Code review is a widely adopted quality assurance practice in software engineering, where expert reviewers assess developers’ code changes before merging. While prior studies have explored review comment quality and usefulness, they often overlook the clarity and understandability of Code Change Request (CCR) comments. Unclear CCR comments can pose significant challenges for developers to address. Therefore, this study investigates the prevalence and impact of confusing or unclear CCR comments and proposes two approaches to enhance CCR communication during code review. Using a dataset of 182 open-source GitHub projects with over 55 K pull requests and 466 K CCR comments, we analyzed how often unclear comments occur and their effects on the review process. Our classifier, built from manually annotated developers’ replies in response to CCR comments, revealed that $24 \%$ of comments led to author confusion. Statistical analysis shows that unclear CCR comments significantly increase resolution time and discussion length, and that pull requests with clear CCR comments are more likely to be addressed and merged. A manual analysis of 400 confusing CCR comments identified six key characteristics, with lack of clarity and unclear rationale being the most common. Our first approach, the confusion classifier, flags authors’ confusion to enable reviewers to clarify ambiguities promptly (recall of 0.96), while the second classifier enables reviewers to evaluate the clarity and understandability of their CCR comments (recall of 0.93). This pioneering study further provides recommendations for enhancing CCR comments and offering a foundation for future research to streamline the review process.

The Android permission system regulates access to sensitive mobile device resources such as camera and location. To access these resources, third-party developers need to request permissions. However, the Android permission system is complex and fast-evolving, presenting developers with numerous challenges surrounding compatibility issues, misuse of permissions, and vulnerabilities related to permissions. Our study aims to explore whether Large Language Models (LLMs) can serve as a reliable tool to assist developers in using Android permissions correctly and securely, thereby reducing the risks of misuse and security vulnerabilities in apps. In our study, we analyzed 1,008 Stack Overflow questions related to Android permissions and their accepted answers. In parallel, we generate answers to these questions using a popular LLM tool, ChatGPT. We focused on how well the ChatGPT's responses align with the accepted answers on Stack Overflow. Our findings show that above 50% of ChatGPT's answers align with Stack Overflow's accepted answers. ChatGPT offers better-aligned responses for challenges related to Documentation and Conceptual Understanding, while it provides less aligned answers for Debugging-related issues. In addition, we found that ChatGPT provides more consistent answers for 73.27% questions. Our study demonstrates the potential for using LLMs such as ChatGPT as a supporting tool to help developers navigate Android permission-related problems.

Context: Technical Debt (TD) is a metaphor used to describe code that is "not quite right." Although TD studies have gained momentum, TD has yet to be studied as thoroughly in non-Object-Oriented (OO) or scientific software such as R. R is a multi-paradigm programming language, whose popularity in data science and statistical applications has amplified in recent years. Due to R's inherent ability to expand through user-contributed packages, several community-led organizations were created to organize and peer-review packages in a concerted effort to increase their quality. Nonetheless, it is well-known that most R users do not have a technical programming background, being from multiple disciplines. Objective: The goal of this study is to investigate TD in the documentation of the peer-review of R packages led by rOpenSci. Method: We collected over 5,000 comments from 157 packages that had been reviewed and approved to be published at rOpenSci. We manually analyzed a sample dataset of these comments posted by package authors, editors of rOpenSci, and reviewers during the review process to investigate the types of TD present in these reviews. Results: The findings of our study include (i) a taxonomy of TD derived from our analysis of the peer-reviews (ii) documentation debt as being the most prevalent type of debt (iii) different user roles are concerned with different types of TD. For instance, reviewers tend to report some types of TD more than other roles, and the types of TD they report are different from those reported by the authors of a package. Conclusion: TD analysis in scientific software or peer-review is almost non-existent. Our study is a pioneer but within the context of R packages. However, our findings can serve as a starting point for replication studies, given our public datasets, to perform similar analyses in other scientific software or to investigate the rationale behind our findings.