of the numbers.

3. Type Error: This error occurs when an operation is applied to an object of an inappropriate type. For example,

attempting to concatenate a string with an integer without converting the integer to a string ﬁrst.

4. Name Error: This happens when the code attempts to reference a variable or a function name that hasn’t been deﬁned.

For example, trying to print a variable that hasn’t been declared.

5. Timeout Error: This error occurs when your code gets stuck in a loop that never ends, either due to a logic ﬂaw or a

condition that never becomes false. In programming, such an error can cause your application to hang indeﬁnitely,

consuming resources and potentially leading to a crash if not handled properly. For example, writing a loop that waits

for a certain condition to change, but the condition is never updated within the loop.

NOTE:

1. You MUST make mistakes in the generated code!

2. Do not explain the errors within. Just write your thoughts and code as normal.

3. Do not tell me you are writing the wrong code in any form (e.g., in text/code/comments). Just pretend you are writing

the correct code and still not recognizing the errors.

C Natural Language Explanations Generation

We use the following prompt to generate explanations for code using GPT-4.

Prompt for generating natural language explanations using GPT-4

Here is a list containing a series of dialogues between a user and a programmer assistant.

Following the previous dialogues, the user posed a latest problem.

The assistant has now crafted the correct code based on the previous dialogues and the latest problem.

Assuming you are this programmer assistant, please add some text before the code.

The purpose of this text is to respond to the latest problem and to introduce the code that follows.

This text may include: language used in the code, algorithm used in the code, step-by-step implementation overview,

and other relevant content.

You may use phrases like "The following code", "My code", "My solution", to refer to the @@Code.

Your response should ONLY contain the text that you add before the code.

Your only task is to write the text, never modify the code or remind me something. Never restate the previous dialogues

and the problem.

@@Previous Dialogues

{previous dialogues}

@@Recent Problem:

{recent problem}

Add the text there.

@@Code:

{code}

D Model Evaluation Prompts

For different benchmarks, distinct prompts were employed during the initial turn of solution generation:

identical prompts were utilized for HUMANEVAL and HUMANEVAL+, while MBPP and MBPP+

shared a similar prompt. The prompts are illustrated in the below.

Prompt for HumanEval and HumanEval+

You are an exceptionally intelligent coding assistant that consistently delivers accurate and reliable responses to user

instructions.

@@ Instruction

Here is the given code to do completion:

“‘{language}

{original prompt}

“‘

Please continue to complete the function with {language} programming language. You are not allowed to modify the

given code and do the completion only.

Please return all completed codes in one code block.

This code block should be in the following format:

“‘{language}

# Your codes here

“‘

@@ Response

Prompt for MBPP and MBPP+

You are an exceptionally intelligent coding assistant that consistently delivers accurate and reliable responses to user

instructions.

@@ Instruction

Here is the given problem and test examples:

{original prompt}

Please use the {language} programming language to solve this problem.

Please make sure that your code includes the functions from the test samples and that the input and output formats of

these functions match the test samples.

Please return all completed codes in one code block.

This code block should be in the following format:

“‘{language}

# Your codes here

“‘

@@ Response

We employ GPT models to emulate human behavior in generating feedback. The prompts provided to

the GPT models are presented as follows.

Prompt for GPT models mimicking human feedback with canonical solution

You are tasked with providing guidance to a programmer who has drafted a code for a programming problem.

Your role is to mimic human-like responses and offer suggestions for modifying the code based on the canonical solution

and the observed execution results.

You should NOT directly revealing contents of the @@Canonical Solution or mentioning terms such as "canonical

solution."

You should refrain from directly writing code.

Begin by thoroughly examining the existing code and its functionality.

Compare the @@Existing Code with the @@Canonical Solution provided. Note any discrepancies in logic, approach,

or implementation.

Analyze the @@Execution Result obtained from running the @@Existing Code. Identify any errors, unexpected

behavior, or deviations from the expected output.

Consider potential edge cases, optimization opportunities, or alternative approaches based on insights from both the

@@Canonical Solution and @@Execution Result.

Offer guidance in a clear and understandable manner, explaining the rationale behind each suggestion.

Refrain from providing actual code solutions, but instead focus on conceptual modiﬁcations or strategies.

Provide constructive feedback to help the programmer improve their coding skills.

Remember, your role is to simulate human-like guidance and expertise in programming without directly implementing

solutions.

Please respond in no more than three sentences.

@@Problem

{original prompt}

@@Existing Code

{sanitized code}

@@Execution Result

{execution result}

@@Canonical Solution

{canonical solution}

@@Guidance

Prompt for GPT models mimicking human feedback without canonical solution

You are tasked with providing guidance to a programmer who has drafted a code for a programming problem.

Your role is to mimic human-like responses and offer suggestions for modifying the code based on the observed

execution results.

You should refrain from directly writing code.

Begin by thoroughly examining the existing code and its functionality.

Analyze the @@Execution Result obtained from running the @@Existing Code. Identify any errors, unexpected

behavior, or deviations from the expected output.

Consider potential edge cases, optimization opportunities, or alternative approaches based on insights from the

@@Execution Result.

Offer guidance in a clear and understandable manner, explaining the rationale behind each suggestion.

Refrain from providing actual code solutions, but instead focus on conceptual modiﬁcations or strategies.

