How do Developers Improve Code Readability? An Empirical Study of Pull Requests

Dantas, Carlos Eduardo C.; Rocha, Adriano M.; Maia, Marcelo A.

doi:10.1109/icsme58846.2023.00022

2023 IEEE International Conference on Software Maintenance and Evolution (ICSME) 2023

DOI: 10.1109/icsme58846.2023.00022

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How do Developers Improve Code Readability? An Empirical Study of Pull Requests

Carlos Eduardo C. Dantas,

Adriano M. Rocha,

Marcelo A. Maia

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2024

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Cited by 2 publications

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How does Technical Debt Evolve within Pull Requests? An Empirical Study with Apache Projects

Calixto,

Araújo,

Alves

2024

Anais Do XXXVIII Simpósio Brasileiro De Engenharia De Software (SBES 2024)

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Technical Debt (TD) refers to the cost to rectify the quality issues affecting a software system. A pull request (PR) represents a discrete unit of work that must adhere to particular quality standards to be integrated into the main codebase. They can serve as a means to gauge how developers address TD and how codebase quality may decay over time. In this work, we present an empirical study on 12 Apache Java repositories, analyzing 2,035 PRs to examine how TD evolves within them. Using the SonarQube tool, we evaluated (i) how TD changes within PRs and (ii) which types of TD issue are most frequently overlooked and resolved. Our results suggest that TD issues are prevalent in PRs, with a tendency to follow a ratio of 1:2:1 (reduced: unchanged: increased). Furthermore, across all issues, we found that the most frequently overlooked are related to code duplication and cognitive complexity, while the most resolved ones include code duplication and obsolete code. These insights can help practitioners become more aware and might inspire the creation of new tools that make managing TD during PRs easier.

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How does Technical Debt Evolve within Pull Requests? An Empirical Study with Apache Projects

Calixto,

Araújo,

Alves

2024

Anais Do XXXVIII Simpósio Brasileiro De Engenharia De Software (SBES 2024)

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What Developers Ask to ChatGPT in GitHub Pull Requests? an Exploratory Study

Silva,

Dantas,

Maia

2024

Anais Do XII Workshop De Visualização, Evolução E Manutenção De Software (VEM 2024)

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The emergence of Large Language Models (LLMs), such as ChatGPT, has introduced a new set of tools to support software developers in solving programming tasks. However, our understanding of the interactions (i.e., prompts) between developers and ChatGPT that result in contributions to the codebase remains limited. To explore this limitation, we conducted a manual evaluation of 155 valid ChatGPT share links extracted from 139 merged Pull Requests (PRs), revealing the interactions between developers and reviewers with ChatGPT that led to merges into the main codebase. Our results produced a catalog of 14 types of ChatGPT requests categorized into four main groups. We found a significant number of requests involving code review and the implementation of code snippets based on specific tasks. Developers also sought to clarify doubts by requesting technical explanations or by asking for text refinements for their web pages. Furthermore, we verified that prompts involving code generation generally required more interactions to produce the desired answer compared to prompts requesting text review or technical information.

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How do Developers Improve Code Readability? An Empirical Study of Pull Requests

Cited by 2 publications

References 46 publications

How does Technical Debt Evolve within Pull Requests? An Empirical Study with Apache Projects

How does Technical Debt Evolve within Pull Requests? An Empirical Study with Apache Projects

What Developers Ask to ChatGPT in GitHub Pull Requests? an Exploratory Study

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