WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7150

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Author: search.filters.author.Ayav, TolgaSubject: search.filters.subject.Mutation analysis

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Now showing 1 - 3 of 3
  • Article
    Citation - WoS: 2
    Citation - Scopus: 3
    Mutation-Based Minimal Test Suite Generation for Boolean Expressions
    (World Scientific Publishing, 2023) Ayav, Tolga; Belli, Fevzi
    Boolean expressions are highly involved in control flows of programs and software specifications. Coverage criteria for Boolean expressions aim at producing minimal test suites to detect software faults. There exist various testing criteria, efficiency of which is usually evaluated through mutation analysis. This paper proposes an integer programming-based minimal test suite generation technique relying on mutation analysis. The proposed technique also takes into account the cost of fault detection. The technique is optimal such that the resulting test suite guarantees to detect all the mutants under given fault assumptions, while maximizing the average percentage of fault detection of a test suite. Therefore, the approach presented can also be considered as a reference method to check the efficiency of any common technique. The method is evaluated using four well-known real benchmark sets of Boolean expressions and is also exemplary compared with MCDC criterion. The results show that the test suites generated by the proposed method provide better fault coverage values and faster fault detection.
  • Article
    Citation - WoS: 2
    Citation - Scopus: 1
    Mutant Selection by Using Fourier Expansion
    (Türkiye Klinikleri Journal of Medical Sciences, 2020) Takan, Savaş; Ayav, Tolga
    Mutation analysis is a widely used technique to evaluate the effectiveness of test cases in both hardware and software testing. The original model is mutated systematically under certain fault assumptions and test cases are checked against the mutants created to see whether the test cases can detect the faults or not. Mutation analysis is usually a computationally intensive task, particularly in finite state machine (FSM) testing due to a possibly huge amount of mutants. Random selection could be a practical reduction method under the assumption that each mutant is identical in terms of the probability of occurrence of its associating fault. The present study proposes a mutant selection method based on Fourier analysis of Boolean functions. Fourier helps to identify the most effective transitions on the output so that the mutants related to those transitions can be selected. Such mutants are considered more important since they are more likely to be killed. To evaluate the method, test cases are generated by the well-known W method, which has the capability of detecting every potential fault. The original and reduced sets of mutants are compared with respect to their importance values. Evaluations show that the mutants selected by the proposed technique are more effective, which reduces the cost of mutation analysis without sacrificing the performance of the mutation analysis.
  • Article
    Citation - WoS: 4
    Citation - Scopus: 8
    Prioritizing Mcdc Test Cases by Spectral Analysis of Boolean Functions
    (John Wiley and Sons Inc., 2017) Ayav, Tolga
    Test case prioritization aims at scheduling test cases in an order that improves some performance goal. One performance goal is a measure of how quickly faults are detected. Such prioritization can be performed by exploiting the Fault Exposing Potential (FEP) parameters associated to the test cases. FEP is usually approximated by mutation analysis under certain fault assumptions. Although this technique is effective, it could be relatively expensive compared to the other prioritization techniques. This study proposes a cost-effective FEP approximation for prioritizing Modified Condition Decision Coverage (MCDC) test cases. A strict negative correlation between the FEP of a MCDC test case and the influence value of the associated input condition allows to order the test cases easily without the need of an extensive mutation analysis. The method is entirely based on mathematics and it provides useful insight into how spectral analysis of Boolean functions can benefit software testing.