Provide constructive feedback to help the programmer improve their coding skills.

Remember, your role is to simulate human-like guidance and expertise in programming without directly implementing

solutions.

Please respond in no more than three sentences.

@@Problem

{original prompt}

@@Existing Code

{sanitized code}

@@Execution Result

{execution result}

@@Guidance

E Examples of Methods used in Data Collection

Here we listed examples of each method in data collection process, including similar query packing,

human feedback simulation and code correction for coding queries from open-source data (Section 2.1),

and similar problem packing and follow-up Q&A for coding challenges from LeetCode (Section 2.2).

User:

You are tasked with creating a Python script that will search for specific files within a given directory and its subdirectories. Your script should be

able to identify files with a particular file extension and return a list of these files. Additionally, the script should be able to handle cases where the

specified directory does not exist or is empty.

Write a function `find_files(directory, extension)` that takes in two parameters:

- `directory`: A string representing the directory path to search for files.

- `extension`: A string representing the file extension to search for (e.g., ".txt", ".csv", ".py").

The function should return a list of file paths that have the specified file extension within the given directory and its subdirectories. If the directory

does not exist or is empty, the function should return an empty list.

Example:

```python

print(find_files('/path/to/directory', '.txt'))

# Output: ['/path/to/directory/file1.txt', '/path/to/directory/subdirectory/file2.txt']

```

Assistant:

```python

import os

def find_files(directory, extension):

file_list = []

if os.path.exists(directory):

for root, dirs, files in os.walk(directory):

for file in files:

if file.endswith(extension):

file_list.append(os.path.join(root, file))

return file_list

```

User:

You are tasked with creating a Python function that processes a given directory and calculates the total size of all files within it. Additionally, the

function should exclude any files with a ".torrent" extension from the total size calculation. You are provided with a code snippet that demonstrates the

use of the `os` module to join a path and a filename, as well as the use of a custom `Torrent` class to read the total size of files in a torrent file.

Create a function `calculate_directory_size` that takes a directory path as input and returns the total size of all files within the directory, excluding any

files with a ".torrent" extension.

Example:

Given the directory structure:

```

my_directory/

file1.txt

file2.jpg

file3.torrent

```

The function call `calculate_directory_size('my_directory')` should return the total size of `file1.txt` and `file2.jpg` in bytes.

Assistant:

```python

import os

def calculate_directory_size(directory_path):

total_size = 0

for dirpath, _, filenames in os.walk(directory_path):

for filename in filenames:

if not filename.endswith('.torrent'):

file_path = os.path.join(dirpath, filename)

total_size += os.path.getsize(file_path)

return total_size

```

The `calculate_directory_size` function uses the `os.walk` method to traverse the directory and its subdirectories, and iterates through the files within

each directory. For each file, it checks if the file does not have a ".torrent" extension and adds its size to the `total_size`. Finally, the function returns

the accumulated `total_size` of all non-torrent files within the directory.

Figure A3: An example of similar query packing for challenging query pools.

User:

Develop a program in Python to implement a Simple Linear Regression

model.

## Problem Statement ##

Write a Python program to implement a Simple Linear Regression

model to predict the salary of employees based on their years of

experience.

## Instructions ##

1. The program should ask the user for the number of data points to be

entered.

2. The program should then ask the user to enter the years of experience

and the corresponding salary for each data point.

3. The program should calculate the slope and intercept of the

regression line using the least squares method.

4. The program should use the calculated slope and intercept to predict

the salary for a given number of years of experience entered by the user.

5. The program should display the regression line equation, the

predicted salary, and a scatter plot of the data points and the regression

line.

### Constraints ###

- The number of data points should be a positive integer.

- The years of experience and salary should be non-negative integers.

- You may assume that the input will be valid and no error checking is

required.

Assistant:

I have implemented the Python program to implement a Simple Linear

Regression model based on the instructions provided. The program

calculates the regression line slope and intercept using the least squares

method and then predicts the salary for the given number of years of

experience.

```python

# Importing required libraries

import numpy as np

# Simulating user input

num_points = 5

data_points = [(2, 50000), (5, 80000), (10, 120000), (3, 60000), (8,

100000)]

years_to_predict = 7

# Separating years of experience and salary

X = np.array([x[0] for x in data_points])

Y = np.array([x[1] for x in data_points])

# Calculating the mean of X and y

mean_x = np.mean(X)

mean_y = np.mean(Y)

# Calculating the terms needed for the num and den of formula of slope

(b1) and intercept (b0)

m = len(X)

numer = 0

denom = 0

for i in range(m):

numer += (X[i] - mean_x) * (Y[i] - mean_y)

denom += (X[i] - mean_x) ** 2

b1 = numer / denom # slope

b0 = mean_y - (b1 * mean_x) # intercept

# Displaying the regression line equation

print('Regression line equation: y = {:.2f} + {:.2f}x'.format(b0, b1))

# Predicting the salary for the given number of years of experience

predicted_salary = b0 + b1 * years_to_predict

print('Predicted salary for {} years of experience:

{:.2f}'.format(years_to_predict, predicted_salary))

```

User:

Execution result:

Regression line equation: y = 34424.78 + 8495.58x

Predicted salary for 7 years of experience: 93893.81

(Continued)

Assistant:

The Python code provided uses the Simple Linear Regression model

to predict the salary based on years of experience. The model was

trained using 5 data points, each consisting of years of experience

and corresponding salary.

