A case study on the transformative potential of AI in software engineering on LeetCode and ChatGPT

• URL: http://arxiv.org/abs/2501.03639v1
• Date: Tue, 07 Jan 2025 09:15:25 GMT
• Title: A case study on the transformative potential of AI in software engineering on LeetCode and ChatGPT
• Authors: Manuel Merkel, Jens Dörpinghaus,
• Abstract summary: This study employs a methodological approach, with the objective of comparing the software quality of Python programs produced by LeetCode users with that generated by GPT-4o. The findings indicate that GPT-4o does not present a considerable impediment to code quality, understandability, or runtime when generating code on a limited scale.
• Score: 0.0
• License:
• Abstract: The recent surge in the field of generative artificial intelligence (GenAI) has the potential to bring about transformative changes across a range of sectors, including software engineering and education. As GenAI tools, such as OpenAI's ChatGPT, are increasingly utilised in software engineering, it becomes imperative to understand the impact of these technologies on the software product. This study employs a methodological approach, comprising web scraping and data mining from LeetCode, with the objective of comparing the software quality of Python programs produced by LeetCode users with that generated by GPT-4o. In order to gain insight into these matters, this study addresses the question whether GPT-4o produces software of superior quality to that produced by humans. The findings indicate that GPT-4o does not present a considerable impediment to code quality, understandability, or runtime when generating code on a limited scale. Indeed, the generated code even exhibits significantly lower values across all three metrics in comparison to the user-written code. However, no significantly superior values were observed for the generated code in terms of memory usage in comparison to the user code, which contravened the expectations. Furthermore, it will be demonstrated that GPT-4o encountered challenges in generalising to problems that were not included in the training data set. This contribution presents a first large-scale study comparing generated code with human-written code based on LeetCode platform based on multiple measures including code quality, code understandability, time behaviour and resource utilisation. All data is publicly available for further research.

Related papers

• Comparing Human and LLM Generated Code: The Jury is Still Out! [8.456554883523472]
  We compare the effectiveness of large language models (LLMs) and human programmers in producing Python software code. We use various static analysis benchmarks, including Pylint, Radon, Bandit and test cases. We observe security flaws in code generated by both humans and GPT-4, but GPT-4 code included more severe outliers.
  arXiv  Detail & Related papers  (2025-01-28T11:11:36Z)
• Understanding Code Understandability Improvements in Code Reviews [79.16476505761582]
  We analyzed 2,401 code review comments from Java open-source projects on GitHub. 83.9% of suggestions for improvement were accepted and integrated, with fewer than 1% later reverted.
  arXiv  Detail & Related papers  (2024-10-29T12:21:23Z)
• Impact of the Availability of ChatGPT on Software Development: A Synthetic Difference in Differences Estimation using GitHub Data [49.1574468325115]
  ChatGPT is an AI tool that enhances software production efficiency. We estimate ChatGPT's effects on the number of git pushes, repositories, and unique developers per 100,000 people. These results suggest that AI tools like ChatGPT can substantially boost developer productivity, though further analysis is needed to address potential downsides such as low quality code and privacy concerns.
  arXiv  Detail & Related papers  (2024-06-16T19:11:15Z)
• CodeIP: A Grammar-Guided Multi-Bit Watermark for Large Language Models of Code [56.019447113206006]
  Large Language Models (LLMs) have achieved remarkable progress in code generation. CodeIP is a novel multi-bit watermarking technique that inserts additional information to preserve provenance details. Experiments conducted on a real-world dataset across five programming languages demonstrate the effectiveness of CodeIP.
  arXiv  Detail & Related papers  (2024-04-24T04:25:04Z)
• How Far Have We Gone in Binary Code Understanding Using Large Language Models [51.527805834378974]
  We propose a benchmark to evaluate the effectiveness of Large Language Models (LLMs) in binary code understanding. Our evaluations reveal that existing LLMs can understand binary code to a certain extent, thereby improving the efficiency of binary code analysis.
  arXiv  Detail & Related papers  (2024-04-15T14:44:08Z)
• Whodunit: Classifying Code as Human Authored or GPT-4 Generated -- A case study on CodeChef problems [0.13124513975412253]
  We use code stylometry and machine learning to distinguish between GPT-4 generated and human-authored code. Our dataset comprises human-authored solutions from CodeChef and AI-authored solutions generated by GPT-4. Our study shows that code stylometry is a promising approach for distinguishing between GPT-4 generated code and human-authored code.
  arXiv  Detail & Related papers  (2024-03-06T19:51:26Z)
• Comparing large language models and human programmers for generating programming code [0.0]
  GPT-4 substantially outperforms other large language models, including Gemini Ultra and Claude 2. In most LeetCode and GeeksforGeeks coding contests evaluated in this study, GPT-4 employing the optimal prompt strategy outperforms 85 percent of human participants.
  arXiv  Detail & Related papers  (2024-03-01T14:43:06Z)
• Comparing Software Developers with ChatGPT: An Empirical Investigation [0.0]
  This paper conducts an empirical investigation, contrasting the performance of software engineers and AI systems, like ChatGPT, across different evaluation metrics. The paper posits that a comprehensive comparison of software engineers and AI-based solutions, considering various evaluation criteria, is pivotal in fostering human-machine collaboration.
  arXiv  Detail & Related papers  (2023-05-19T17:25:54Z)
• AI-assisted coding: Experiments with GPT-4 [0.22366638308792727]
  GPT-4 can generate tests with substantial coverage, but that many of the tests fail applied to the associated code. These findings suggest that while AI coding tools are very powerful, they still require humans in the loop to ensure validity and accuracy of the results.
  arXiv  Detail & Related papers  (2023-04-25T22:59:01Z)
• PEOPL: Characterizing Privately Encoded Open Datasets with Public Labels [59.66777287810985]
  We introduce information-theoretic scores for privacy and utility, which quantify the average performance of an unfaithful user. We then theoretically characterize primitives in building families of encoding schemes that motivate the use of random deep neural networks.
  arXiv  Detail & Related papers  (2023-03-31T18:03:53Z)
• Measuring Coding Challenge Competence With APPS [54.22600767666257]
  We introduce APPS, a benchmark for code generation. Our benchmark includes 10,000 problems, which range from having simple one-line solutions to being substantial algorithmic challenges. Recent models such as GPT-Neo can pass approximately 15% of the test cases of introductory problems.
  arXiv  Detail & Related papers  (2021-05-20T17:58:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.