Browsing by Subject "Dataset"

Now showing 1 - 4 of 4

Open Access
BFSig: leveraging file significance in bus factor estimation
(Association for Computing Machinery, Inc., 2023) Haratian, Vahid; Evtikhiev, M.; Derakhshanfar, P.; Tüzün, Eray; Kovalenko, V.
Software projects experience the departure of developers due to various reasons. As developers are one of the main sources of knowl edge in software projects, their absence will inevitably result in a certain degree of knowledge depletion. Bus Factor (BF) is a met ric to evaluate how this knowledge loss can affect the project’s continuity
Open Access
International political economy in Turkey: the evolution and current state of a maturing subfield
(International Relations Council of Turkey, 2020) Köstem, Seçkin; Şen, Ö. F.
Since its emergence in the 1970s, international political economy (IPE) has been one of the main subfields of International Relations (IR) in North America and Britain. The past two decades have witnessed a growing academic interest in IPE among Turkish IR scholars. This study explores the emergence, evolution and the current state of IPE studies in Turkey. Based on an original dataset, it examines the research dimension of Turkish IPE and presents a comprehensive overview of the thematic, theoretical and methodological orientations of the publications of Turkish IPE scholars. It also offers implications on the sociology of IPE in Turkey.
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Taxonomy of inline code comment smells
(Springer, 2024-04-03) Jabrayilzade, Elgun; Yurtoğlu, Ayda; Tüzün, Eray
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 a good practice in software engineering. However, low-quality comments can have a detrimental effect on software quality or be ineffective for code understanding. This study aims to create a taxonomy of inline code comment smells and determine how frequently each smell type occurs in software projects. We conducted a multivocal literature review to define the initial taxonomy of inline comment smells. 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 created a taxonomy of 11 inline code comment smell types and 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 impact 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. We also opened pull requests and issues fixing the comment smells in the sampled projects, where we got a 27% acceptance rate. We share our manually labeled dataset online and provide implications for software engineering practitioners, researchers, and educators.
Unknown
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.