Automated defect prediction of large and complex software systems is a challenging task. However, by utilising correlated quality metrics, a defect prediction model can be devised to automatically predict the defects in a software system. The robustness and accuracy of a prediction model is highly dependent on the selection of contributing and non-contributing features. Hence, in this regard, the contribution of this paper is twofold, first it separates those features which are contributing towards the development of a defect in a software component from those which are non-contributing features. Secondly, a logistic regression and Ensemble Bagged Trees-based prediction model are applied on the contributing features for accurately predicting a defect in a software component. The proposed models are compared with the most recent scheme in the literature in terms of accuracy and area under the curve (AUC). It is evident from the results and analysis that the performance of the proposed prediction models outperforms the schemes in the literature.
|Name||Artificial Intelligence XXXVI|
|Conference||39th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, AI 2019|
|Period||17/12/19 → 19/12/19|
- Machine leraning
- Intelligent information retrieval
- Prediction models