.png){kind=icon}
返回
Basis path coverage testing of MPI programs based on multi-task evolutionary optimization
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2024.124557.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
A M essage - P assing I nterface (MPI) program usually consists of several processes, and a target path of this program is composed of a target sub -path selected in each process. However, there may be coverage conflict between different target sub -paths in the target path, and there is currently no effective approach to avoid this problem. Therefore, this paper proposes an approach of basis path coverage testing of MPI programs based on multi -task evolutionary optimization, which is used to generate a test case set that meets the criteria of branch and sentence coverage. Firstly, the proposed approach samples a certain number of representative test inputs, and calculates the coverage level of each test input for each target sub -path in the target path, which is used to group the target sub -paths with coverage conflict. Then, we construct an optimization model for each group of the target sub -paths, which can guide test case generation with high effectiveness. Finally, each optimization model is solved by an evolutionary algorithm, and the efficiency of solving these optimization models is improved based on a multi -task framework. The proposed approach is applied to basis path coverage testing of seven benchmark MPI programs under test, and compared with several state-of-the-art approaches. Experimental results indicate that the proposed approach has significant advantages in test case generation, as well as performs good scalability.
Keyword:
Multi-task framework
Evolutionary optimization
Test case generation
Basis path coverage testing
MPI programs
期刊
IF:
7.5
论文数:
3.0W
被引数:
10.2W
机构
引用论文
TEMPORAL PATTERNS OF PARENTAL RESOURCE DISTRIBUTION IN THE RED-NECKED GREBE: EQUALIZING THE SHARE OF THE SURVIVORS.png)
Behaviour
BehaviourIF0
PREAI
分享收藏LEFSA: label enhancement-based feature selection with adaptive neighborhood via ant colony optimization for multilabel learningLEFSA: 通过蚁群优化的多标签学习的基于标签增强的自适应邻域特征选择
PREAI
分享收藏Genetic algorithm based test data generation for MPI parallel programs with blocking communication基于遗传算法的MPI并行程序阻塞通信测试数据生成
PREAI
分享收藏Performance analysis of six meta-heuristic algorithms over automated test suite generation for path coverage-based optimizationSOFT COMPUTING
SOFT COMPUTINGIF2.5
PREAI
分享收藏
查看更多内容