Lazy Product Discovery in Huge Configuration Spaces
Highly-configurable software systems can have thousands of interdependent configuration options across different subsystems. In the resulting configuration space, discovering a valid product configuration for some selected options can be complex and error prone. The configuration space can be organized using a feature model, fragmented into smaller interdependent feature models reflecting the configuration options of each subsystem.
We propose a method for lazy product discovery in large fragmented feature models with interdependent features. We formalize the method and prove its soundness and completeness. The evaluation explores an industrial-size configuration space. The results show that lazy product discovery has significant performance benefits compared to standard product discovery, which in contrast to our method requires all fragments to be composed to analyze the feature model. Furthermore, the method succeeds when more efficient, heuristics-based engines fail to find a valid configuration.
Sat 11 JulDisplayed time zone: (UTC) Coordinated Universal Time change
16:05 - 17:05
|Lazy Product Discovery in Huge Configuration SpacesTechnical|
|Reducing Run-Time Adaptation Space via Analysis of Possible Utility BoundsTechnical|
|Exploring Differences and Commonalities between Feature Flags and Configuration OptionsSEIP|
Software Engineering in Practice
Jens Meinicke Carnegie Mellon University, Chu-Pan Wong Carnegie Mellon University, Bogdan Vasilescu Carnegie Mellon University, Christian Kästner Carnegie Mellon UniversityPre-print