Print Email Facebook Twitter Comparing Fine-Grained Source Code Changes And Code Churn For Bug Prediction Title Comparing Fine-Grained Source Code Changes And Code Churn For Bug Prediction Author Giger, E. Pinzger, M. Gall, H.C. Faculty Electrical Engineering, Mathematics and Computer Science Department Software Technology Date 2011-05-22 Abstract A significant amount of research effort has been dedicated to learning prediction models that allow project managers to efficiently allocate resources to those parts of a software system that most likely are bug-prone and therefore critical. Prominent measures for building bug prediction models are product measures, e.g., complexity or process measures, such as code churn. Code churn in terms of lines modified (LM) and past changes turned out to be significant indicators of bugs. However, these measures are rather imprecise and do not reflect all the detailed changes of particular source code entities during maintenance activities. In this paper, we explore the advantage of using fine-grained source code changes (SCC) for bug prediction. SCC captures the exact code changes and their semantics down to statement level. We present a series of experiments using different machine learning algorithms with a dataset from the Eclipse platform to empirically evaluate the performance of SCC and LM. The results show that SCC outperforms LM for learning bug prediction models. Accepted for publication in the Proceedings of the Working Conference on Mining Software Repositories, 2011, ACM Press. Subject Software bugscode churnsource code changesprediction modelsnonlinear regression To reference this document use: http://resolver.tudelft.nl/uuid:0a97b966-a163-42ee-8432-09a93f1979ff Publisher Delft University of Technology, Software Engineering Research Group ISSN 1872-5392 Source Technical Report Series TUD-SERG-2011-007 Part of collection Institutional Repository Document type report Rights (c) 2011 The authors. Software Engineering Research Group, Department of Software Technology, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology. Files PDF TUD-SERG-2011-007.pdf 649.94 KB Close viewer /islandora/object/uuid:0a97b966-a163-42ee-8432-09a93f1979ff/datastream/OBJ/view