Mining software repositories for software architecture - A systematic mapping study

被引:0
|
作者
Soliman, Mohamed [1 ]
Albonico, Michel [2 ]
Malavolta, Ivano [3 ]
Wortmann, Andreas [4 ]
机构
[1] Paderborn Univ, Heinz Nixdorf Inst, Paderborn, Germany
[2] Fed Univ Technol Parana UTFPR, IntelAgir Res Grp, Francisco Beltrao, PR, Brazil
[3] Vrije Univ Amsterdam, Software & Sustainabil Res Grp, Amsterdam, Netherlands
[4] Univ Stuttgart, Inst Control Engn Machine Tools & Mfg Units ISW, Stuttgart, Germany
关键词
Mining software repositories; Software architecture; Empirical research; CODE; KNOWLEDGE; RECOVERY; MODEL;
D O I
10.1016/j.infsof.2025.107677
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Context: A growing number of researchers are investigating how Mining Software Repositories (MSR) approaches can support software architecture activities, such as architecture recovery, tactics identification, architectural smell detection, and others. However, as of today, it is difficult to have a clear view of existing research on MSR for software architecture. Objectives: The objective of this study is to identify, classify, and summarize the state-of-the-art MSR approaches applied to software architecture (MSR4SA). Methods: This study is designed according to the systematic mapping study research method. Specifically, out of 2442 potentially relevant studies, we systematically identify 151 primary studies where MSR approaches are applied to perform software architecture activities. Then, we rigorously extract relevant data from each primary study and synthesize the obtained results to produce a clear map of reasons for adopting MSR approaches to support architecting activities, used data sources, applied MSR techniques, and captured architectural information. Results: The major reasons to adopt MSR4SA techniques are about addressing industrial concerns like achieving quality attributes and minimizing practitioners' efforts. Most MSR4SA studies support architectural analysis, while architectural synthesis and evaluation are not commonly supported in MSR4SA studies. The most frequently mined data sources are source code repositories and issue trackers, which are also commonly mined together. Most of the MSR4SA studies apply more than one mining technique, where the most common MSR techniques are: (source code analysis, model analysis, statistical analysis), (machine learning, NLP). Architectural quality issues and components are the mostly mined type of information. Conclusion: Our results give a solid foundation for researchers and practitioners towards future research and applications of MSR approaches for software architecture.
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收藏
页数:18
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