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Contribution Details

Type Conference or Workshop Paper
Scope Discipline-based scholarship
Published in Proceedings No
Title Predicting Defect Densities in Source Code Files with Decision Tree Learners
Organization Unit
  • Patrick Knab
  • Martin Pinzger
  • Abraham Bernstein
Item Subtype Original Work
Refereed Yes
Status Published in final form
Event Title MSR '06: Proceedings of the 2006 International Workshop on Mining Software Repositories
Place of Publication New York, NY, USA
Publisher ACM
Abstract Text 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.
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