Extended Comprehensive Study of Association Measures for Fault Localization

• Main Authors: LUCIA, Lucia, LO, David, JIANG, Lingxiao, THUNG, Ferdian, BUDI, Aditya
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2014
• Subjects: Association Measures; Fault Localization; Program Spectra; Computer Sciences; Databases and Information Systems; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/1818; https://ink.library.smu.edu.sg/context/sis_research/article/2817/viewcontent/jsep_association.pdf
• Institution: Singapore Management University

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.