Building Defect Prediction Models in Practice

Building Defect Prediction Models in Practice

Rudolf Ramler (Software Competence Center Hagenberg, Austria), Johannes Himmelbauer (Software Competence Center Hagenberg, Austria) and Thomas Natschläger (Software Competence Center Hagenberg, Austria)
DOI: 10.4018/978-1-5225-3923-0.ch014

Abstract

The information about which modules of a future version of a software system will be defect-prone is a valuable planning aid for quality managers and testers. Defect prediction promises to indicate these defect-prone modules. In this chapter, building a defect prediction model from data is characterized as an instance of a data-mining task, and key questions and consequences arising when establishing defect prediction in a large software development project are discussed. Special emphasis is put on discussions on how to choose a learning algorithm, select features from different data sources, deal with noise and data quality issues, as well as model evaluation for evolving systems. These discussions are accompanied by insights and experiences gained by projects on data mining and defect prediction in the context of large software systems conducted by the authors over the last couple of years. One of these projects has been selected to serve as an illustrative use case throughout the chapter.
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Data Mining And Knowledge Discovery For Defect Prediction

Defect prediction is based on prediction models built from software engineering data. Thus, defect prediction can be understood as an application within the broad area of data mining and knowledge discovery which refer to general results of research, techniques and tools used to extract useful information and models from (large volumes of) data (Mariscal et al. 2010).

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