This paper presents an approach to automatically analyzing programspectra, an execution profile of programtesting results for fault\r\nlocalization. Using a mathematical theory of evidence for uncertainty reasoning, the proposed approach estimates the likelihood\r\nof faulty locations based on evidence from program spectra. Our approach is theoretically grounded and can be computed\r\nonline. Therefore, we can predict fault locations immediately after each test execution is completed. We evaluate the approach\r\nby comparing its performance with the top three performing fault localizers using a benchmark set of real-world programs. The\r\nresults show that our approach is at least as effective as others with an average effectiveness (the reduction of the amount of code\r\nexamined to locate a fault) of 85.6% over 119 versions of the programs.We also study the quantity and quality impacts of program\r\nspectra on our approach where the quality refers to the spectra support in identifying that a certain unit is faulty. The results show\r\nthat the effectiveness of our approach slightly improves with a larger number of failed runs but not with a larger number of passed\r\nruns. Program spectra with support quality increases from 1% to 100% improves the approach�s effectiveness by 3.29%.
Loading....