Diagnosing Software Faults Using Multiverse Analysis

• Type: Article in International Conference Proceedings Book
• Date: 2020

Authors

Chatterjee, P (Author) Other; Chatterjee, A (Author) Other; José Campos (Author) FEUP; Rui Abreu (Author) FEUP; Roy, S (Author) Other

Conference proceedings International

Title: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI 2020 [scheduled for July 2020, Yokohama, Japan, postponed due to the Corona pandemic].

Pages: 1629-1635

29th International Joint Conference on Artificial Intelligence, IJCAI 2020

1 January 2021

Indexing

Scopus - 1 Citation

Other information

Authenticus ID: P-00S-CRE

DOI: 10.24963/ijcai.2020/226

Abstract (EN): Spectrum-based Fault Localization (SFL) approaches aim to efficiently localize faulty components from examining program behavior. This is done by collecting the execution patterns of various combinations of components and the corresponding outcomes into a spectrum. Efficient fault localization depends heavily on the quality of the spectra. Previous approaches, including the current state-of-the-art Density- Diversity-Uniqueness (DDU) approach, attempt to generate ¿good¿ test-suites by improving certain structural properties of the spectra. In this work, we propose a different approach, Multiverse Analysis, that considers multiple hypothetical universes, each corresponding to a scenario where one of the components is assumed to be faulty, to generate a spectrum that attempts to reduce the expected worst-case wasted effort over all the universes. Our experiments show that the Multiverse Analysis not just improves the efficiency of fault localization but also achieves better coverage and generates smaller test-suites over DDU, the current state-of-the-art technique. On average, our approach reduces the developer effort over DDU by over 16% for more than 92% of the instances. Further, the improvements over DDU are indeed statistically significant on the paired Wilcoxon Signed-rank test.

Language: English

Type (Professor's evaluation): Scientific