Bio‐inspired optimization to support the test data generation of concurrent software

Summary Concurrent programming is increasingly present in modern applications. Although it provides higher performance and better use of available resources, the mechanisms of interaction between processes/threads result in a greater challenge for software testing activity. The nondeterminism presen...

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Vydáno v:Concurrency and computation Ročník 35; číslo 2
Hlavní autoři: Ferreira Vilela, Ricardo, Choma Neto, João, Santiago Costa Pinto, Victor Hugo, Lopes de Souza, Paulo Sérgio, do Rocio Senger de Souza, Simone
Médium: Journal Article
Jazyk:angličtina
Vydáno: Hoboken Wiley Subscription Services, Inc 25.01.2023
Témata:
Concurrent processing
concurrent software testing
Fuzzy logic
Genetic algorithms
Optimization
Optimization techniques
search‐based software testing
Software
Software reliability
Software testing
test data generation
ISSN:1532-0626, 1532-0634
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Shrnutí:Summary Concurrent programming is increasingly present in modern applications. Although it provides higher performance and better use of available resources, the mechanisms of interaction between processes/threads result in a greater challenge for software testing activity. The nondeterminism present in those applications is one of the main issues during the test activity since the same test input can produce different possible execution paths, which may or not contain defects. The test data automatic generation can alleviate this problem, ensuring higher speed and reliability in software testing activity. This paper explores the automatic test data generation for concurrent programs through Genetic Algorithm, a bioinspired optimization technique, and proposes a test data generation approach for concurrent programs, called BioConcST, and a new operator for the selection of test subjects, called FuzzyST, which uses fuzzy logic. The approaches were evaluated in an experimental study towards their validation. The results showed that BioConcST is more promising than the other approaches at all analyzed levels. FuzzyST, together with Elitism and Tournament operators, provided the best results; however, it proved more suitable for concurrent programs of higher complexity.
Bibliografie:Funding information
Conselho Nacional de Desenvolvimento Científico e Tecnológico
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ISSN:1532-0626
1532-0634
DOI:10.1002/cpe.7489