Book chapter
Constraint-based heuristic algorithms for software test generation
Decision-Making Models: A Perspective of Fuzzy Logic and Machine Learning, pp.111-123
Elsevier Inc
2024
Metrics
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Abstract
While software testing is essential for enhancing a software system's quality, it can be time-consuming and costly during developing software. Automation of software testing can help solve this problem, streamlining time-consuming testing tasks. However, generating automated test data that maximally covers program branches is a complex optimization problem referred to as NP-complete and should be addressed appropriately. Although a variety of heuristic algorithms have already been suggested to create test suites with the greatest coverage, they have issues such as insufficient branch coverage, low rate of success in generating test data with high coverage, and unstable results. The main objective of the current chapter is to investigate and compare the coverage, success rate (SR), and stability of various heuristic algorithms in software structural test generation. To achieve this, the effectiveness of seven algorithms, genetic algorithm (GA), simulated annealing (SA), ant colony optimizer (ACO), particle swarm optimizer (PSO), artificial bee colony (ABC), shuffle frog leaping algorithm (SFLA), and imperialist competitive algorithm (ICA), are examined in automatically generating test data, and their performance is compared on the basis of various criteria. The experiment results demonstrate the superiority of the SFLA, ABC, and ICA to other examined algorithms. Overall, SFLA outperforms all other algorithms in coverage, SR, and stability.
Details
- Title
- Constraint-based heuristic algorithms for software test generation
- Creators
- Bahman Arasteh - Istinye UniversityBabak Aghaei - Islamic Azad UniversityReza Ghanbarzadeh - Southern Cross UniversityReza Kalan - Research and Development Department, Digiturk Bein Media Group, Istanbul, Turkey
- Contributors
- Tofigh Allahviranloo (Editor) - Istinye UniversityWitold Pedrycz (Editor) - University of AlbertaAmir Seyyedabbasi (Editor) - Istinye University
- Publication Details
- Decision-Making Models: A Perspective of Fuzzy Logic and Machine Learning, pp.111-123
- Publisher
- Elsevier Inc
- Identifiers
- 991013211713102368
- Copyright
- Copyright © 2024 Elsevier Inc. All rights are reserved including those for text and data mining AI training and similar technologies.
- Academic Unit
- Information Technology; Faculty of Science and Engineering
- Language
- English
- Resource Type
- Book chapter