{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T03:58:33Z","timestamp":1769745513817,"version":"3.49.0"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,7]]},"abstract":"<jats:p>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\n\ncombinations 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\n\nstate-of-the-art Density- Diversity-Uniqueness (DDU) approach, attempt to generate \u201cgood\u201d test-suites by improving certain structural properties of the spectra. In this work, we propose a different\n\napproach, 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\n\nattempts 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.<\/jats:p>","DOI":"10.24963\/ijcai.2020\/226","type":"proceedings-article","created":{"date-parts":[[2020,7,8]],"date-time":"2020-07-08T12:12:10Z","timestamp":1594210330000},"page":"1629-1635","source":"Crossref","is-referenced-by-count":12,"title":["Diagnosing Software Faults Using Multiverse Analysis"],"prefix":"10.24963","author":[{"given":"Prantik","family":"Chatterjee","sequence":"first","affiliation":[{"name":"Indian Institute of Technology Kanpur"}]},{"given":"Abhijit","family":"Chatterjee","sequence":"additional","affiliation":[{"name":"Indian Institute of Technology Kanpur"}]},{"given":"Jose","family":"Campos","sequence":"additional","affiliation":[{"name":"University of Lisbon"}]},{"given":"Rui","family":"Abreu","sequence":"additional","affiliation":[{"name":"University of Lisbon"}]},{"given":"Subhajit","family":"Roy","sequence":"additional","affiliation":[{"name":"Indian Institute of Technology Kanpur"}]}],"member":"10584","event":{"name":"Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}","theme":"Artificial Intelligence","location":"Yokohama, Japan","acronym":"IJCAI-PRICAI-2020","number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2020,7,11]]},"end":{"date-parts":[[2020,7,17]]}},"container-title":["Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2020,7,9]],"date-time":"2020-07-09T02:13:58Z","timestamp":1594260838000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2020\/226"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2020,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2020\/226","relation":{},"subject":[],"published":{"date-parts":[[2020,7]]}}}