The calculated regression line equation from the model is: y =

34424.78 + 8495.58x

From this equation, the predicted salary for 7 years of experience is:

93893.81

A scatter plot was generated, which visually represents the data

points and the predicted regression line. The red points represent the

input data points, and the blue line represents the regression line or

the salary prediction line.

This plot visually expresses how the model predicts salary based on

years of experience. The closer these red points are to the blue line,

the better the model is at predicting.

Please let me know if anything else is needed.

User:

Consider optimizing the loop for calculating the slope and intercept

to enhance code efficiency. Please ensure consistent formatting for

better readability.

Assistant:

```python

# Importing required libraries

import numpy as np

# Simulating user input

num_points = 5

data_points = [(2, 50000), (5, 80000), (10, 120000), (3, 60000), (8,

100000)]

years_to_predict = 7

# Separating years of experience and salary

X = np.array([x[0] for x in data_points])

Y = np.array([x[1] for x in data_points])

# Calculating the slope and intercept of the regression line

b1 = (np.sum((X - np.mean(X)) * (Y - np.mean(Y)))) / np.sum((X -

np.mean(X)) ** 2)

b0 = np.mean(Y) - (b1 * np.mean(X))

# Displaying the regression line equation

print('Regression line equation: y = {:.2f} + {:.2f}x'.format(b0, b1))

# Predicting the salary for the given number of years of experience

predicted_salary = b0 + b1 * years_to_predict

print('Predicted salary for {} years of experience:

{:.2f}'.format(years_to_predict, predicted_salary))

```

User:

Execution result:

Regression line equation: y = 34424.78 + 8495.58x

Predicted salary for 7 years of experience: 93893.81

Assistant:

The Python program has been improved for better code efficiency

and readability.

The loop for computing the slope (b1) and intercept (b0) of the

regression line is eliminated and replaced with an optimized

computation using numpy. This change enhances performance,

especially when dealing with larger data sets.

The formatted regression line equation is: y = 34424.78 + 8495.58x

The predicted salary for 7 years of experience is: 93893.81

Please let me know if any further assistance is needed.

Figure A4: An example of human feedback simulation for challenging query pools.

User:

Here's a piece of Python code I got from an OCR scan. Can you help me

clean it up?

def s7udert_qrad€s(gpva1s, prev_9pva1s):

if_len{gqu&ls) 1= l3n(pr3v_qpvals):

re7vrr "€rror:_Input ve<tor$ are rOt t#e 5&m€ leng7h"

lettens = {'At':o, 'A':0. '&-':0, '13+':0. 'B':0, 'B-':0, 'CT':O, 'C':o, '<=':0,

'D':0, 'T':o]

gr@des = []

progress = ["impnOved":0, "maint&ined":0, "d€ciined":0}

t0+al_= 0

d€f g3+_l€tten(gpa}:

if gpa > 4.0 or gqa < 0.0:

neturm "3rrOr: Inua1id GPA"

e1it qp& >= 3,85:

neturn 'At'

ellf qpa >= 3.65:

return 'A'

€lif g9a >= 3.S0:

neturn 'A='

e|1f_gpa >= E.15:

neturn 'B+'

elif gpa_>= z.bs:

neturn "B"

elit gpa >= 2.50:

return_'8-'

elif qq4 >= 2.15:

n€tvrn 'C+'

3lif gpa >=_1.8s:

n€turn '<'

ellf 9pa >= 1.s0:

re+urm '<-'

el!f gqa >=_1.0:

re+unn 'D'

else:

returm 'T'

03f get progress(pr€v, curr):

if prev < (urr:

ne+urn "improved"

el!t qnev ==_curr:

re7urn "m&intaimed"

els€:

return "o€c1ineo"

for i in r@nge[len(gpvals)):

|etter_gra03 - g€+_l€tten(gpvals[i])

grad3s.appemd(le+ter grade]

1ett3rs[let7er_grade] += 1

to7al += qpu&l$[i]

9nogr3zs[g37_9nOqr35s(preu 9pvals[i], gpval5[i]}} += 1

avera9e_gnad€ - get_|et+er(tota|/ler(gpu&l$))

lowest_grade = min(gpvais, k€y=qet_l37t3r}

highezt_gn&d€ = nak(gpvals,_key=get_let+er)

r€turn {'gnade$':_gnades, '@v3na9e grade': &u3rage grade, 'lowest

gnade':_lovvest_9na03, 'highest 9r@de': nighest 9rad€`

'l3tters_(0unt':_let7ers, 'progress': pnogress}

# 3xampl3 vsaqe:

gpuals_= (4.o, 3, 1.7,_2, 3.5]

pnev_gpual5 - [E.5` 3` 1.4, 1.8, 3.2]

s7voent_9radez{gpvals,_prev_gpv&|s]

Assistant:

```python

def student_grades(gpvals, prev_gpvals):

[truncated code]

average_grade = get_letter(total/len(gpvals))

lowest_grade = min(gpvals, key=get_letter)

highest_grade = max(gpvals, key=get_letter)

return {'grades': grades, 'average grade': average_grade,

'lowest grade': lowest_grade, 'highest grade': highest_grade,

'letter count': letters, 'progress': progress}

# Example usage:

gpvals = [4.0, 3.0, 1.7, 2.0, 3.5]

prev_gpvals = [3.5, 3.0, 1.4, 1.8, 3.2]

student_grades(gpvals, prev_gpvals)

