Using improved genetic algorithm for software fault localization aided test case generation

被引：0
作者：
Yang B. [1 ,2 ,3 ]
He Y. [3 ]
Xu F. [1 ,2 ]
Chen Z. [1 ,2 ]
机构：
[1] School of Information, Beijing Forestry University, Beijing
[2] Engineering Research Center for Forestry Goriented Intelligent Information Processing, National Forestry and Grassland Administration, Beijing
[3] School of Information, North China of Science and Technology, Beijing
来源：
Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics | 2023年 / 49卷 / 09期
基金：
中国国家自然科学基金;
关键词：
fault localization; fitness function; genetic algorithm; random test; test case generation;
D O I：
10.13700/j.bh.1001-5965.2022.0524
中图分类号：
学科分类号：
摘要：
The ranking of suspected faults in the process of automatic software fault localization will be continuously created and is determined after the execution of existing test cases. Sometimes the program units corresponding to the fault are ranked lower in the ranking of suspected failures based on the existing test cases. If it is necessary to improve the suspected fault ranking of the program unit corresponding to the fault, supplementary test cases are a feasible method. This article suggests a technique for creating test cases based on a genetic algorithm that can make use of knowledge about the location of software faults. The paper analyzes and analyzes the methods used. Based on the joint experiment of the paper on 6 C programs and 2 Python programs, experimental results show that the test cases automatically generated by this method can effectively help improve the efficiency of fault location. © 2023 Beijing University of Aeronautics and Astronautics (BUAA). All rights reserved.
引用
收藏
页码：2279 / 2288
页数：9
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