Who Can Maintain This Code?: Assessing the Effectiveness of Repository-Mining Techniques for Identifying Software Maintainers

Avelino, Guilherme; Passos, Leonardo; Petrillo, Fábio; Valente, M.

doi:10.1109/ms.2018.185140155

Cited by 12 publications

(14 citation statements)

References 8 publications

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“…In summary, these studies are based mainly on information about changes such as the number of commits and who made the last change to identify expertise. However, based on past works [7,41], we suspect that these variables alone are not enough. For this reason, in this study, we analyze more variables and their relationship with developers' knowledge.…”

Section: Research That Proposes New Techniquesmentioning

confidence: 95%

“…Regarding the differences for the models studied in this work, DOK does not deal with recency directly and does not consider the size of the file when estimating knowledge. These two variables were pointed out as important factors in the calculation of knowledge in previous works [7,41].…”

Section: Research That Proposes New Techniquesmentioning

confidence: 99%

“…Other works used techniques and models to identify expertise. Avelino and colleagues compared the performance of Commits, Blame, and Degree-of-Authorship (DOA) techniques in identifying source code file maintainers [7]. A survey similar to the one presented in this paper was made to create a dataset with data from eight open-source repositories and two private ones.…”

Section: Comparison Of Existing Techniquesmentioning

confidence: 99%

“…The work presented in this paper can be distinguished from the works described before in three key aspects: purpose, compared techniques, and scope. In relation to purpose, two works have the same objectives as ours: Avelino et al [7] and Anvik and Murphy [3] (but they do not use the same techniques, particularly machine learning classifiers).…”

Section: Comparison Of Existing Techniquesmentioning

confidence: 99%

“…Some research has been conducted to address the file expert identification problem. For example, in the work [7], the authors compared the performance of three techniques for identifying file experts. They identified an opportunity for improving the performance of existing techniques by adding information on file size and recency of modifications.…”

Section: Introductionmentioning

confidence: 99%

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Identifying Source Code File Experts

Cury

Avelino

Neto

et al. 2022

Proceedings of the 16th ACM / IEEE International Symposium on Empirical Software Engineering and Measurement

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Background: In software development, the identification of source code file experts is an important task. Identifying these experts helps to improve software maintenance and evolution activities, such as developing new features, code reviews, and bug fixes. Although some studies have proposed repository-mining techniques to automatically identify source code experts, there are still gaps in this area that can be explored. For example, investigating new variables related to source code knowledge and applying machine learning aiming to improve the performance of techniques to identify source code experts. Aim: The goal of this study is to investigate opportunities to improve the performance of existing techniques to recommend source code files experts. Method: We built an oracle by collecting data from the development history and surveying developers of 113 software projects. Then, we use this oracle to: (i) analyze the correlation between measures extracted from the development history and the developers' source code knowledge and (ii) investigate the use of machine learning classifiers by evaluating their performance in identifying source code files experts. Results: First Authorship and Recency of Modification are the variables with the highest positive and negative correlations with source code knowledge, respectively. Machine learning classifiers outperformed the linear techniques (F-Measure = 71% to 73%) in the public dataset, but this advantage is not clear in the private dataset, with F-Measure ranging from 55% to 68% for the linear techniques and 58% to 67% for ML techniques. Conclusion: Overall, the linear techniques and the machine learning classifiers achieved similar performance, particularly if we analyze F-Measure. However, machine learning classifiers usually get higher precision while linear techniques obtained the highest recall values. Therefore, the choice of the best technique depends on the user's tolerance to false positives and false negatives.

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Section: Research That Proposes New Techniquesmentioning

confidence: 95%

Section: Research That Proposes New Techniquesmentioning

confidence: 99%

Section: Comparison Of Existing Techniquesmentioning

confidence: 99%

Section: Comparison Of Existing Techniquesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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