Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
119 tokens/sec
GPT-4o
56 tokens/sec
Gemini 2.5 Pro Pro
43 tokens/sec
o3 Pro
6 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Code Smells Detection via Modern Code Review: A Study of the OpenStack and Qt Communities (2205.07535v1)

Published 16 May 2022 in cs.SE

Abstract: Code review that detects and locates defects and other quality issues plays an important role in software quality control. One type of issue that may impact the quality of software is code smells. Yet, little is known about the extent to which code smells are identified during modern code review. To investigate the concept behind code smells identified in modern code review and what actions reviewers suggest and developers take in response to the identified smells, we conducted a study of code smells in code reviews by analyzing reviews from four large open source projects from the OpenStack (Nova and Neutron) and Qt (Qt Base and Qt Creator) communities. We manually checked a total of 25,415 code review comments obtained by keywords search and random selection, and identified 1,539 smell-related reviews. Our analysis found that 1) code smells were not commonly identified in code reviews, 2) smells were usually caused by violation of coding conventions, 3) reviewers usually provided constructive feedback, including fixing (refactoring) recommendations to help developers remove smells, 4) developers generally followed those recommendations and actioned the changes, 5) once identified by reviewers, it usually takes developers less than one week to fix the smells, and 6) the main reason why developers chose to ignore the identified smells is not worth fixing the smell. Our results suggest that: 1) developers should closely follow coding conventions in their projects to avoid introducing code smells, 2) review-based detection of code smells is perceived to be a trustworthy approach by developers, mainly because reviews are context-sensitive (as reviewers are more aware of the context of the code given that they are part of the project's development team), and 3) program context needs to be fully considered in order to make a decision of whether to fix the identified code smell immediately.

User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (7)
  1. Xiaofeng Han (8 papers)
  2. Amjed Tahir (34 papers)
  3. Peng Liang (94 papers)
  4. Steve Counsell (8 papers)
  5. Kelly Blincoe (12 papers)
  6. Bing Li (374 papers)
  7. Yajing Luo (7 papers)
Citations (9)

Summary

We haven't generated a summary for this paper yet.