Conference Proceeding Article
In statistics and data mining communities, there have been many measures proposed to gauge the strength of association between two variables of interest, such as odds ratio, confidence, Yule-Y, Yule-Q, Kappa, and gini index. These association measures have been used in various domains, for example, to evaluate whether a particular medical practice is associated positively to a cure of a disease or whether a particular marketing strategy is associated positively to an increase in revenue, etc. This paper models the problem of locating faults as association between the execution or non-execution of particular program elements with failures. There have been special measures, termed as suspiciousness measures, proposed for the task. Two state-of-the-art measures are Tarantula and Ochiai, which are different from many other statistical measures. To the best of our knowledge, there is no study that comprehensively investigates the effectiveness of various association measures in localizing faults. This paper fills in the gap by evaluating 20 wellknown association measures and compares their effectiveness in fault localization tasks with Tarantula and Ochiai. Evaluation on the Siemens programs show that a number of association measures perform statistically comparable as Tarantula and Ochiai.
Association measures, Fault location, Debugging, Data mining, Statistical analysis, Variables of interest
Proceedings of the IEEE International Conference on Software Maintenance (ICSM)
City or Country
Lucia, David Lo, Jiang Lingxiao, Aditya Budi. 2010. "Comprehensive Evaluation of Association Measures for Fault Localization." In Proceedings of IEEE International Conference on Software Maintenance (ICSM), September 12-18, Timisoara, Romania.. New York: IEEE. doi: 10.1109/ICSM.2010.5609542
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Data from Comprehensive Evaluation of Association Measures for Fault Localization