Browsing by Author "Jabiullah, Md Ismail"

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Assessing the Effect of Imbalanced Learning on Cross-project Software Defect Prediction
(10th International Conference on Computing, Communication and Networking Technologies, ICCCNT 2019, IEEE, 2019-07-08) Sohan, Md Fahimuzzman; Jabiullah, Md Ismail; Rahman, Sheikh Shah Mohammad Motiur; Mahmud, S M Hasan
Software Defect Prediction (SDP) identifies the defect-prone modules from software source code, which helps to serve good quality software. Mostly previous cross-project SDP models were built based on single project data, where single project was used to prepare prediction models. However, this investigation represents an empirical study of SDP where multiple projects data have been used to prepare prediction models. In this study, multiple projects data have been used to prepare a balance and an imbalance datasets. After that this datasets have been used in different prediction models with eight different classifier algorithms. The trained models have been cross-checked by one balanced and imbalanced test datasets. Five evaluation metrics have been considered for evaluating the performance of the models. The experimental results show that there was no significant changes observed between balanced and imbalanced training models. Only AUC (Area Under the Curve) scores have increased significantly in terms of balanced training model with imbalanced test datasets. In the same training model with the balanced test, accuracy and AUC score have increased significantly. However, this study covers widely by creating the classification model from multiple projects' historical data. Further, it proves that if the sufficient number of non-defective and defective data are supplied in the prediction model, it can predict balanced and imbalanced both categories dataset alike. Here, recommendation will be to consider the imbalanced learning while building the prediction model for cross-projects.
Prevalence of Machine Learning Techniques in Software Defect Prediction
(Scopus, 2020) Sohan, Md Fahimuzzman; Kabir, Md Alamgir; Rahman, Mostafijur; Bhuiyan, Touhid; Jabiullah, Md Ismail
Software Defect Prediction (SDP) is a popular research area which plays an important role for software quality. It works as an indicator of whether a software module is defect-free or defective. In this study, a review has been conducted from January 2015 to August 2019 and 165 articles are selected in the area of SDP to know the prevalence of Machine Learning (ML) techniques. These articles are collected by searching in Google Scholar, and they are published in various platforms (e.g., IEEE, Springer, Elsevier). Firstly the information has been extracted from the collected particles, and then the information has been pre-processed, categorized, visualized, and finally, the results have been reported. The result shows the most frequently used data sets, classifiers, performance metrics, and techniques in SDP. This investigation will help to find the prevalence of ML techniques in SDP and give a quick view to understand the trends of ML techniques in defect prediction research.