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2006
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Predicting defect densities in source code files with decision tree learners

9 years 6 months ago
Predicting defect densities in source code files with decision tree learners
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 eas...
Patrick Knab, Martin Pinzger, Abraham Bernstein
Added 14 Jun 2010
Updated 14 Jun 2010
Type Conference
Year 2006
Where MSR
Authors Patrick Knab, Martin Pinzger, Abraham Bernstein
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