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Software Development Data for Architecture Analysis: Expectations, Reality, and Future Directions
Conference proceeding

Software Development Data for Architecture Analysis: Expectations, Reality, and Future Directions

Yuanfang Cai, Rick Kazman and IEEE COMP SOC
2020 IEEE/ACM 42nd International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP)
Oct 2020
DOI: https://doi.org/10.1145/3377813.3381357

Abstract

Architecture Debt Computer architecture Software architecture Software Modularity Software Quality Technical Debt
Recently we have worked with a dozen industrial collaborators to pinpoint and quantify architecture debts, from multi-national corporations to startup companies. Our technology leverages a wide range of project data, from source file dependencies to issue records, and we interacted with projects of various sizes and characteristics. Crossing the border between research and practice, we have observed significant gaps in terms of data availability and quality among projects of different kinds. Compared with successful open source projects, data from proprietary projects are rarely complete or well-organized. Consequently, not all projects can benefit from all the features and analyses we provide. This, in turn, made them realize they needed to improve their development processes. In this talk, we categorize the commonly observed differences between open source and proprietary project data, analyze the reasons for such differences, and propose suggestions to minimize the gaps, to facilitate advances to both software research and practice.

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13 Record Views
1 citations in Web of Science
2 citations in Scopus

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Collaboration types
Domestic collaboration
Web of Science research areas
Computer Science, Software Engineering
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