Abstract
Software Product Lines (SPLs) are a common technique to capture families of software products in terms of commonalities and variabilities. On a conceptual level, functionality of an SPL is modeled in terms of features in Feature Models (FMs). As other software systems, SPLs and their FMs are subject to evolution that may lead to the introduction of anomalies (e.g., non-selectable features). To fix such anomalies, developers need to understand the cause for them. However, for large evolution histories and large SPLs, explanations may become very long and, as a consequence, hard to understand. In this paper, we present a method for anomaly detection and explanation that, by encoding the entire evolution history, identifies the evolution step of anomaly introduction and explains which of the performed evolution operations lead to it. In our evaluation, we show that our method significantly reduces the complexity of generated explanations.
Original language | English |
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Title of host publication | GPCE 2018 - Proceedings of the 17th ACM SIGPLAN International Conference on Generative Programming : Concepts and Experiences, co-located with SPLASH 2018 |
Editors | Eric Van Wyk, Tiark Rompf |
Number of pages | 14 |
Publisher | Association for Computing Machinery |
Publication date | Nov 2018 |
Pages | 188-201 |
ISBN (Electronic) | 9781450360456 |
DOIs | |
Publication status | Published - Nov 2018 |
Event | 17th ACM SIGPLAN International Conference on Generative Programming: Concepts and Experiences, GPCE 2018, co-located with SPLASH 2018 - Boston, United States Duration: 5. Nov 2018 → 6. Nov 2018 |
Conference
Conference | 17th ACM SIGPLAN International Conference on Generative Programming: Concepts and Experiences, GPCE 2018, co-located with SPLASH 2018 |
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Country/Territory | United States |
City | Boston |
Period | 05/11/2018 → 06/11/2018 |
Sponsor | ACM SIGPLAN, itemis AG, Raincode Labs |
Keywords
- Anomalies
- Evolution
- Evolution Operation
- Explanation
- Feature Model
- Software Product Line