Discretized optimization algorithms for finding the bug-prone locations of a program source code
Küçük Resim Yok
Tarih
2024
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
The number of discovered bugs determines the efficacy of software test data. Software mutation testing is an important issue in software engineering since it is used to evaluate the effectiveness of test techniques. Syntactical changes are made to the program source code to generate buggy versions, which are then run alongside the original programs using test data. However, one of the key disadvantages of mutation testing is its high processing cost, which presents a difficult dilemma in the field of software engineering. The major goal of this study is to investigate the performance of different heuristic algorithms associated with mutation testing. According to the 80%–20 rule, 80% of a program's bugs are detected in only 20% of its bug-prone code. Different heuristic algorithms have been proposed to find out the bug-prone and sensitive locations of a program source code. Next, mutants are only put into the identified bug-prone instructions and data. This technique guarantees that mutation operators are only injected into code portions that are prone to bugs. Experimental evaluation on typical benchmark programs shows the effectiveness of different heuristic algorithms in reducing the number of generated mutants. A decrease in the number of created mutants reduces the total cost of mutation testing. Another feature of the heuristic-based mutation testing technique is its independence from platform and testing tool. Experimental findings show that using the heuristic strategy in different testing tools such as Mujava, Muclipse, Jester, and Jumble results in a considerable reduction of mutations created during testing. © 2024 Elsevier Inc. All rights reserved.
Açıklama
Anahtar Kelimeler
Automatic Test Data Generation, Branch Coverage, Heuristic Algorithms, Software Testing, Success Rate
Kaynak
Decision-Making Models: A Perspective of Fuzzy Logic and Machine Learning
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Scopus Q Değeri
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Künye
Arasteh, B., Sefati, S. S., Shami, S., & Abdollahian, M. (2024). Discretized optimization algorithms for finding the bug-prone locations of a program source code. In Decision-Making Models (pp. 125-137). Academic Press.
