Author(s)

Ishaq Sandoqa¹, Fawaz Alzghoul¹, Hamad Alsawalqah¹, Isra Alzghoul¹, Loai Alnemer¹, Mohammad Akour²

¹Department of Computer Information Systems, The University of Jordan, Amman, Jordan.
²Department of Computer Information System, Yarmouk University, Irbid, Jordan.

Fault localization is an important topic in software testing, as it enables the developer to specify fault location in their code. One of the dynamic fault localization techniques is statistical debugging. In this study, two statistical debugging algorithms are implemented, SOBER and Cause Isolation, and then the experimental works are conducted on five programs coded using Python as an example of well-known dynamic programming language. Results showed that in programs that contain only single bug, the two studied statistical debugging algorithms are very effective to localize a bug. In programs that have more than one bug, SOBER algorithm has limitations related to nested predicates, rarely observed predicates and complement predicates. The Cause Isolation has limitations related to sorting predicates based on importance and detecting bugs in predicate condition. The accuracy of both SOBER and Cause Isolation is affected by the program size. Quality comparison showed that SOBER algorithm requires more code examination than Cause Isolation to discover the bugs.

Testing and Debugging, Dynamic Language, Statistical Debugging, Fault Localization

Sandoqa, I. , Alzghoul, F. , Alsawalqah, H. , Alzghoul, I. , Alnemer, L. and Akour, M. (2016) Statistical Debugging Effectiveness as a Fault Localization Approach: Comparative Study. Journal of Software Engineering and Applications, 9, 412-423. doi: 10.4236/jsea.2016.98027.