A Survey on Mutation Testing Approaches

Kontar, Karam Al; Naji, Joumana; Demiane, Freddy; Sobeh, Salma; Haraty, Ramzi A.

doi:10.1109/chilecon47746.2019.8987448

Cited by 2 publications

(1 citation statement)

References 31 publications

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“…But, these introduced methods provide better performance in error identification. 13,14 The process of mutation investigation is systematically a simple method in the case of other test schemes. The testing tool of mutation analysis provide the ability to check various test methods and helps for mutation testing.…”

Section: Introductionmentioning

confidence: 99%

Equivalent mutant identification using hybrid wavelet convolutional rain optimization

Jammalamadaka

Parveen

2021

Softw Pract Exp

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Mutation testing is a significant software testing approach that identifies the faults present in the source codes and the potentiality of the test cases in detecting the mutated codes. In this testing approach, equivalent mutant detection is complicated as the test cases will not identify the mutated codes from the source program. To overcome this problem, the article proposes a novel hybrid strategy known as the hybrid wavelet convolutional rain optimization (HWCRO) to classify the equivalent mutants present in the source codes accurately. The proposed technique considers three different classes of equivalent mutants based on the RIPR model and exactly identifies the mutated code. Initially, the features such as the semantic similarity and the information entropy are extracted, and these features are given as the input to the wavelet convolutional neural network (wCNN) classifier. The dimensions of the features are reduced in the convolutional layers using the wavelet function, which enhances the classifier's performance. To improve the classification accuracy, the loss function is minimized with an adaptive rain optimization algorithm (ROA) that iteratively tunes the parameter of wCNN. The proposed approach is compared with the existing classification techniques based on the parameters such as precision, recall, f1-score, and accuracy, and the simulation results yielded 85.17% accuracy value for the proposed approach.

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Section: Introductionmentioning

confidence: 99%

Equivalent mutant identification using hybrid wavelet convolutional rain optimization

Jammalamadaka

Parveen

2021

Softw Pract Exp

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HyperPUT: generating synthetic faulty programs to challenge bug-finding tools

Felici,

Pozzi,

Furia

2024

Empir Software Eng

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As research in automatically detecting bugs grows and produces new techniques, having suitable collections of programs with known bugs becomes crucial to reliably and meaningfully compare the effectiveness of these techniques. Most of the existing approaches rely on benchmarks collecting manually curated real-world bugs, or synthetic bugs seeded into real-world programs. Using real-world programs entails that extending the existing benchmarks or creating new ones remains a complex time-consuming task. In this paper, we propose a complementary approach that automatically generates programs with seeded bugs. Our technique, called HyperPUT, builds C programs from a “seed” bug by incrementally applying program transformations (introducing programming constructs such as conditionals, loops, etc.) until a program of the desired size is generated. In our experimental evaluation, we demonstrate how HyperPUT can generate buggy programs that can challenge in different ways the capabilities of modern bug-finding tools, and some of whose characteristics are comparable to those of bugs in existing benchmarks. These results suggest that HyperPUT can be a useful tool to support further research in bug-finding techniques—in particular their empirical evaluation.

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A Survey on Mutation Testing Approaches

Cited by 2 publications

References 31 publications

Equivalent mutant identification using hybrid wavelet convolutional rain optimization

Equivalent mutant identification using hybrid wavelet convolutional rain optimization

HyperPUT: generating synthetic faulty programs to challenge bug-finding tools

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