Yu, Y., Jones, J.A. and Harrold, M.J. (2008) An Empirical Study of the Effects of Test-Suite Reduction on Fault Localization. Proceedings of the 30th International Conference on Software Engineering, 201-210.
http://doi.acm.org/10.1145/1368088.1368116

has been cited by the following article:

• TITLE: Statistical Debugging Effectiveness as a Fault Localization Approach: Comparative Study

  AUTHORS: Ishaq Sandoqa, Fawaz Alzghoul, Hamad Alsawalqah, Isra Alzghoul, Loai Alnemer, Mohammad Akour

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

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.9 No.8, August 19, 2016

  ABSTRACT: 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.