The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Fault localization is the activity to locate faults in programs. Spectrum-based fault localization (SBFL) is a class of techniques for it. It contrasts the code coverage achieved by passed runs and that by failed runs, and estimates program entities responsible for the latter. Although previous work has empirically shown that the effectiveness of typical SBFL techniques can be improved by incorporating...
Statistical fault localization techniques analyze the dynamic program information provided by executing a large number of test cases to predict fault positions in faulty programs. Related studies show that the extent of imbalance between the number of passed test cases and that of failed test cases may reduce the effectiveness of such techniques, while failed test cases can frequently be less than...
Fault localization commonly relies on both passed and failed runs, but passed runs are generally susceptible to coincidental correctness and modern software automatically produces a huge number of bug reports on failed runs. FOnly is an effective new technique that relies only on failed runs to locate faults statistically.
Fault localization is a major activity in software debugging. Many existing statistical fault localization techniques compare feature spectra of successful and failed runs. Some approaches, such as SOBER, test the similarity of the feature spectra through parametric self-proposed hypothesis testing models. Our finding shows, however, that the assumption on feature spectra forming known distributions...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.