```

(Continued)

User:

Execution result:

{'grades': ['A+', 'B', 'C-', 'C', 'A-'],

'average grade': 'B-',

'lowest grade': 4.0,

'highest grade': 1.7,

'letter count': {'A+': 1,

'A': 0,

'A-': 1,

'B+': 0,

'B': 1,

'B-': 0,

'C+': 0,

'C': 1,

'C-': 1,

'D': 0,

'F': 0},

'progress': {'improved': 4, 'maintained': 1, 'declined': 0}}

Assistant:

It looks like there was a mistake in the logic for determining the

lowest_grade and highest_grade. The current approach mistakenly tries

to use the get_letter function with the min and max functions, which

does not work as intended because get_letter is designed to return a

letter grade based on a GPA value, not to compare GPA values directly.

Here's how to correctly calculate the lowest_grade and highest_grade:

1. Calculate the lowest_grade and highest_grade based on GPA values

directly, not through the get_letter function. This will give us the actual

lowest and highest GPA values in the list.

2. Correct the calculation to ensure it reflects the actual GPA values,

not their letter grades.

Let's correct the code:

```python

# Correct calculation for lowest and highest GPA values

def student_grades(gpvals, prev_gpvals):

[truncated code]

average_grade = get_letter(total_gpa / len(gpvals))

lowest_gpa = min(gpvals) # This will correctly identify the lowest

GPA value.

highest_gpa = max(gpvals) # This will correctly identify the highest

GPA value.

# Then, if needed, you can get their corresponding letter grades

lowest_grade_letter = get_letter(lowest_gpa)

highest_grade_letter = get_letter(highest_gpa)

return {

'grades': grades,

'average grade': average_grade,

'lowest grade': lowest_grade_letter, # Use letter grade for lowest

GPA

'highest grade': highest_grade_letter, # Use letter grade for

highest GPA

'letters_count': letters,

'progress': progress

}

# Example usage:

gpvals = [4.0, 3, 1.7, 2, 3.5]

prev_gpvals = [3.5, 3, 1.4, 1.8, 3.2]

student_grades(gpvals, prev_gpvals)

User:

Execution result:

{'grades': ['A+', 'B', 'C-', 'C', 'A-'],

'average grade': 'B-',

'lowest grade': 'C-',

'highest grade': 'A+',

'letters_count': {'A+': 1,

'A': 0,

'A-': 1,

'B+': 0,

'B': 1,

'B-': 0,

'C+': 0,

'C': 1,

'C-': 1,

'D': 0,

'F': 0},

'progress': {'improved': 4, 'maintained': 1, 'declined': 0}}

Assistant:

With the corrected logic in place, the execution result now accurately

reflects the grades distribution, progress, and identifies the lowest and

highest grades based on the GPA values.

Figure A5: An example of code correction for challenging query pools.

User:

A city's **skyline** is the outer contour of the silhouette formed by

all the buildings in that city when viewed from a distance. Given the

locations and heights of all the buildings, return _the **skyline**

formed by these buildings collectively_.

The geometric information of each building is given in the array

`buildings` where `buildings[i] = [lefti, righti, heighti]`:

* `lefti` is the x coordinate of the left edge of the `ith` building.

* `righti` is the x coordinate of the right edge of the `ith` building.

* `heighti` is the height of the `ith` building.

You may assume all buildings are perfect rectangles grounded on an

absolutely flat surface at height `0`.

The **skyline** should be represented as a list of "key points "

**sorted by their x-coordinate** in the form `[[x1,y1],[x2,y2],...]`.

Each key point is the left endpoint of some horizontal segment in the

skyline except the last point in the list, which always has a y-

coordinate `0` and is used to mark the skyline's termination where the

rightmost building ends. Any ground between the leftmost and

rightmost buildings should be part of the skyline's contour.

**Note:** There must be no consecutive horizontal lines of equal

height in the output skyline. For instance, `[...,[2 3],[4 5],[7 5],[11

5],[12 7],...]` is not acceptable; the three lines of height 5 should be

merged into one in the final output as such: `[...,[2 3],[4 5],[12 7],...]`

[Examples are truncated]

**Constraints:**

* `1 <= buildings.length <= 104`

* `0 <= lefti < righti <= 231 - 1`

* `1 <= heighti <= 231 - 1`

* `buildings` is sorted by `lefti` in non-decreasing order.

Assistant:

In the following code, we tackle the problem of computing a city

skyline from a given set of buildings. Each building is defined by its

x-coordinate boundaries and height, and our goal is to construct the

contour of these buildings when collectively observed from a

distance.

The algorithm used can be summarized in the following steps:

[Steps are truncated]

My solution is designed to handle the input efficiently and conforms

to the problem's constraints regarding sorting and eliminating

redundant horizontal lines. Let's proceed with the code that solves the

skyline problem.

