For the path coverage testing of a Message-Passing Interface (MPI) program, test data generation based on an evolutionary optimization algorithm (EOA) has been widely known. However, during the use of the above techni...
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For the path coverage testing of a Message-Passing Interface (MPI) program, test data generation based on an evolutionary optimization algorithm (EOA) has been widely known. However, during the use of the above technique, it is necessary to evaluate the fitness of each evolutionary individual by executing the program, which is generally computationally expensive. In order to reduce the computational cost, this article proposes a method of integrating an ensemble surrogate model's estimation into the process of generating test data. The proposed method first produces a number of test inputs using an EOA, and forms a training set together with their real fitness. Then, this article trains an ensemble surrogate model (ESM) based on the training set, which is employed to estimate the fitness of each individual. Finally, a small number of individuals with good estimations are selected to further execute the program, so as to have their real fitness for the subsequent evolution. This article applies the proposed method to seven benchmark MPI programs, which is compared with several state-of-the-art approaches. The experimental results show that the proposed method can generate test data with significantly low computational cost.
This paper presents a new parallel algorithm for backward symbolic execution. We use a program modeling allowing an easy distributed symbolic execution and a scalable program testing. A program is divided into several...
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ISBN:
(纸本)9783642396434
This paper presents a new parallel algorithm for backward symbolic execution. We use a program modeling allowing an easy distributed symbolic execution and a scalable program testing. A program is divided into several parts assigned to different nodes. A particular node: the Coordinator allocates tasks to workers and collects final results.
Automatic test data generation for pathcoverage is an undecidable problem and genetic algorithm (GA) has been used as one good solution. This paper presents a method for optimizing GA efficiency by identifying the mo...
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ISBN:
(纸本)9781509061440
Automatic test data generation for pathcoverage is an undecidable problem and genetic algorithm (GA) has been used as one good solution. This paper presents a method for optimizing GA efficiency by identifying the most critical path clusters in a program under test. We do this by using the static program analysis to find all the paths having the path conditions with low probability in generating coverage data, then basing on these path conditions to adjust the procedure of generating new populations in GA. The proposed approach is also applied some program under tests. Experimental results show that improved GA which can generate suitable test data has higher pathcoverage than the traditional GA.
In the field of message-passing interface (MPI) program pathcoverage test case generation, evolutionary algorithms (EAs) have been frequently utilized to generate test cases. However, relying solely on EAs will incur...
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In the field of message-passing interface (MPI) program pathcoverage test case generation, evolutionary algorithms (EAs) have been frequently utilized to generate test cases. However, relying solely on EAs will incur excessive computational costs. In this article, we improve the efficiency and quality of MPI program pathcoverage test cases generated by EAs based on elite individual selection. First, data within the data domain is sampled and fitness is calculated to form a shared set. Then, the population data is initialized using EAs, and the fitness of individuals is predicted using the neighbor value sharing algorithm (NVSA). Subsequently, individuals are ranked using rank-based elite selection (RES). Finally, elite individuals are chosen through ranking to run the program and verify the generation of test cases. In order to reduce computational costs, data dimensionality reduction operations are added to the above process. We demonstrate that the proposed method can effectively generate test data and reduce test costs by comparing it with several excellent methods on seven representative MPI programs. Among them, NVSA has a maximum improvement of 42.2%, RES has a maximum improvement of 31.5%, dimensionality reduction can increase by 20.2%, and the overall method has a maximum improvement of 47.4%.
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