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Mining architectural information: A systematic mapping study

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Abstract

Mining Software Repositories (MSR) has become an essential activity in software development. Mining architectural information (e.g., architectural models) to support architecting activities, such as architecture understanding, has received significant attention in recent years. However, there is a lack of clarity on what literature on mining architectural information is available. Consequently, this may create difficulty for practitioners to understand and adopt the state-of-the-art research results, such as what approaches should be adopted to mine what architectural information in order to support architecting activities. It also hinders researchers from being aware of the challenges and remedies for the identified research gaps. We aim to identify, analyze, and synthesize the literature on mining architectural information in software repositories in terms of architectural information and sources mined, architecting activities supported, approaches and tools used, and challenges faced. A Systematic Mapping Study (SMS) has been conducted on the literature published between January 2006 and December 2022. Of the 104 primary studies finally selected, 7 categories of architectural information have been mined, among which architectural description is the most mined architectural information; 11 categories of sources have been leveraged for mining architectural information, among which version control system (e.g., GitHub) is the most popular source; 11 architecting activities can be supported by the mined architectural information, among which architecture understanding is the most supported activity; 95 approaches and 56 tools were proposed and employed in mining architectural information; and 4 types of challenges in mining architectural information were identified. This SMS provides researchers with promising future directions and help practitioners be aware of what approaches and tools can be used to mine what architectural information from what sources to support various architecting activities.

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Data Availibility Statement

The Supplementary Material of the current study is available in the Zenodo repository at de Dieu et al. (2024).

Notes

  1. https://www.maxqda.com/

  2. https://essere.disco.unimib.it/wiki/arcan/

  3. https://depfind.sourceforge.io/

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Acknowledgements

This work is partially sponsored by the National Natural Science Foundation of China (NSFC) under Grant No. 62172311 and 62176099, the Special Fund of Hubei Luojia Laboratory, the financial support from the China Scholarship Council, Shenzhen Polytechnic University with Grant No. 6022312043K, and State Key Laboratory for Novel Software Technology at Nanjing University with Grant No. KFKT2022B37.

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Authors and Affiliations

  1. School of Computer Science, Wuhan University, Wuhan, China

    Musengamana Jean de Dieu & Peng Liang

  2. Hubei Luojia Laboratory, Wuhan, China

    Musengamana Jean de Dieu & Peng Liang

  3. School of Computing Technologies, RMIT University, Melbourne, Australia

    Mojtaba Shahin

  4. School of Artificial Intelligence, Shenzhen Polytechnic University, Shenzhen, China

    Chen Yang

  5. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China

    Chen Yang

  6. School of Computer Science & Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan, China

    Zengyang Li

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  1. Musengamana Jean de Dieu
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Correspondence to Peng Liang.

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Jean de Dieu, M., Liang, P., Shahin, M. et al. Mining architectural information: A systematic mapping study. Empir Software Eng 29, 79 (2024). https://doi.org/10.1007/s10664-024-10480-6

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