计算机与现代化

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基于遗传神经网络的软件错误定位方法

  

  1. (平顶山学院软件学院,河南平顶山467000)
  • 收稿日期:2015-04-03 出版日期:2015-11-12 发布日期:2015-11-16
  • 作者简介:作者简介:吕琼帅(1985-),男,河南平顶山人,平顶山学院软件学院讲师,硕士,研究方向:机器学习,智能算法; 单冬红(1976-),女,副教授,硕士,研究方向:软件工程,网络安全; 申远(1983-),男,讲师,硕士,研究方向:信息安全及密码学,软件工程。
  • 基金资助:
    河南省科技厅项目(132102310516); 平顶山学院青年基金重点项目(PDSU-QNJJ-2013002)

Software Fault Localization Based on Genetic Neural Network

  1. (Software College, Pingdingshan University, Pingdingshan 467000, China)
  • Received:2015-04-03 Online:2015-11-12 Published:2015-11-16

摘要: 软件错误定位的准确率有助于提高软件调试的效率。通过对软件运行时信息的分析,提出一种利用遗传神经网络来提高软件错误定位准确率的方法。首先,采集程序运行过程中的相关信息并将其编码,将此编码作为遗传神经网络的训练数据集;然后,构建虚拟测试集,并根据虚拟测试集计算程序语句的可疑度,再根据可疑度的大小来定位软件错误;最后,利用Siemens Suite中132个预先植入错误的程序进行实验。实验结果表明,遗传神经网络能够在一定程度上提高软件错误定位的准确率,对软件调试工作起到一定的帮助作用。

关键词: 错误定位, 遗传神经网络, 可疑度, 软件调试

Abstract: Software fault localization accuracy helps to improve software debugging efficiency. According to the analysis of software runtime information, a method is proposed to improve the accuracy of software error locating by using genetic neural network. First, the information is collected and encoded while the program is running. The encoding is considered as a genetic neural network training data set. Then, building a virtual test set, the program statement suspicious degree is calculated according to the virtual test set and the software error is positioned according to the size of the suspicious. Finally, this paper conducts on the Siemens Suite of 132 programs with injected bugs. The experimental results show that the genetic neural network can improve the accuracy of locating software faults to some extent and is helpful for software debugging.

Key words: fault localization, genetic neural network, suspicious degree, software debugging

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