```python

from sortedcontainers import SortedList

def getSkyline(buildings):

points = []

for l, r, h in buildings:

points.append((l, -h))

points.append((r, h))

points.sort()

heights = SortedList([0])

prev = 0

skyline = []

for x, h in points:

if h < 0:

heights.add(-h)

else:

heights.remove(h)

cur = heights[-1]

if cur != prev:

skyline.append([x, cur])

prev = cur

return skyline

```

(Continued)

User:

There are several squares being dropped onto the X-axis of

a 2D plane.

You are given a 2D integer array `positions` where

`positions[i] = [lefti, sideLengthi]` represents the `ith`

square with a side length of `sideLengthi` that is dropped

with its left edge aligned with X-coordinate `lefti`.

Each square is dropped one at a time from a height above

any landed squares. It then falls downward (negative Y

direction) until it either lands **on the top side of another

square** or **on the X-axis**. A square brushing the

left/right side of another square does not count as landing

on it. Once it lands, it freezes in place and cannot be

moved.

After each square is dropped, you must record the **height

of the current tallest stack of squares**.

Return _an integer array_ `ans` _where_ `ans[i]`

_represents the height described above after dropping the_

`ith` _square_.

[Examples are truncated]

**Constraints:**

* `1 <= positions.length <= 1000`

* `1 <= lefti <= 108`

* `1 <= sideLengthi <= 106`

Assistant:

In the provided code snippet, I have implemented a

solution to calculate the height of the tallest stack after

dropping each square on a 2D plane. The algorithm

processes a list of squares, represented by their left edge

and side length, and follows these main steps:

[steps are truncated]

```python

def fallingSquares(positions):

ans = []

intervals = []

for p in positions:

L, size = p

R = L + size

h = size

for h2, R2 in intervals:

if R2 > L and R > R2:

h = max(h, size + h2)

maxHeight = max((h2 for h2, R2 in intervals),

default=0)

ans.append(max(maxHeight, h))

intervals.append((h, R))

return ans

```

Figure A6: An example of similar problem packing for LeetCode problems.

User:

Please solve this problem:

In a galaxy far away, there is a constant battle between the republic

and the droid army. The droid army decided to launch their final

attack on the republic. They have N space-fighters.

Initially the $i^{th}$ fighter is located at (x_{i}, y_{i}). All of the

space-fighters move with constant velocity V units/sec in the positive

X direction.

i.e., fighter at (x_{i}, y_{i}) moves to (x_{i}+V, y_{i}) in 1 second.

The $ i^{th} $ space-fighter broadcasts enemy information at a

frequency f_{i}.

The republic is not scared of the artificially intelligent droid force as

they have Yoda. Yoda has a special power, at any time T he can

choose a region of the droid army and block one specific frequency F.

This power has one constraint; it can be applied only in the form of a

two sided unbounded axis parallel rectangular box open towards the

both the directions across X axis (refer image below for clarity). If a

frequency (F) is blocked all the space-fighters in the region having the

frequency F can’t communicate.

Given the initial positions of the space-fighters, and their velocity,

you are to answer queries of the following form:

YU YD T

where YU, YD are the bounds on y-axis inside which YODA can

block a frequency at time T.

In the region described by the query, after a time T seconds from the

start, if Yoda can chose one frequency (F) he wishes to, what is the

maximum number of communications he can block?

Input Format

Each test case is described as follows; the first line contains 3 space

separated integers N - the number of space-fighters, Q - the number of

queries you have to answer, and V - the velocity of the space-fighters

separated by a single space.

N lines follow, each containing 3 space separated integers x_{i},

y_{i}, and f_{i}, denoting the x co-ordinate, y co-ordinate and the

frequency at which the $ i^{th} $ ship broadcasts respectively. Each

of the next Q lines contain 3 space separated integers representing

YU, YD, T respectively. Refer the figure for more clarity

Note: Points on the boundaries should be counted as well.

Output Format

For each query you are to output a single integer denoting the result.

Constraints

1 <= N <= 50000

1 <= Q <= 30000

1 <= V <= 10000

$-10^{9} $<= x_{i} <= $10^{9} $

$-10^{9} $ <= y_{i} <= $10^{9} $

1 <= f_{i} <= $10^{9} $

$-10^{9} $ <= YU <= $ 10^{9} $

$-10^{9} $<= YD <= $10^{9} $

1 <= T <= 10000

YU >= YD

[sample input, output and explanation are truncated]

(Continued)

Assistant:

To address your request, I've developed a Python code that

evaluates the situations presented in this interstellar

conflict scenario. The program is designed to take input

details for the droid army's space-fighters regarding their

initial positions, frequencies, and velocities, and then

process multiple queries to determine the maximum

number of communications Yoda can block at a given time

within specified regions of space.

Here's a step-by-step implementation overview:

[steps are truncated]

The following code encapsulates the logic detailed above,

and executes the required queries to produce the desired

output.

