Highly-configurable software systems can be easily adapted to address user's needs. Modelling parameter configurations and their relationships can facilitate software reuse. Combinatorial Interaction Testing (CIT) methods are already often used to drive systematic testing of software system configurations. However, a model of the system's configurations not conforming with respect to its software implementation, must be repaired in order to restore conformance. In this paper we extend CIT by devising a new search-based technique able to repair a model composed of a set of constraints among the various software system's parameters. Our technique can be used to detect and fix faults both in the model and in the real software system. Experiments for five real-world systems show that our approach can repair on average 37% of conformance faults. Moreover, we also show it can infer parameter constraints in a large real-world software system, hence it can be used for automated creation of CIT models.

(2017). Combinatorial Interaction Testing for Automated Constraint Repair . Retrieved from http://hdl.handle.net/10446/108341

Combinatorial Interaction Testing for Automated Constraint Repair

Gargantini, Angelo Michele;Petke, Justyna;Radavelli, Marco
2017-01-01

Abstract

Highly-configurable software systems can be easily adapted to address user's needs. Modelling parameter configurations and their relationships can facilitate software reuse. Combinatorial Interaction Testing (CIT) methods are already often used to drive systematic testing of software system configurations. However, a model of the system's configurations not conforming with respect to its software implementation, must be repaired in order to restore conformance. In this paper we extend CIT by devising a new search-based technique able to repair a model composed of a set of constraints among the various software system's parameters. Our technique can be used to detect and fix faults both in the model and in the real software system. Experiments for five real-world systems show that our approach can repair on average 37% of conformance faults. Moreover, we also show it can infer parameter constraints in a large real-world software system, hence it can be used for automated creation of CIT models.
2017
Gargantini, Angelo Michele; Petke, Justyna; Radavelli, Marco
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10446/108341
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