中国科学院软件研究所机构知识库
Advanced  
ISCAS OpenIR  > 软件所图书馆  > 会议论文
Title:
wielding statistical fault localization statistically
Author: Zhang Yunqian ; Chen Lin ; Jiang Bo ; Zhang Zhenyu
Source: Proceedings - 23rd IEEE International Symposium on Software Reliability Engineering Workshops, ISSREW 2012
Conference Name: 23rd IEEE International Symposium on Software Reliability Engineering Workshops, ISSREW 2012
Conference Date: November 27, 2012 - November 30, 2012
Issued Date: 2012
Conference Place: Dallas, TX, United states
Keyword: Maximum likelihood estimation ; Software reliability ; Software testing ; Technical presentations
Indexed Type: EI
ISBN: 9780769549286
Department: (1) State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China; (2) State Key Laboratory for Novel Software Technology Nanjing University Nanjing China; (3) Department of Computer Science Beihang University Beijing China
Abstract: Program debugging is a laborious but necessary phase of software development. It generally consists of fault localization, bug fix, and regression testing. Statistical software fault localization automates the manual and error-prone first task. It predicts fault locations by analyzing dynamic program spectrum captured in program runs. Previous studies mostly focused on how to provide reliable input data to such a technique and how to process the data accurately, but inadequately studied how to wield the output result of such a technique. In this work, we raise the assumption of symmetric distribution on the effectiveness of such a technique in locating faults, based on empirical results. We use maximum likelihood estimate and linear programming to develop a tuning method to enhance the result of a statistical fault localization technique. Experiments with two representative such techniques on two realistic UNIX utility programs validate our assumption and show our method effective. © 2012 IEEE.
English Abstract: Program debugging is a laborious but necessary phase of software development. It generally consists of fault localization, bug fix, and regression testing. Statistical software fault localization automates the manual and error-prone first task. It predicts fault locations by analyzing dynamic program spectrum captured in program runs. Previous studies mostly focused on how to provide reliable input data to such a technique and how to process the data accurately, but inadequately studied how to wield the output result of such a technique. In this work, we raise the assumption of symmetric distribution on the effectiveness of such a technique in locating faults, based on empirical results. We use maximum likelihood estimate and linear programming to develop a tuning method to enhance the result of a statistical fault localization technique. Experiments with two representative such techniques on two realistic UNIX utility programs validate our assumption and show our method effective. © 2012 IEEE.
Language: 英语
Content Type: 会议论文
URI: http://ir.iscas.ac.cn/handle/311060/15819
Appears in Collections:软件所图书馆_会议论文

Files in This Item:

There are no files associated with this item.


Recommended Citation:
Zhang Yunqian,Chen Lin,Jiang Bo,et al. wielding statistical fault localization statistically[C]. 见:23rd IEEE International Symposium on Software Reliability Engineering Workshops, ISSREW 2012. Dallas, TX, United states. November 27, 2012 - November 30, 2012.
Service
Recommend this item
Sava as my favorate item
Show this item's statistics
Export Endnote File
Google Scholar
Similar articles in Google Scholar
[Zhang Yunqian]'s Articles
[Chen Lin]'s Articles
[Jiang Bo]'s Articles
CSDL cross search
Similar articles in CSDL Cross Search
[Zhang Yunqian]‘s Articles
[Chen Lin]‘s Articles
[Jiang Bo]‘s Articles
Related Copyright Policies
Null
Social Bookmarking
Add to CiteULike Add to Connotea Add to Del.icio.us Add to Digg Add to Reddit
所有评论 (0)
暂无评论
 
评注功能仅针对注册用户开放,请您登录
您对该条目有什么异议,请填写以下表单,管理员会尽快联系您。
内 容:
Email:  *
单位:
验证码:   刷新
您在IR的使用过程中有什么好的想法或者建议可以反馈给我们。
标 题:
 *
内 容:
Email:  *
验证码:   刷新

Items in IR are protected by copyright, with all rights reserved, unless otherwise indicated.

 

 

Valid XHTML 1.0!
Copyright © 2007-2021  中国科学院软件研究所 - Feedback
Powered by CSpace