Assessing the Effectiveness of Coverage-Based Fault Localizations Using Mutants

Author:

Xue Xiaozhen1,Siami-Namini Sima2,Namin Akbar Siami1

Affiliation:

1. Department of Computer Science, Texas Tech University, Lubbock, TX, USA

2. Department of Applied Economics, Texas Tech University, Lubbock, TX, USA

Abstract

Empirical studies show that coverage-based fault localizations are very effective in testing and debugging software applications. It is also a commonly held belief that no software testing techniques would perform best for all programs with various data structures and complexity. An important research question posed in this paper is whether the type and complexity of faults in a given program has any influence on the performance of these fault localization techniques. This paper investigates the performance of coverage-based fault localizations for different types of faults. We explore and compare the accuracy of these techniques for two large groups of faults often observed in object-oriented programs. First, we explore different types of traditional method-level faults grouped into six categories including those related to arithmetic, relational, conditional, logical, assignment, and shift. We then focus on class-level faults related to object-oriented features and group them into four categories including inheritance, overriding, Java-specific features, and common programming mistakes. The results show that coverage-based fault localizations are less effective for class-level faults associated with object-oriented features of programs. We therefore advocate the needs for designing more effective fault localizations for debugging object-oriented and class-level defects.

Publisher

World Scientific Pub Co Pte Lt

Subject

Artificial Intelligence,Computer Graphics and Computer-Aided Design,Computer Networks and Communications,Software

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. An Empirical Study on Higher-Order Mutation-Based Fault Localization;International Journal of Software Engineering and Knowledge Engineering;2022-01

2. TestLocal;Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing;2019-04-08

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3