<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Rui Abreu</style></author><author><style face="normal" font="default" size="100%">Zoeteweij, Peter</style></author><author><style face="normal" font="default" size="100%">Gemund, A</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Localizing Software Faults Simultaneously</style></title><secondary-title><style face="normal" font="default" size="100%">9th International Conference on Quality Software - QSIC</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year><pub-dates><date><style  face="normal" font="default" size="100%">August</style></date></pub-dates></dates><urls><related-urls><url><style face="normal" font="default" size="100%">https://haslab.uminho.pt/sites/default/files/ruimaranhao/files/qsic09.pdf</style></url></related-urls></urls><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><pub-location><style face="normal" font="default" size="100%">Jeju, Korea</style></pub-location><pages><style face="normal" font="default" size="100%">367–376</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Current automatic diagnosis techniques are predominantly of a statistical nature and, despite typical defect densities, do not explicitly consider multiple faults, as also demonstrated by the popularity of the single-fault Siemens set. We present a logic reasoning approach, called Zoltar-M(ultiple fault), that yields multiple-fault diagnoses, ranked in order of their probability. Although application of Zoltar-M to programs with many faults requires further research into heuristics to reduce computational complexity, theory as well as experiments on synthetic program models and two multiple-fault program versions from the Siemens set show that for multiple-fault programs this approach can outperform statistical techniques, notably spectrum-based fault localization (SFL). As a side-effect of this research, we present a new SFL variant, called Zoltar-S(ingle fault), that is provably optimal for single-fault programs, outperforming all other variants known to date.&lt;/p&gt;
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