```python

import bisect

from collections import defaultdict

(N, Q, _) = map(int, input().split())

a = defaultdict(list)

y = list()

for _ in range(N):

(_, y_, freq) = map(int, input().split())

a[freq].append(y_)

y.append(y_)

a = {freq: sorted(y) for (freq, y) in a.items() if len(y) > 1}

y = sorted(y)

res = []

for _ in range(Q):

(y_max, y_min, T) = map(int, input().split())

lres = 0

index_start = bisect.bisect_left(y, y_min)

if y[index_start] <= y_max:

lres = 1

for freq in a:

index_start = bisect.bisect_left(a[freq],

y_min)

index_stop = bisect.bisect_right(a[freq],

y_max)

lres = max(lres, index_stop - index_start)

res.append(lres)

print(*res, sep='\n’)

```

User:

What is the difficulty of this question?

Assistant:

The difficulty of the problem described can be considered

HARD.

User:

Do you have any other way to solve this problem?

Assistant:

To provide an alternative solution to the given problem,

I've utilized the Square Root Decomposition algorithm,

which is often used to optimize the computational

complexity of problems involving range queries.

[remaining content and generated code are truncated]

Figure A7: An example of follow-up Q&A for LeetCode problems.

F Case Study

User:

Write code to calculate all prime numbers in the range of 1-100.

Assistant:

Sure, here is a simple Python code to calculate all prime numbers in

the range of 1 to 100.

```python

def is_prime(n):

if n <= 1:

return False

elif n <= 3:

return True

elif n % 2 == 0 or n % 3 == 0:

return False

i = 1

while i * i <= n:

if n % i == 0 or n % (i + 2) == 0:

return False

i += 1

return True

for num in range(1, 101):

if is_prime(num):

print(num)

```

This code includes a function `is_prime(n)` to check if a number `n` is

prime. It then iterates over the numbers 1 to 100 and prints the

numbers for which `is_prime(n)` returns `True`.

User:.

Execution Result:

Assistant:

I apologize for the confusion, there seems to have been an error in the

code. The function to check if a number is prime was not correctly

implemented. Here is the corrected Python code:

```python

def is_prime(n):

if n <= 1:

return False

elif n <= 3:

return True

elif n % 2 == 0 or n % 3 == 0:

return False

i = 5

while i * i <= n:

if n % i == 0 or n % (i + 2) == 0:

return False

i += 6

return True

for num in range(1, 101):

if is_prime(num):

print(num)

```

In the `is_prime(n)` function, the loop should start from 5 and

increment by 6 each time. This is because all primes are of the form

6k ± 1, with the exception of 2 and 3.

(Continued)

User:.

I want you to extend the existing solution to fit the

arbitrary range x-y. Also, please add comments to your

code for better readability.

Assistant:

Sure thing, here is the updated Python code, now with

parameters for the range and added comments.

```python

def is_prime(n):

"""Checks if a number is prime."""

if n <= 1:

return False # numbers less than or equal to 1 are not

prime

elif n <= 3:

return True # 2 and 3 are prime

elif n % 2 == 0 or n % 3 == 0:

return False # eliminate multiples of 2 and 3

i = 5

while i * i <= n:

# eliminate multiples of primes already found

if n % i == 0 or n % (i + 2) == 0:

return False

i += 6 # increment by 6 to check for prime of form 6k

± 1

return True

def print_primes(x, y):

"""Prints all prime numbers in the range x to y."""

for num in range(x, y + 1):

if is_prime(num):

print(num)

# Now you can call print_primes with any range you want.

# For example, to get primes between 50 and 100, use:

print_primes(50, 100)

```

In this code, `print_primes(x, y)` is a function that prints

all prime numbers between `x` and `y`, inclusive. It does

this by iterating over all numbers in the range and printing

the ones for which `is_prime(num)` returns `True`. You

can call this function with any range of numbers you want.

User:

Execution Result:

Assistant:

I'm glad to see the code worked as expected! Those are

indeed the prime numbers in the range from 50 to 100. If

you have any other questions or need further assistance,

feel free to ask!

Figure A8: A good case where we required our model to write code to calculate all prime numbers in the range of

1-100, and then generalize the function to arbitrary range x-y. Our model successfully corrected the error and made

improvements according to human feedback.

User:

Implement a Python function to check if a string is a valid IPv6

address with regular expressions.

Assistant:

Sure, here is a Python function using Regular Expression (regex) to

check if a string is a valid IPv6 address:

```python

import re

def is_valid_ipv6(ip):

"""

Checks if a given string is a valid IPv6 address.

"""

pattern = re.compile(r'(([0-9a-fA-F]{1,4}:)|{7,7}[0-9a-fA-

F]{1,4}|([0-9a-fA-F]{1,4}:){1,7}:|([0-9a-fA-F]{1,4}:){1,6}:[0-9a-fA-

F]{1,4}|([0-9a-fA-F]{1,4}:){1,5}(:[0-9a-fA-F]{1,4}){1,2}|([0-9a-fA-

F]{1,4}:){1,4}(:[0-9a-fA-F]{1,4}){1,3}|([0-9a-fA-F]{1,4}:){1,3}(:[0-

9a-fA-F]{1,4}){1,4}|([0-9a-fA-F]{1,4}:){1,2}(:[0-9a-fA-

F]{1,4}){1,5}|[0-9a-fA-F]{1,4}:((:[0-9a-fA-F]{1,4}){1,6})|:((:[0-9a-

fA-F]{1,4}){1,7}|:)|fe80:(:[0-9a-fA-F]{0,4}){0,4}%[0-9a-zA-

Z]{1,}|::(ffff(:0{1,4}){0,1}:){0,1}((25[0-5]|(2[0-4]|1{0,1}[0-

9]){0,1}[0-9])\.){3,3}(25[0-5]|(2[0-4]|1{0,1}[0-9]){0,1}[0-9])|([0-9a-

fA-F]{1,4}:){1,4}:((25[0-5]|(2[0-4]|1{0,1}[0-9]){0,1}[0-

9])\.){3,3}(25[0-5]|(2[0-4]|1{0,1}[0-9]){0,1}[0-9]))(?=\s|$)')

