Commits represent an essential part of software development practices, serving as the means for collaboration and management of software changes made to a software project’s codebase. Changes are necessary for the survival of software, for instance, to fix faults, address security vulnerabilities, meet new functional requirements, and improve software performance. To aid software research and practice, several research endeavors attempted to automate the process of identifying the nature of the maintenance tasks performed in a commit based on the data associated with the commit. This paper presents a systematic literature review of supervised-learning-based models for commit classification into maintenance activities. Through the study selection process, 19 primary studies were identified, published between 2008 and 2023 in various journals and conference proceedings. For the independent variables for classification, features extracted from commit messages are prevalent, followed by features extracted from source code and commit metadata. In the majority of existing studies, multi-class classification approaches are used, whereas multi-label approaches are considered rarely. The most commonly used classification algorithms include Decision Tree, Random Forest, Naive Bayes, and Neural Network. Several datasets exist, consisting mainly of commits from different open-source projects based on the Java programming language. Some research gaps and open challenges were identified that can guide future research efforts.

Changes to a software project are inevitable as the software requires continuous adaptations, improvements, and corrections throughout maintenance. Identifying the purpose and impact of changes made to the codebase is critical in software engineering. However, manually identifying and characterizing software changes can be a time-consuming and tedious process that adds to the workload of software engineers. To address this challenge, several attempts have been made to automatically identify and demystify intents of software changes based on software artifacts such as commit change logs, issue reports, change messages, source code files, and software documentation. However, these existing approaches have their limitations. These include a lack of data, limited performance, and an inability to evaluate compound changes. This paper presents a doctoral research proposal that aims to automate the process of identifying commit-level changes in software projects using software repository mining and code representation learning models. The research background, state-of-the-art, research objectives, research agenda, and threats to validity are discussed.

When developers collaborate on a software project, the style of the code should be consistent across the codebase. However, as developers do not always concur on code style practices, achieving a consensus can be burdensome. Even when code style guidelines are defined for a project, developers often have difficulties adhering to them. Additionally, when working on several projects with diverse guidelines, following different styles can become a frustrating, error-prone, and time-consuming task for a developer. This work presents experiences from adopting two development workflow patterns that automatically ensure that code’s layout and formatting are consistent in a project’s repository while enabling each developer to utilize their preferences locally. From our experience, using any of these two patterns provides a successful way for collaborative software development that allows developers to relocate their time and concerns from code formatting to more valuable matters.