Browsing by Author "Basri, Shuib"

Now showing 1 - 4 of 4
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    A Hybrid Multi-Filter Wrapper Feature Selection Method for Software Defect Predictors
    (ExcelingTech Publisher, UK, 2019-04) Balogun, Abdullateef Oluwagbemiga; Basri, Shuib; Abdulkadir, Said Jadid; Sobri, Ahmad Hashim
    Software Defect Prediction (SDP) is an approach used for identifying defect-prone software modules or components. It helps software engineer to optimally, allocate limited resources to defective software modules or components in the testing or maintenance phases of the software development life cycle (SDLC). Nonetheless, the predictive performance of SDP models reckons largely on the quality of dataset utilized for training the predictive models. The high dimensionality of software metric features has been noted as a data quality problem which negatively affects the predictive performance of SDP models. Feature Selection (FS) is a well-known method for solving high dimensionality problem and can be divided into filter-based and wrapper-based methods. Filter-based FS has low computational cost, but the predictive performance of its classification algorithm on the filtered data cannot be guaranteed. On the contrary, wrapper-based FS have good predictive performance but with the high computational cost and lack of generalizability. Therefore, this study proposes a hybrid multi-filter wrapper method for feature selection of relevant and irredundant features in software defect prediction. The proposed hybrid feature selection will be developed to take advantage of filter-filter and filter-wrapper relationships to give optimal feature subsets, reduce its evaluation cycle and subsequently improve SDP models overall predictive performance in terms of Accuracy, Precision and Recall values.
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    The Organisational Factors of Software Process Improvement in Small Software Industry: Comparative Study
    (Springer, Cham, 2019-09) Basri, Shuib; Almomani, Malek Ahmad; Imam, Abubakar Abdullahi; Thangiah, Murugan; Gilal, Abdul Rehman; Balogun, Abdullateef Oluwagbemiga
    Small and Medium Enterprises (SMEs) are a great contribution to the international economy and have also been considered an important component in today’s world business. Thus, in order to be more competitive, it is necessary for these companies to deliver their products with high-quality. However, despite their importance, small software companies still face myriad challenges and barriers in producing high-quality products. The objective of this study is to identify the organizational factors that have a positive impact to enable Software Process Improvement (SPI) effort in the small software industry. A Systematic Literature Review (SLR) was conducted to achieve the main objective of this study. The findings from this study provide a roadmap to guide future research in order to enable SPI effort in the small software development industry. We believe that findings from this study will give interesting insights to encourage researchers in using compromise technique to analyze future empirical studies based on a specific region to validate the suitability of identified factors in the specific country.
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    Performance Analysis of Feature Selection Methods in Software Defect Prediction: A Search Method Approach
    (Multidisciplinary Digital Publishing Institute (MDPI), 2019-07-09) Balogun, Abdullateef; Basri, Shuib; Abdulkadir, Said Jadid; Sobri, Ahmad Hashim
    Software Defect Prediction (SDP) models are built using software metrics derived from software systems. The quality of SDP models depends largely on the quality of software metrics (dataset) used to build the SDP models. High dimensionality is one of the data quality problems that affect the performance of SDP models. Feature selection (FS) is a proven method for addressing the dimensionality problem. However, the choice of FS method for SDP is still a problem, as most of the empirical studies on FS methods for SDP produce contradictory and inconsistent quality outcomes. Those FS methods behave differently due to different underlining computational characteristics. This could be due to the choices of search methods used in FS because the impact of FS depends on the choice of search method. It is hence imperative to comparatively analyze the FS methods performance based on different search methods in SDP. In this paper, four filter feature ranking (FFR) and fourteen filter feature subset selection (FSS) methods were evaluated using four different classifiers over five software defect datasets obtained from the National Aeronautics and Space Administration (NASA) repository. The experimental analysis showed that the application of FS improves the predictive performance of classifiers and the performance of FS methods can vary across datasets and classifiers. In the FFR methods, Information Gain demonstrated the greatest improvements in the performance of the prediction models. In FSS methods, Consistency Feature Subset Selection based on Best First Search had the best influence on the prediction models. However, prediction models based on FFR proved to be more stable than those based on FSS methods. Hence, we conclude that FS methods improve the performance of SDP models and that there is no single best FS method, as their performance varied according to datasets and the choice of the prediction model. However, we recommend the use of FFR methods as the prediction models based on FFR are more stable in terms of predictive performance
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    SOFTWARE DEFECT PREDICTION: ANALYSIS OF CLASS IMBALANCE AND PERFORMANCE STABILITY
    (School of Engineering, Taylor’s University, 2019-12) Balogun, Abdullateef Oluwagbemiga; Basri, Shuib; Said, Jadid Abdulkadir; Adeyemo, Victor Ebenezer; Imam, Abdullahi Abubakar; Bajeh, Amos Orenyi
    The performance of prediction models in software defect prediction depends on the quality of datasets used for training such models. Class imbalance is one of data quality problems that affect prediction models. This has drawn the attention of researchers and many approaches have been developed to address this issue. In this study, an extensive empirical study is presented, which evaluates the performance stability of prediction models in SDP. Ten software defect datasets from NASA and PROMISE repositories with varying imbalance ratio (IR) values were used as the original datasets. New datasets are generated from the original datasets using undersampling (Random under Sampling: RUS) and oversampling (Synthetic Minority Oversampling Technique: SMOTE) methods with different IR values. The sampling techniques were based on the equal proportion (100%) of the increment (SMOTE) of minority class label or decrement (RUS) of the majority class label until each dataset is balanced. IR is the ratio of the defective instances to non-defective instances in a dataset. Each newly generated datasets with different IR values based on different sampling techniques were randomized before applying prediction models. Nine standard prediction models were used on the newly generated datasets. The performance of the prediction models was measured using the Area Under Curve (AUC) and Co-efficient of Variation (CV) is used to determine the performance stability. Firstly, experimental results showed that class imbalance had a negative effect on the performance of prediction models and the oversampling method (SMOTE) enhanced the performances of prediction models. Secondly, Oversampling method of balancing datasets is better than using Undersampling methods as the latter had poor performance as a result of the random deletion of useful instances in the datasets. Finally, among the prediction models used in this study, it appeared that Logistic Regression (LR) (RUS: 30.05; SMOTE: 33.51), Naïve Bayes (NB) (RUS: 34.18; SMOTE: 33.05), and Random Forest (RF) (RUS: 29.24; SMOTE: 64.25) with their respective CV values are more stable prediction models and they work well with imbalanced datasets.