SDP is the term that describes the most significant step in the process of identifying fault-prone components provided in the case of the software development life cycle. Its main agenda is not only to enhance software quality but also to cut down maintenance cost in the long run. Although the sheer amount of research dedicated to SDP is quite high, the current models still have numerous limitations in its practice such as high false positive rates and the serious imbalance of the number of defective and substitute modules. Natural limitations negatively affect the learning capabilities of predictive models and lower the accuracy and quality of the debugging process as a whole. To solve these nagging issues, the current study has proposed a robust and strong SDP model that builds on a mixture of sophisticated computing techniques, in particular, those that are eventualities of making precise forecasts and model generalization. The method stated here starts with a thorough preprocessing bezel on the information, during which the information will be normalized in terms of features, so that all elements are on a similar scale. The move serves to reduce the impact of outliers and noisy data hence improving the quality of training dataset. After preprocessing, a class imbalance will be solved with the help of Minority Oversampling by Synthetic Data (MOSD) technique. This can be done by creating synthetic data of the minority class to get a more balanced distribution of defective and good examples which is essential toward good training of classifiers. After that, an Adaptive Sequential K-Best (ASKB) feature selection algorithm is applied to highlight the most considerable and informative features. This approach analytically discerns the significance of any attribute in a dynamic fashion; thus decreasing the dimension of the dataset with negligible loss of crucial information on predictive variables. The slimmed down feature set will also help in building a model that can be interpreted better and is also computationally economical. As a classification algorithm a Weighted Random Forest (WRF) is used in the case of the classification task. This extension of the conventional Random Forest incorporates instance-based weighting where the model is made to give more weight to developing accurate labels of instances of the minority class. The WRF classifier, in turn, improves the overall performance in prediction and lessens the bias in relation to the dominant category. The empirical assessment of the offered structure proves its capabilities to be highly advanced in comparison to the existing models. The method produced outstanding classification accuracy measures that were 99.11999, 99.43111, 99.12199 and 99.33333 in terms of accuracy rate, precision, recall and F1- score respectively. The outcomes can substantiate the usefulness of the combined methodology on enhancing defect forecasting abilities. Moreover, the suggested model has practical implications that can be employed in proactive software quality assurance, thus more reliable and cost-effective software engineering processes can be conducted.
Akkar et al. (Sun,) studied this question.