Umair Ali

Work place: Department of Computer Science, Virtual University of Pakistan, Lahore, Pakistan



Research Interests: Software Engineering, Data Mining, Data Structures and Algorithms


Umair Ali is student of MS Computer Science with the specialization of Software Engineering in Virtual University of Pakistan. He received the degree, BS Computer Science from Virtual University of Pakistan in 2016. His research interest includes Software Engineering and Data Mining.

Author Articles
Software Defect Prediction Using Variant based Ensemble Learning and Feature Selection Techniques

By Umair Ali Shabib Aftab Ahmed Iqbal Zahid Nawaz Muhammad Salman Bashir Muhammad Anwaar Saeed

DOI:, Pub. Date: 8 Oct. 2020

Testing is considered as one of the expensive activities in software development process. Fixing the defects during testing process can increase the cost as well as the completion time of the project. Cost of testing process can be reduced by identifying the defective modules during the development (before testing) stage. This process is known as “Software Defect Prediction”, which has been widely focused by many researchers in the last two decades. This research proposes a classification framework for the prediction of defective modules using variant based ensemble learning and feature selection techniques. Variant selection activity identifies the best optimized versions of classification techniques so that their ensemble can achieve high performance whereas feature selection is performed to get rid of such features which do not participate in classification and become the cause of lower performance. The proposed framework is implemented on four cleaned NASA datasets from MDP repository and evaluated by using three performance measures, including: F-measure, Accuracy, and MCC. According to results, the proposed framework outperformed 10 widely used supervised classification techniques, including: “Naïve Bayes (NB), Multi-Layer Perceptron (MLP), Radial Basis Function (RBF), Support Vector Machine (SVM), K Nearest Neighbor (KNN), kStar (K*), One Rule (OneR), PART, Decision Tree (DT), and Random Forest (RF)”.

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