Description
Feature models are a de facto standard for representing the commonalities and variability of product lines and configurable software systems. Requirements-level features are commonly implemented in multiple source code artifacts, which results in complex dependencies at the code level. As developers change and evolve features frequently, it is challenging to keep feature models consistent with their implementation. We thus present an approach combining feature-to-code mappings and code dependency analyses to inform engineers about possible inconsistencies. Our focus is on code-level changes requiring updates in feature dependencies and constraints. Our approach uses static code analysis and a variation control system to lift complex code-level dependencies to feature models. We present the suggested dependencies to the engineer in two ways: directly as links between features in a feature model and as a heatmap visualizing the dependency changes of all features in a model. We present results of an evaluation on the Pick-and-Place Unit system, which demonstrates the utility and performance of our approach and the quality of the suggestions.Period | 22 Oct 2019 |
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Event title | Proceedings of the 18th International Conference on Generative Programming: Concepts & Experiences (GPCE) |
Event type | Conference |
Location | GreeceShow on map |
Fields of science
- 102 Computer Sciences
- 102022 Software development
- 102025 Distributed systems
JKU Focus areas
- Digital Transformation
Documents & Links
Related content
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Projects
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Christian Doppler Labor für Monitoring and Evolution of Very-Large-Scale Software Systems
Project: Funded research › Other mainly public funds
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Multi-Modeling and Evolution in Software Ecosystems (M02)
Project: Funded research › Other sponsors