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Incremental Development and Cost-based Evaluation of Software Fault Prediction Models

By Yue Jiang

Abstract

It is difficult to build high quality software with limited quality assurance budgets. Software fault prediction models can be used to learn fault predictors from software metrics. Fault prediction prior to software release can guide Verification and Validation (V &V) activity and allocate scarce resources to modules which are predicted to be fault-prone. One of the most important goals of fault prediction is to detect fault prone modules as early as possible in the software development life cycle. Design and code metrics have been successfully used for predicting fault-prone modules. In this dissertation, we introduce fault prediction from software requirements. Furthermore, we investigate the advantages of the incremental development of software fault prediction models, and we compare the performance of these models as the volume of data and their life cycle origin (design, code, or their combination) evolution during project development. We confirm that increasing the volume of training data improves model performance. And that, models built from code metrics typically outperform those built using design metrics only. However, both types of models prove to be useful as they can be constructed in different phases of the life cycle. We also demonstrate that models that utilize a combination of design and code level metrics outperform models which use either one metric set exclusively. I

Topics: fault-proneness, prediction, requirement, design, software metrics machine learning, classification
Year: 2009
OAI identifier: oai:CiteSeerX.psu:10.1.1.352.8152
Provided by: CiteSeerX
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