<?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%">Knab, Patrick</style></author><author><style face="normal" font="default" size="100%">Pinzger, Martin</style></author><author><style face="normal" font="default" size="100%">Bernstein, Abraham</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Predicting defect densities in source code files with decision tree learners</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 2006 international workshop on Mining software repositories</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">MSR '06</style></tertiary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">change analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">data mining</style></keyword><keyword><style  face="normal" font="default" size="100%">decision tree learner</style></keyword><keyword><style  face="normal" font="default" size="100%">defect density</style></keyword><keyword><style  face="normal" font="default" size="100%">defect prediction</style></keyword><keyword><style  face="normal" font="default" size="100%">mozilla</style></keyword><keyword><style  face="normal" font="default" size="100%">prediction</style></keyword><keyword><style  face="normal" font="default" size="100%">release history</style></keyword><keyword><style  face="normal" font="default" size="100%">scm</style></keyword><keyword><style  face="normal" font="default" size="100%">source code</style></keyword><keyword><style  face="normal" font="default" size="100%">version control</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://doi.acm.org/10.1145/1137983.1138012</style></url></web-urls><related-urls><url><style face="normal" font="default" size="100%">http://ifipwg213.org/sites/flosshub.org/files/119Predicting.pdf</style></url></related-urls></urls><publisher><style face="normal" font="default" size="100%">ACM</style></publisher><pub-location><style face="normal" font="default" size="100%">New York, NY, USA</style></pub-location><pages><style face="normal" font="default" size="100%">119–125</style></pages><isbn><style face="normal" font="default" size="100%">1-59593-397-2</style></isbn><abstract><style face="normal" font="default" size="100%">With the advent of open source software repositories the data available for defect prediction in source files increased tremendously. Although traditional statistics turned out to derive reasonable results the sheer amount of data and the problem context of defect prediction demand sophisticated analysis such as provided by current data mining and machine learning techniques.In this work we focus on defect density prediction and present an approach that applies a decision tree learner on evolution data extracted from the Mozilla open source web browser project. The evolution data includes different source code, modification, and defect measures computed from seven recent Mozilla releases. Among the modification measures we also take into account the change coupling, a measure for the number of change-dependencies between source files. The main reason for choosing decision tree learners, instead of for example neural nets, was the goal of finding underlying rules which can be easily interpreted by humans. To find these rules, we set up a number of experiments to test common hypotheses regarding defects in software entities. Our experiments showed, that a simple tree learner can produce good results with various sets of input data.</style></abstract></record></records></xml>