Browsing by Author "Jabrayilzade, Elgun"

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    Bug tracking process smells in practice
    (IEEE Computer Society, 2022-05-27) Jabrayilzade, Elgun; Evtikhiev, Mikhail; Tüzün, Eray; Kovalenko, Vladimir
    Software teams use bug tracking (BT) tools to report and manage bugs. Each record in a bug tracking system (BTS) is a reporting entity consisting of several information fields. The contents of the reports are similar across different tracking tools, though not the same. The variation in the workflow between teams prevents defining an ideal process of running BTS. Nevertheless, there are best practices reported both in white and gray literature. Developer teams may not adopt the best practices in their BT process. This study investigates the non-compliance of developers with best practices, so-called smells, in the BT process. We mine bug reports of four projects in the BTS of JetBrains, a software company, to observe the prevalence of BT smells in an industrial setting. Also, we survey developers to see (1) if they recognize the smells, (2) their perception of the severity of the smells, and (3) the potential benefits of a BT process smell detection tool. We found that (1) smells occur, and their detection requires a solid understanding of the BT practices of the projects, (2) smell severity perception varies across smell types, and (3) developers considered that a smell detection tool would be useful for six out of the 12 smell categories.
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    Taxonomy of inline code comment smells
    (2022-07) Jabrayilzade, Elgun
    Code comments play a vital role in source code comprehension and software maintainability. It is common for developers to write comments to explain a code snippet, and commenting code is generally considered as a good practice in soft-ware engineering. However, low-quality comments can have a detrimental effect on software quality or be ineffective for code understanding. In this study, we conducted a multivocal literature review and created a taxonomy of inline code comments smells consisting of 11 types. Afterward, we manually labeled 2447 inline comments from eight open-source projects where half of them were Java, and another half were Python projects. We found out that the smells exist in both Java and Python projects with varying degrees. Moreover, we conducted an online survey with 41 software practitioners to learn their opinions on these smells and their effect on code comprehension and software maintainability. The survey respondents generally agreed with the taxonomy; however, they reported that some smell types might have a positive effect on code comprehension in certain scenarios. Additionally, using our labeled dataset, we developed various machine learning-based models to categorize the smell types automatically. Our best model achieved an F1 score of 0.53. We share our manually labeled dataset online and provide implications of this study for software engineering practition-ers, researchers, and educators.