A Theoretical and Empirical Analysis of Program Spectra Diagnosability

• Type: Article in International Scientific Journal
• Date: 2021

Authors

Alexandre Perez (Author) Other; Rui Abreu (Author) FEUP; Arie Van Deursen (Author) Other

Journal

Title: IEEE Transactions on Software Engineering

Vol. 47

Pages: 412-431

ISSN: 0098-5589

Publisher: IEEE

Indexing

ISI Web of Knowledge - 1 Citation

Scopus - 5 Citations

Other information

Authenticus ID: P-00T-JG2

DOI: 10.1109/tse.2019.2895640

Abstract (EN): Current metrics for assessing the adequacy of a test-suite plainly focus on the number of components (be it lines, branches, paths) covered by the suite, but do not explicitly check how the tests actually exercise these components and whether they provide enough information so that spectrum-based fault localization techniques can perform accurate fault isolation. We propose a metric, called DDU, aimed at complementing adequacy measurements by quantifying a test-suite's diagnosability, i.e., the effectiveness of applying spectrum-based fault localization to pinpoint faults in the code in the event of test failures. Our aim is to increase the value generated by creating thorough test-suites, so they are not only regarded as error detection mechanisms but also as effective diagnostic aids that help widely-used fault-localization techniques to accurately pinpoint the location of bugs in the system. We have performed a topology-based simulation of thousands of spectra and have found that DDU can effectively establish an upper bound on the effort to diagnose faults. Furthermore, our empirical experiments using the Defects4J dataset show that optimizing a test suite with respect to DDU yields a 34 percent gain in spectrum-based fault localization report accuracy when compared to the standard branch-coverage metric.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 20