Prediction with V&V and Fault-Tolerance Strategies for Mandelbugs

Abstract: The information about which modules in a software system’s future version are potentially Mandelbugs is a valuable aid for quality managers and testers. Mandelbugs prediction promises to indicate these Mandelbugs-vulnerable modules. Constructing effective defect prediction models in an industrial setting involves the decision from what data source the Mandelbugs predictors should be derived. In this paper we compare Mandelbugs prediction consequences based on three different data sources of a large industrial software system to answer the question what repositories to mine. In addition, we investigate whether a combination of different data sources improves the prediction consequences. The findings indicate that predictors derived from static code and design analysis provide slightly yet still significant better outcome than predictors derived from version control, while a combination of all data sources showed no further improvement. we aim to combine prediction with V&V and fault-tolerance strategies