Nonconformity Resolving Recommendations for Product Line Configuration

Abstract

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 using multi-objective search. Solutions recommended by Zen-FIX conform to all pre-defined constraints and are optimized 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 outperformed all others: CellDE, IBEA, NSGA-II, PESA2, Random, SPEA2, for most of the problems, in terms of Efficiency (a combined metric of finding optimized solutions and time performance).