return bool(pattern.match(ip))

print(is_valid_ipv6("2404:6800:4003:c02::8a")) # True

print(is_valid_ipv6("2404:6800:4003:804::200e")) # True

print(is_valid_ipv6("102.192.230.21")) # False

print(is_valid_ipv6("2404:6800:4003:804:200e")) # False

```

This function uses a complex regular expression which covers all

possible valid IPv6 formats according to RFC 3986. It checks for

hexadecimal values which are represented in 8 groups of 4

hexadecimal digits.

User:.

Execution Result:

---------------------------------------------------------------------------

error Traceback (most recent call last)

Input In [1], in <cell line: 11>()

[output is truncated]

error: nothing to repeat at position 21

Assistant:

I apologize for the confusion. It seems like there was a mistake in the

regular expression pattern. Let's correct that. Here is the corrected

function:

```python

import re

def is_valid_ipv6(ip):

"""

Checks if a given string is a valid IPv6 address.

"""

pattern = re.compile(r'(([0-9a-fA-F]{1,4}:){7,7}[0-9a-fA-

F]{1,4}|([0-9a-fA-F]{1,4}:){1,7}:|([0-9a-fA-F]{1,4}:){1,6}:[0-9a-fA-

F]{1,4}|([0-9a-fA-F]{1,4}:){1,5}(:[0-9a-fA-F]{1,4}){1,2}|([0-9a-fA-

F]{1,4}:){1,4}(:[0-9a-fA-F]{1,4}){1,3}|([0-9a-fA-F]{1,4}:){1,3}(:[0-

9a-fA-F]{1,4}){1,4}|([0-9a-fA-F]{1,4}:){1,2}(:[0-9a-fA-

F]{1,4}){1,5}|[0-9a-fA-F]{1,4}:((:[0-9a-fA-F]{1,4}){1,6})|:((:[0-9a-

fA-F]{1,4}){1,7}|:)|fe80:(:[0-9a-fA-F]{0,4}){0,4}%[0-9a-zA-

Z]{1,}|::(ffff(:0{1,4}){0,1}:){0,1}((25[0-5]|(2[0-4]|1{0,1}[0-

9]){0,1}[0-9])\.){3,3}(25[0-5]|(2[0-4]|1{0,1}[0-9]){0,1}[0-9])|([0-9a-

fA-F]{1,4}:){1,4}:((25[0-5]|(2[0-4]|1{0,1}[0-9]){0,1}[0-

9])\.){3,3}(25[0-5]|(2[0-4]|1{0,1}[0-9]){0,1}[0-9]))(?=\s|$)')

return bool(pattern.match(ip))

print(is_valid_ipv6("2404:6800:4003:c02::8a")) # True

print(is_valid_ipv6("2404:6800:4003:804::200e")) # True

print(is_valid_ipv6("102.192.230.21")) # False

print(is_valid_ipv6("2404:6800:4003:804:200e")) # False

```

This function should now correctly validate IPv6 addresses. It should

return True for valid IPv6 addresses and False for invalid ones.

(Continued)

User:.

Execution Result:

True

False

Assistant:

Great! The function appears to be working as expected now.

It correctly identifies the strings "2404:6800:4003:c02::8a" and

"2404:6800:4003:804::200e" as valid IPv6 addresses (returning

`True`), and it correctly identifies the strings "102.192.230.21" and

"2404:6800:4003:804:200e" as invalid IPv6 addresses (returning

`False`).

Remember that this function uses regular expressions to match the

input string against the patterns of IPv6 addresses. It checks for

hexadecimal values which are represented in 8 groups of 4

hexadecimal digits.

Let me know if there's anything else I can assist you with!

User:

Can you explain the generated regular expression in detail and make

this function more maintainable?

Assistant:

Sure, let me break down the regular expression for you:

- `([0-9a-fA-F]{1,4}:){7,7}[0-9a-fA-F]{1,4}`: This pattern matches

an IPv6 address that consists of eight groups of hexadecimal digits,

separated by colons.

- `([0-9a-fA-F]{1,4}:){1,7}:`: This pattern matches an IPv6 address

that has a trailing colon and at least one leading group of hexadecimal

digits.

[explanations are truncated]

These patterns are all combined with the `|` operator, which means

that a match on any of these patterns will result in a match for the

entire regular expression.

Here is the code with step-by-step explanation:

```python

import re

def is_valid_ipv6(ip):

"""

Checks if a given string is a valid IPv6 address.

