Abstract
Concurrent programs are being widely adopted in development of multi-core and many-core processors. However, these types of programs present some features such as concurrency, communication and synchronization which make their testing more challenging than sequential programs. Search-based techniques, which use meta-heuristic search algorithms, have frequently been used for testing sequential programs, especially in the test data generation activity. However, application of search-based techniques in test data generation for concurrent programs has seldom been covered in the literature. The first contribution of this paper is to presenta search-based test data generation framework for concurrent programs. Additionally, a hybrid meta-heuristic algorithm, called SFLA-VND, is proposed, which could be used in the mentioned framework as well as other meta-heuristic algorithms. SFLA-VND is a combination of the shuffled frog leaping algorithm (SFLA) and the variable neighborhood descent (VND). The proposed framework has been experimented on five concurrent benchmark programs by applying genetic algorithm (GA), ant colony optimization (ACO), particle swarm optimization (PSO), SFLA and SFLA-VND. Experimental results demonstrate the effectiveness and efficiency of this framework. Also, the results confirm the superiority of SFLA-VND in comparison with some popular meta-heuristic algorithms, when they are used for test data generation.
Highlights
The concurrent programming style is widely used in modern programs to improve the performance and decrease the computation time of the programs [1]
In order to draw conclusions in much higher confidence, the ANOVA test was conducted on two metrics of the average coverage (AC) and average time (AT)
We found the value of the AC obtained by each competitive algorithm exactly when the shuffled frog leaping algorithm (SFLA)-variable neighborhood descent (VND) algorithm reached the highest level of coverage
Summary
The concurrent programming style is widely used in modern programs to improve the performance and decrease the computation time of the programs [1]. Such adoption of concurrent systems calls for new challenges, as it is important that these systems work correctly. Test data generation is an important activity in software testing because effectiveness and efficiency of the test is highly dependent to the number and quality of the test data. Various methods, such as search-based techniques, have been used for test data generation in sequential programs. In Prado et al [11], the method of converting a multithreaded concurrent program to the PCFG graph has been described
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