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Identifying Source Code File Experts
Federal University of Piauí, BRA.
Federal University of Piauí, BRA.
Federal University of Piauí, BRA.
Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.ORCID iD: 0000-0002-7220-9570
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2022 (English)In: ESEM '22: Proceedings of the 16th ACM / IEEE International Symposium on Empirical Software Engineering and Measurement / [ed] Madeiral F., Lassenius C., Conte T., Mannisto T., IEEE Computer Society, 2022, p. 125-136Conference paper, Published paper (Refereed)
Abstract [en]

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. © 2022 Association for Computing Machinery.

Place, publisher, year, edition, pages
IEEE Computer Society, 2022. p. 125-136
Series
International Symposium on Empirical Software Engineering and Measurement, ISSN 1949-3770, E-ISSN 1949-3789
Keywords [en]
machine learning, mining software repository, software evolution, software maintenance, source-code expertise, Classification (of information), Codes (symbols), Computer programming languages, Software design, Learning classifiers, Linear techniques, Machine-learning, Mining software, Performance, Software repositories, Source codes, Computer software maintenance
National Category
Software Engineering
Identifiers
URN: urn:nbn:se:bth-23785DOI: 10.1145/3544902.3546243Scopus ID: 2-s2.0-85139845140ISBN: 9781450394277 (print)OAI: oai:DiVA.org:bth-23785DiVA, id: diva2:1707021
Conference
16th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement, ESEM 2022, Helsinki, 18 September through 23 September 2022
Note

open access

Available from: 2022-10-28 Created: 2022-10-28 Last updated: 2022-12-13Bibliographically approved

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Britto, Ricardo

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