|Authors||H. Lu, T. Yue, S. Ali and L. Zhang|
|Title||Nonconformity Resolving Recommendations for Product Line Configuration|
|Project(s)||The Certus Centre (SFI), Zen-Configurator: Interactive and Optimal Configuration of Cyber Physical System Product Lines|
|Publication Type||Proceedings, refereed|
|Year of Publication||2016|
|Conference Name||IEEE International Conference on Software Testing, Verification and Validation (ICST)|
In the context of large-scale system product line engineering, manual configuration is often mandatory and therefore inevitably introduces nonconformities: violating pre-defined constraints for conformance checking. Resolving nonconformities without proper tool support is more or less random, as there are usually hundreds and thousands of configurable parameters and conformance constraints, in the context of configuring a large-scale and directly deployable system. Moreover, inter-connections among constraints and configurable parameters worsen the feasibility of manual resolving nonconformities without proper tool support. In this paper, we present an automatic approach (named as Zen-FIX) to optimally recommend solutions to resolve nonconformities by combining multi-objective search and constraint solving techniques. Solutions recommended by Zen-FIX conform to all pre-defined constraints and are optimal in terms of maximizing the overall efficiency of an interactive product configuration process. We evaluated Zen-FIX with a real- world case study containing 52454 optimization problems, with which we evaluated seven multi-objective search algorithms. Results show that MoCell significantly outperformed all the others: CellDE, IBEA, NSGA-II, PESA2, Random, SPEA2, for most of the problems, in terms of Efficiency (a combined metric of finding optimal solutions and time performance).