|Authors||A. Hervieu, D. Marijan, A. Gotlieb and B. Baudry|
|Title||Practical Minimization of Pairwise-Covering Test Configurations Using Constraint Programming|
|Project(s)||The Certus Centre (SFI)|
|Publication Type||Journal Article|
|Year of Publication||2016|
|Journal||Information and Software Technology|
Context: Testing highly-configurable software systems is challenging due to a large number of test configurations that have to be carefully selected in order to reduce the testing effort as much as possible, while maintaining high software quality. Finding the smallest set of valid test configurations that ensure sufficient coverage of the system's feature interactions is thus the objective of validation engineers, especially when the execution of test configurations is costly or time-consuming. However, this problem is NP-hard in general and approximation algorithms have often been used to address it in practice.
Objective: In this paper, we explore an alternative exact approach based on constraint programming that will allow engineers to increase the effectiveness of configuration testing while keeping the number of configurations as low as possible.
Method: Our approach consists in using a (time-aware) minimization algorithm based on constraint programming. Given the amount of time, our solution generates a minimized set of valid test configurations that ensure coverage of all pairs of feature values (a.k.a. pairwise coverage). The approach has been implemented in a tool called PACOGEN.
Results: PACOGEN was evaluated on 224 feature models from the standard benchmark repository SPLOT, and compared
Conclusion: Our experimental evaluation concluded that optimal time-aware minimization of pairwise-covering test configurations is efficiently addressed using constraint programming techniques.