NDLI: Finding the Optimal Balance between Over and Under Approximation of Models Inferred from Execution Logs

Content Provider	IEEE Xplore Digital Library
Author	Tonella, P. Marchetto, A. Nguyen, C.D. Yue Jia Lakhotia, K. Harman, M.
Copyright Year	2012
Abstract	Models inferred from execution traces (logs) may admit more behaviours than those possible in the real system (over-approximation) or may exclude behaviours that can indeed occur in the real system (under-approximation). Both problems negatively affect model based testing. In fact, over-approximation results in infeasible test cases, i.e., test cases that cannot be activated by any input data. Under-approximation results in missing test cases, i.e., system behaviours that are not represented in the model are also never tested. In this paper we balance over- and under-approximation of inferred models by resorting to multi-objective optimization achieved by means of two search-based algorithms: A multi-objective Genetic Algorithm (GA) and the NSGA-II. We report the results on two open-source web applications and compare the multi-objective optimization to the state-of-the-art KLFA tool. We show that it is possible to identify regions in the Pareto front that contain models which violate fewer application constraints and have a higher bug detection ratio. The Pareto fronts generated by the multi-objective GA contain a region where models violate on average 2% of an application's constraints, compared to 2.8% for NSGA-II and 28.3% for the KLFA models. Similarly, it is possible to identify a region on the Pareto front where the multi-objective GA inferred models have an average bug detection ratio of 110 : 3 and the NSGA-II inferred models have an average bug detection ratio of 101 : 6. This compares to a bug detection ratio of 310928 : 13 for the KLFA tool.
Starting Page	21
Ending Page	30
File Size	392624
Page Count	10
File Format	PDF
ISBN	9781457719066
e-ISBN	9780769546704
DOI	10.1109/ICST.2012.82
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-04-17
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Measurement Computational modeling Model-based testing Model inference Inference algorithms Approximation methods Optimization Genetic algorithms Testing Search-based software engineering
Content Type	Text
Resource Type	Article

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