Abstract: A defect level for a software application may be predicted by training a model using aspects of development processes from previous software applications as training data. Aspects of previous software development processes may be aggregated to form signal vectors for each deployed application. Defect scores calculated from actual defects in the deployed software applications may be paired with the corresponding development signal vectors. The signal vectors and calculated defect scores may act as training data and labels for a predictive model that uses lasso regression to generate a predicted defect score during the development process. A signal vector for a current development process may be updated in real time as the software is developed to update a predicted defect score and provide a subset of aspects in the signal vector that contribute most to the score such that actions may be taken to improve the score.

Abstract: A defect level for a software application may be predicted by training a model using aspects of development processes from previous software applications as training data. Aspects of previous software development processes may be aggregated to form signal vectors for each deployed application. Defect scores calculated from actual defects in the deployed software applications may be paired with the corresponding development signal vectors. The signal vectors and calculated defect scores may act as training data and labels for a predictive model that uses lasso regression to generate a predicted defect score during the development process. A signal vector for a current development process may be updated in real time as the software is developed to update a predicted defect score and provide a subset of aspects in the signal vector that contribute most to the score such that actions may be taken to improve the score.