A critical evaluation of spectrum-based fault localization techniques on a large-scale software system
File(s)stardust.pdf (708.18 KB)
Accepted version
Author(s)
Type
Conference Paper
Abstract
In the past, spectrum-based fault localization (SBFL) techniques have been developed to pinpoint a fault location in a program given a set of failing and successful test executions. Most of the algorithms use similarity coefficients and have only been evaluated on established but small benchmark programs from the Software-artifact Infrastructure Repository (SIR). In this paper, we evaluate the feasibility of applying 33 state-of-the-art SBFL techniques to a large real-world project, namely ASPECTJ. From an initial set of 350 faulty version from the iBugs repository of ASPECTJ we manually classified 88 bugs where SBFL techniques are suitable. Notably, only 11 bugs of these bugs can be found after examining the 1000 most suspicious lines and on average 250 source code files need to be inspected per bug. Based on these results, the study showcases the limitations of current SBFL techniques on a larger program.
Date Issued
2017-08-15
Date Acceptance
2017-07-25
Citation
Proceedings - 2017 IEEE International Conference on Software Quality, Reliability and Security, QRS 2017, 2017, pp.114-125
ISBN
9781538605929
Publisher
IEEE
Start Page
114
End Page
125
Journal / Book Title
Proceedings - 2017 IEEE International Conference on Software Quality, Reliability and Security, QRS 2017
Copyright Statement
© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Source
2017 IEEE International Conference on Software Quality, Reliability and Security, QRS 2017
Publication Status
Published
Start Date
2017-07-25
Finish Date
2017-07-29
Coverage Spatial
Prague, Czech Republic