"""

# Compile a regular expression pattern into a regular expression

object, which can be used for matching using its match() method

pattern = re.compile(

r"""(

# Matches 8 groups of hexadecimal digits separated by colons

([0-9a-fA-F]{1,4}:){7,7}[0-9a-fA-F]{1,4}|

# Matches an IPv6 address with a trailing colon and at least

one leading group of hexadecimal digits

([0-9a-fA-F]{1,4}:){1,7}:|

# Matches an IPv6 address that has a group of hexadecimal

digits both before and after a double colon

([0-9a-fA-F]{1,4}:){1,6}:[0-9a-fA-F]{1,4}|

[generated code is truncated]

""", re.VERBOSE)

# If the whole string matches the regular expression pattern, return

True. Otherwise, return False

return bool(pattern.match(ip))

```

This code uses the `re.VERBOSE` flag to allow the regular expression

to be spread out over multiple lines, with comments in between. It

makes the regular expression more readable and maintainable.

Figure A9: A good case that asked our model to implement a Python function to check if a string is a valid IPv6

address with regular expressions. Inspired by errors raised in the execution result, our model managed to recognize

the small error from complex regular expression. Then, our model successfully generated explanation for this

regular expression and improve its readability with thorough in-line comments.

User:

Design a function that finds the intersection of two input lists, and

returns a list of tuples, each containing a distinct element that is

present in both lists and its frequency of occurrence. For this task,

avoid using Python's built-in list functions or any libraries to sort the

result and remove duplicates. Also, implement your own hash table

data structure for this task.

def shared_elements(list1: list, list2: list):

"""Return a list of tuples of unique elements that appeared in both

input lists and their frequency, sorted in ascending order without

using Python's built-in list functions or any libraries. Implement your

own hash table for this task.

>>> shared_elements([1, 4, 1, 3, 4, 653, 2, 5], [5, 7, 1, 5, 9, 653,

121])

[(1, 2), (5, 3), (653, 2)]

>>> shared_elements([5, 3, 3, 2, 8], [3, 2])

[(2, 1), (3, 2)]"""

Assistant:

```python

class HashMap:

[code is truncated]

def shared_elements(list1, list2):

[code is truncated]

for i in range(hash_map1.size)

if hash_map1.get(i) is not None and hash_map2.get(i) is not

None:

intersection.append((i, hash_map1.get(i) + hash_map2.get(i)))

insertion_sort(intersection)

return intersection

def insertion_sort(lst):

for i in range(len(lst)):

j = i

while j > 0 and lst[j - 1] > lst[j]:

lst[j - 1], lst[j] = lst[j], lst[j - 1]

j -= 1

samples = [[1, 4, 1, 3, 4, 653, 2, 5], [5, 7, 1, 5, 9, 653, 121]], [[5, 3, 3,

2, 8], [3, 2]]

for sample in samples:

print(shared_elements(sample[0], sample[1]))

```

User:.

Execution result:

Cell In[2], line 45

for i in range(hash_map1.size)

SyntaxError: invalid syntax

Assistant:

Sorry, seems like there is a small mistake. Let me correct it.

class HashMap:

[code is truncated]

def shared_elements(list1, list2):

[code is truncated]

for i in range(hash_map1.size):

if hash_map1.get(i) is not None and hash_map2.get(i) is not

None:

intersection.append((i, hash_map1.get(i) + hash_map2.get(i)))

insertion_sort(intersection)

return intersection

def insertion_sort(lst):

for i in range(len(lst)):

j = i

while j > 0 and lst[j - 1] > lst[j]:

lst[j - 1], lst[j] = lst[j], lst[j - 1]

j -= 1

samples = [[1, 4, 1, 3, 4, 653, 2, 5], [5, 7, 1, 5, 9, 653, 121]], [[5, 3, 3,

2, 8], [3, 2]]

for sample in samples:

print(shared_elements(sample[0], sample[1]))

(Continued)

User:.

Execution result:

[(1, 3), (5, 3), (653, 2)]

[(2, 2), (3, 3)]

Assistant:

Oops, looks like the output gives the total count of each number from

both lists, but we only want the count per list. Need to adjust this.

Also, it seems like the test results from shared_elements function give

the correct elements but wrong frequencies, which indicates a logical

error. It's counting total occurrences in both lists rather than in each

list. Let's fix this.

```python

class HashMap:

[code is truncated]

def shared_elements(list1, list2):

[code is truncated]

for i in range(hash_map1.size):

if hash_map1.get(i) is not None and hash_map2.get(i) is not

None:

intersection.append((i, min(hash_map1.get(i),

hash_map2.get(i))))

insertion_sort(intersection)

print("Insertion sort result: "+intersection)

return intersection

def insertionsort(lst):

for i in range(len(lst)):

j = i

while j > 0 and lst[j - 1] > lst[j]:

lst[j - 1], lst[j] = lst[j], lst[j - 1]

j -= 1

samples = [[1, 4, 1, 3, 4, 653, 2, 5], [5, 7, 1, 5, 9, 653, 121]], [[5, 3, 3,

2, 8], [3, 2]]

for sample in samples:

print(shared_elements(sample[0], sample[1]))

```

User:.

Execution result:

---------------------------------------------------------------------------

TypeError Traceback (most recent call last)

Cell In[4], line 64

62 samples = [[1, 4, 1,

[Output too much, the middle part output is omitted]

End part of output:

(i), hash_map2.get(i))))

49 insertion_sort(intersection)

---> 51 print("Insertion sort result: "+intersection)

53 return intersection

TypeError: can only concatenate str (not "list") to str

[max retries are exceeded, so this session stops here]

Figure A10: In this bad case, we tried to ask our model to design a function that ﬁnds the intersection of two input

lists and returns a list of tuples, each containing a distinct element that is present in both lists and its frequency of

occurrence. Although our model tried to make corrections, it only corrected one error at a time and ﬁnally exceeded

max number of attempts (3).