{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,5]],"date-time":"2025-06-05T10:09:31Z","timestamp":1749118171503},"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":[[2019,8]]},"abstract":"<jats:p>Several approaches have been proposed to reduce debugging costs through\nautomated software fault diagnosis.  Dynamic Slicing (DS) and Spectrum-based\nFault Localization (SFL) are popular fault diagnosis techniques and normally\nseen as complementary. This paper reports on a comprehensive\nstudy to reassess the effects of combining DS with SFL.  With this\ncombination, components that are often involved in failing but seldom in passing\ntest runs could be located and their suspiciousness reduced. \nResults show that the DS-SFL combination, coined\nas Tandem-FL, improves the diagnostic accuracy up\nto 73.7%  (13.4%  on average). Furthermore, results\nindicate that the risk of missing faulty statements,\nwhich is a DS?s key limitation, is not high ? DS\nmisses faulty statements in 9%  of the 260 cases. To\nsum up, we found that the DS-SFL combination\nwas practical and effective and encourage new SFL\ntechniques to be evaluated against that optimization.<\/jats:p>","DOI":"10.24963\/ijcai.2019\/661","type":"proceedings-article","created":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T07:46:05Z","timestamp":1564299965000},"page":"4760-4766","source":"Crossref","is-referenced-by-count":13,"title":["Demystifying the Combination of Dynamic Slicing and Spectrum-based Fault Localization"],"prefix":"10.24963","author":[{"given":"Sofia","family":"Reis","sequence":"first","affiliation":[{"name":"IST, University of Lisbon, Portugal"},{"name":"INESC-ID, Portugal"}]},{"given":"Rui","family":"Abreu","sequence":"additional","affiliation":[{"name":"IST, University of Lisbon, Portugal"},{"name":"INESC-ID, Portugal"}]},{"given":"Marcelo","family":"d'Amorim","sequence":"additional","affiliation":[{"name":"Federal University of Pernambuco, Brazil"}]}],"member":"10584","event":{"number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2019","name":"Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}","start":{"date-parts":[[2019,8,10]]},"theme":"Artificial Intelligence","location":"Macao, China","end":{"date-parts":[[2019,8,16]]}},"container-title":["Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T07:50:54Z","timestamp":1564300254000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2019\/661"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2019,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2019\/661","relation":{},"subject":[],"published":{"date-parts":[[2019,8]]}}}