TY - GEN
T1 - Contributing Features-Based Schemes for Software Defect Prediction
AU - Ali, Aftab
AU - Abu-tair, Mamun
AU - Noppen, Joost
AU - Mcclean, Sally
AU - Lin, Zhiwei
AU - Mcchesney, Ian
PY - 2019/11/19
Y1 - 2019/11/19
N2 - 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.
AB - 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.
KW - Machine leraning
KW - Intelligent information retrieval
KW - Prediction models
UR - http://link.springer.com/10.1007/978-3-030-34885-4_27
UR - https://link.springer.com/book/10.1007/978-3-030-34885-4#about
U2 - 10.1007/978-3-030-34885-4_27
DO - 10.1007/978-3-030-34885-4_27
M3 - Conference contribution
SN - 978-3-030-34884-7
T3 - Artificial Intelligence XXXVI
SP - 350
EP - 361
BT - Artificial Intelligence XXXVI
PB - Springer Netherlands
T2 - 39th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, AI 2019
Y2 - 17 December 2019 through 19 December 2019
ER -