Markov approach for quantifying the software code coverage using genetic algorithm in software testing

Boopathi, M.; Sujatha, R.; Kumar, C. Senthil; Narasimman, S.; Rajan, Arun Tejusve Raghunath

doi:10.1504/ijbic.2019.101152

Cited by 7 publications

(1 citation statement)

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“…Many problems in software engineering can be transformed into the combinatorial optimization problem. Thus, the genetic algorithm, which is simple and suitable for discrete problems, has been widely applied in software engineering [23][24][25][26][27][28]. Particularly, Mu [25] proposed a hybrid genetic algorithm-based strategy for software architecture re-modularization.…”

Section: Genetic Algorithmmentioning

confidence: 99%

A Genetic Algorithm-Based Approach for Composite Metamorphic Relations Construction

Xiang

2019

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The test oracle problem exists widely in modern complex software testing, and metamorphic testing (MT) has become a promising testing technique to alleviate this problem. The inference of efficient metamorphic relations (MRs) is the core problem of metamorphic testing. Studies have proven that the combination of simple metamorphic relations can construct more efficient metamorphic relations. In most previous studies, metamorphic relations have been mainly manually inferred by experts with professional knowledge, which is an inefficient technique and hinders the application. In this paper, a genetic algorithm-based approach is proposed to construct composite metamorphic relations automatically for the program to be tested. We use a set of relation sequences to represent a particular class of MRs and turn the problem of inferring composite MRs into a problem of searching for suitable sequences. We then dynamically implement multiple executions of the program and use a genetic algorithm to search for the optimal set of relation sequences. We conducted empirical studies to evaluate our approach using scientific functions in the GNU scientific library (abbreviated as GSL). From the empirical results, our approach can automatically infer high-quality composite MRs, on average, five times more than basic MRs. More importantly, the inferred composite MRs can increase the fault detection capabilities by at least 30 % more than the original metamorphic relations.

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