Publication Type

Journal Article

Publication Date

2-2014

Abstract

Spectrum-based fault localization is a promising approach to automatically locate root causes of failures quickly. Two well-known spectrum-based fault localization techniques, Tarantula and Ochiai, measure how likely a program element is a root cause of failures based on profiles of correct and failed program executions. These techniques are conceptually similar to association measures that have been proposed in statistics, data mining, and have been utilized to quantify the relationship strength between two variables of interest (e.g., the use of a medicine and the cure rate of a disease). In this paper, we view fault localization as a measurement of the relationship strength between the execution of program elements and program failures. We investigate the effectiveness of 40 association measures from the literature on locating bugs. Our empirical evaluations involve single-bug and multiple-bug programs. We find there is no best single measure for all cases. Klosgen and Ochiai outperform other measures for localizing single-bug programs. Although localizing multiple-bug programs, Added Value could localize the bugs with on average smallest percentage of inspected code, whereas a number of other measures have similar performance. The accuracies of the measures in localizing multi-bug programs are lower than single-bug programs, which provokes future research.

Keywords

Association Measures, Fault Localization, Program Spectra

Discipline

Computer Sciences | Databases and Information Systems | Software Engineering

Research Areas

Data Management and Analytics; Software and Cyber-Physical Systems

Publication

Journal of Software: Evolution and Process

Volume

26

Issue

2

First Page

172

Last Page

219

ISSN

2047-7481

Identifier

10.1002/smr.1616

Publisher

Wiley

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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Additional URL

http://doi.org/10.1002/smr.1616

Comments

To cite the data package, please use the following citation:

Lucia, L., Lo, D., Jiang, L., Thung, F., & Budi, A. (2014). Data from: Extended Comprehensive Study of Association Measures for Fault Localization. InK Repository at Singapore Management University. http://ink.library.smu.edu.sg/sis_research/1818

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