International Business Machines Corporation

Yogesh Rane

Bo Zhang

Alexander Sobran

Guilherme Ferreira

Approaches presented herein enable domain knowledge-based software defect prediction. More specifically, a cost function configured to train a machine learning model to predict a defect in a software version is obtained. A domain knowledge penalty metric is then determined for a software development team associated with the software version. A linear combination of the determined domain knowledge penalty metric is then applied to the obtained cost function to create a domain knowledge-modified cost function. Using this domain knowledge-modified cost function, the machine learning model is then trained based on a minimization of the domain knowledge-modified cost function. Once trained, the machine learning model is used to report a set of predicted values based on the domain knowledge-modified cost function trained machine learning model.

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Raleigh, NC, United States of America

Yogesh Rane