Abstract Context Software development comprises many processes, including Code Review (CR) and Bug Tracking (BT). Although no perfect practices exist, recognizing bad practices helps avoid detrimental habits in CR and BT. Objectives This study investigates CR and BT process smells in industrial settings using Smellyzer , a detection tool based on established smell taxonomies. We aimed to explore the challenges, insights, and outcomes of applying a smell detection tool in real-world software projects. Method We conducted an evaluative case study involving a large-scale proprietary company, analyzing CR and BT smells in a large software project using Smellyzer . We collected data through pre-surveys, focus groups, questionnaires, follow-up study and tool-based analyses, and then analyzed these data using triangulation and grounded theory methods. Results Practitioners confirmed the presence of CR and BT smells in their workflows and provided detailed feedback on root causes and their impacts on development efficiency. They also rated the tool’s usefulness at identifying CR smells as 5 out of 5 and BT smells as 4.6 out of 5. Its practicality, measured by the System Usability Scale, scored 77.5 for CR and 80.0 for BT smell detection. Conclusions This study highlights the presence of CR and BT smells in industrial settings and their potential to affect workflow efficiency and software quality. While our tool, Smellyzer , proved effective in identifying these smells, the findings emphasize the importance of integrating theoretical frameworks with practical solutions. Future work should explore these smells across diverse project contexts and investigate their influence on development practices over time.

GitHub Issues is a widely used issue tracking tool in open-source software projects. Originally designed with broad flexibility, its lack of standardization led to incomplete issue reports, impeding software development and maintenance efficiency. To counteract this, GitHub introduced issue templates in 2016, which rapidly became popular. Our study assesses the current use and evolution of these templates in large-scale open-source projects and their impact on issue tracking metrics, including resolution time, number of reopens, and number of issue comments. Employing a comprehensive analysis of 350 templates from 100 projects, we also evaluated over 1.9 million issues for template conformity and impact. Additionally, we solicited insights from open-source software maintainers through a survey. Our findings highlight issue templates’ extensive usage in 99 of the 100 surveyed projects, with a growing preference for YAML-based templates, a more structured template variant. Projects with a template exhibited markedly reduced resolution time (381.02 days to 103.18 days) and reduced issue comment count (4.95 to 4.32) compared to those without. The use of YAML-based templates further significantly decreased resolution time, the number of reopenings, and the discussion extent. Thus, our research underscores issue templates’ positive impact on large-scale open-source projects, offering recommendations for improved effectiveness.

Bug tracking is the process of monitoring and reporting malfunctions or issues found in software. While there is no consensus on a formally specified bug tracking process, some certain rules and best practices for an optimal bug tracking process are accepted by many companies and open-source software (OSS) projects. Despite slight variations between different platforms, the primary aim of all these rules and practices is to perform a more efficient bug tracking process. Practitioners’ noncompliance with the best practices not only impedes the benefits of the bug tracking process but also negatively affects the other phases of the life cycle of software development.In this study, based on the results of a multivocal literature review, we analyzed 60 sources in academic and gray literature and propose a taxonomy of 12 bad practices in the bug tracking process, that is bug tracking process smells. To quantitatively analyze these process smells, we inspect bug reports collected from six projects. Among these projects, four of them are Jira-based (MongoDB Core Server, Evergreen, Confluence Server & Data Center, Jira Server & Data Center) and the other two are Bugzilla-based (GCC and Wireshark). We observed that a considerable amount of bug tracking process smells exist in all projects with varying ratios.

During the lifecycle of a software project, software artifacts constantly change. A change should be peer-reviewed to ensure the software quality. To maximize the benefit of review, the reviewer(s) should be chosen appropriately. However, choosing the right reviewer(s) might not be trivial especially in large projects. Researchers developed different methods to recommend reviewers. In this study, we introduce a novel approach for reviewer recommendation problem. Our approach utilizes the traceability graph of a software project and assigns a know-about score to each developer, then recommends the developers who have the maximum know-about score for an artifact. We tested our approach on an open source project and achieved top-3 recall of 0.85 with an MRR (mean reciprocal ranking) of 0.73.