Project Data Incorporating Qualitative Factors for Improved Software Defect Prediction

Authors:
Norman Fenton;Martin Neil;William Marsh;Peter Hearty;Lukasz Radlinski;Paul Krause
Affiliations:
Queen Mary, University of London, UK;Queen Mary, University of London, UK;Queen Mary, University of London, UK;Queen Mary, University of London, UK;Queen Mary, University of London, UK/ University of Szczecin, Poland;University of Surrey, UK
Venue:
PROMISE '07 Proceedings of the Third International Workshop on Predictor Models in Software Engineering
Year:
2007

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Abstract

To make accurate predictions of attributes like defects found in complex software projects we need a rich set of process factors. We have developed a causal model that includes such process factors, both quantitative and qualitative. The factors in the model were identified as part of a major collaborative project. A challenge for such a model is getting the data needed to validate it. We present a dataset, elicited from 31 completed software projects in the consumer electronics industry, which we used for validation. The data were gathered using a questionnaire distributed to managers of recent projects. The dataset will be of interest to other researchers evaluating models with similar aims. We make both the dataset and causal model available for research use.