Understanding Code Understandability Improvements in Code Reviews

• URL: http://arxiv.org/abs/2410.21990v1
• Date: Tue, 29 Oct 2024 12:21:23 GMT
• Title: Understanding Code Understandability Improvements in Code Reviews
• Authors: Delano Oliveira, Reydne Santos, Benedito de Oliveira, Martin Monperrus, Fernando Castor, Fernanda Madeiral,
• Abstract summary: 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.
• Score: 79.16476505761582
• License:
• Abstract: Motivation: Code understandability is crucial in software development, as developers spend 58% to 70% of their time reading source code. Improving it can improve productivity and reduce maintenance costs. Problem: Experimental studies often identify factors influencing code understandability in controlled settings but overlook real-world influences like project culture, guidelines, and developers' backgrounds. Ignoring these factors may yield results with limited external validity. Objective: This study investigates how developers enhance code understandability through code review comments, assuming that code reviewers are specialists in code quality. Method and Results: We analyzed 2,401 code review comments from Java open-source projects on GitHub, finding that over 42% focus on improving code understandability. We further examined 385 comments specifically related to this aspect and identified eight categories of concerns, such as inadequate documentation and poor identifiers. Notably, 83.9% of suggestions for improvement were accepted and integrated, with fewer than 1% later reverted. We identified various types of patches that enhance understandability, from simple changes like removing unused code to context-dependent improvements such as optimizing method calls. Additionally, we evaluated four well-known linters for their ability to flag these issues, finding they cover less than 30%, although many could be easily added as new rules. Implications: Our findings encourage the development of tools to enhance code understandability, as accepted changes can serve as reliable training data for specialized machine-learning models. Our dataset supports this training and can inform the development of evidence-based code style guides. Data Availability: Our data is publicly available at https://codeupcrc.github.io.

Related papers

• Assessing Consensus of Developers' Views on Code Readability [3.798885293742468]
  Developers now spend more time reviewing code than writing it, highlighting the importance of Code Readability for code comprehension. Previous research found that existing Code Readability models were inaccurate in representing developers' notions. We surveyed 10 Java developers with similar coding experience to evaluate their consensus on Code Readability assessments and related aspects.
  arXiv  Detail & Related papers  (2024-07-04T09:54:42Z)
• An Empirical Study on Code Review Activity Prediction and Its Impact in Practice [7.189276599254809]
  This paper aims to help code reviewers by predicting which files in a submitted patch need to be commented, (2) revised, or (3) are hot-spots (commented or revised) Our empirical study on three open-source and two industrial datasets shows that combining the code embedding and review process features leads to better results than the state-of-the-art approach.
  arXiv  Detail & Related papers  (2024-04-16T16:20:02Z)
• 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)
• Comments as Natural Logic Pivots: Improve Code Generation via Comment Perspective [85.48043537327258]
  We propose MANGO (comMents As Natural loGic pivOts), including a comment contrastive training strategy and a corresponding logical comment decoding strategy. Results indicate that MANGO significantly improves the code pass rate based on the strong baselines. The robustness of the logical comment decoding strategy is notably higher than the Chain-of-thoughts prompting.
  arXiv  Detail & Related papers  (2024-04-11T08:30:46Z)
• Code Prompting Elicits Conditional Reasoning Abilities in Text+Code LLMs [65.2379940117181]
  We introduce code prompting, a chain of prompts that transforms a natural language problem into code. We find that code prompting exhibits a high-performance boost for multiple LLMs. Our analysis of GPT 3.5 reveals that the code formatting of the input problem is essential for performance improvement.
  arXiv  Detail & Related papers  (2024-01-18T15:32:24Z)
• Toward Effective Secure Code Reviews: An Empirical Study of Security-Related Coding Weaknesses [14.134803943492345]
  We conducted an empirical case study in two large open-source projects, OpenSSL and PHP. Based on 135,560 code review comments, we found that reviewers raised security concerns in 35 out of 40 coding weakness categories. Some coding weaknesses related to past vulnerabilities, such as memory errors and resource management, were discussed less often than the vulnerabilities.
  arXiv  Detail & Related papers  (2023-11-28T00:49:00Z)
• How do Developers Improve Code Readability? An Empirical Study of Pull Requests [0.0]
  We collect 370 code readability improvements from 284 Merged Pull Requests (PRs) under 109 GitHub repositories. We produce a catalog with 26 different types of code readability improvements. Surprisingly, SonarQube only detected 26 out of the 370 code readability improvements.
  arXiv  Detail & Related papers  (2023-09-05T21:31:21Z)
• CONCORD: Clone-aware Contrastive Learning for Source Code [64.51161487524436]
  Self-supervised pre-training has gained traction for learning generic code representations valuable for many downstream SE tasks. We argue that it is also essential to factor in how developers code day-to-day for general-purpose representation learning. In particular, we propose CONCORD, a self-supervised, contrastive learning strategy to place benign clones closer in the representation space while moving deviants further apart.
  arXiv  Detail & Related papers  (2023-06-05T20:39:08Z)
• The Mind Is a Powerful Place: How Showing Code Comprehensibility Metrics Influences Code Understanding [10.644832702859484]
  We investigate whether a displayed metric value for source code comprehensibility anchors developers in their subjective rating of source code comprehensibility. We found that the displayed value of a comprehensibility metric has a significant and large anchoring effect on a developer's code comprehensibility rating.
  arXiv  Detail & Related papers  (2020-12-16T14:27:45Z)
• Deep Just-In-Time Inconsistency Detection Between Comments and Source Code [51.00904399653609]
  In this paper, we aim to detect whether a comment becomes inconsistent as a result of changes to the corresponding body of code. We develop a deep-learning approach that learns to correlate a comment with code changes. We show the usefulness of our approach by combining it with a comment update model to build a more comprehensive automatic comment maintenance system.
  arXiv  Detail & Related papers  (2020-10-04T16:49:28Z)

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.