Predictive Analytics and Software Defect Severity: A Systematic Review and Future Directions

Olaleye, Taiwo; Arogundade, Oluwasefunmi ‘Tale; Misra, Sanjay; Abayomi‐Alli, Adebayo; Köse, Utku

doi:10.1155/2023/6221388

Cited by 8 publications

(3 citation statements)

References 58 publications

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“…Classifiers are used to categorize data into different classes. The main goal of a classifier is to learn a mapping from input features to predefined output classes, which enables the algorithm to predict the class of new unseen instances based on their features [78], [79]. Many researchers have implemented various single and ensemble classifiers to improve the prediction accuracy of the proposed models for SDP [31],…”

Section: Embedded Methodsmentioning

confidence: 99%

Analysis of Feature Selection Methods in Software Defect Prediction Models

Ali,

Mazhar,

Shahzad

et al. 2023

IEEE Access

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Improving software quality by proactively detecting potential defects during development is a major goal of software engineering. Software defect prediction plays a central role in achieving this goal. The power of data analytics and machine learning allows us to focus our efforts where they are needed most. A key factor in the success of software fault prediction is selecting relevant features and reducing data dimensionality. Feature selection methods contribute by filtering out the most critical attributes from a plethora of potential features. These methods have the potential to significantly improve the accuracy and efficiency of fault prediction models. However, the field of feature selection in the context of software fault prediction is vast and constantly evolving, with a variety of techniques and tools available. Based on these considerations, our systematic literature review conducts a comprehensive investigation of feature selection methods used in the context of software fault prediction. The research uses a refined search strategy involving four reputable digital libraries, including IEEE Explore, Science Direct, ACM Digital Library, and Springer Link, to provide a comprehensive and exhaustive review through a rigorous analysis of 49 selected primary studies from 2014. The results highlight several important issues. First, there is a prevalence of filtering and hybrid feature selection methods. Second, single classifiers such as Naïve Bayes, Support Vector Machine, and Decision Tree, as well as ensemble classifiers such as Random Forest, Bagging, and AdaBoost are commonly used. Third, evaluation metrics such as area under the curve, accuracy, and F-measure are commonly used for performance evaluation. Finally, there is a clear preference for tools such as WEKA, MATLAB, and Python. By providing insights into current trends and practices in the field, this study offers valuable guidance to researchers and practitioners to make informed decisions to improve software fault prediction models and contribute to the overall improvement of software quality.

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Section: Embedded Methodsmentioning

confidence: 99%

Analysis of Feature Selection Methods in Software Defect Prediction Models

Ali,

Mazhar,

Shahzad

et al. 2023

IEEE Access

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“…EDA would reveal an in-depth analysis of the data which would aid better understanding of the gene attributes as they contribute to the positive status of the myeloid leukemia or the acute lymphoblastic leukemia cancer conditions. EDA has proven to be an indispensable technique needed to be implemented prior to any machine learning-based predictive analysis (Olaleye, et al, 2023). The techniques involved include:…”

Section: Exploratory Data Analysismentioning

confidence: 99%

Improving Acute Leukemia Classification through Recursive Feature Elimination and Multilayer Perceptron Analysis of Gene Expression Data

Ogunbiyi,

Adegoke,

Oluwatosin

et al. 2024

Int. J. Data. Science.

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This study presents an approach to improving the classification of acute leukemia subtypes using gene expression data analysis. Leveraging Recursive Feature Elimination (RFE) as a feature selection technique and Multilayer Perceptron (MLP) as the predictive modeling framework, this research aims to identify the most influential genes for distinguishing between Acute Lymphoblastic Leukemia (ALL) and Acute Myeloid Leukemia (AML) cases. RFE systematically ranks and selects the most discriminative gene attributes, while MLP constructs a predictive model based on these attributes. The results demonstrate the effectiveness of this combined approach, achieving precision, accuracy, F1-Score, and recall rates of approximately 99% for leukemia subtype classification. Furthermore, specific genes contributing most to the model's predictive power and shedding light on potential biomarkers for leukemia diagnosis were identified. This research underscores the significance of RFE and MLP in the analysis of gene expression data and their potential impact on clinical decision-making in the field of oncology.

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“…The organization involved in development of software products are required to deal with various risk and threats in its development process irrespective of the size of an organization and one such important risk factor is associated with software defects [1]. There are various types of software defects viz.…”

Section: Introductionmentioning

confidence: 99%

Insights of effectivity analysis of learning-based approaches towards software defect prediction

Rai,

Arcot Prashant

2024

IJECE

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Software defect prediction is one of the essential sets of operation towards mitigating issues of risk management in software development known to contribute towards enhancing the quality of software. There is evolution of various methodologies towards resolving this issue while learning-based methodology is witnessed to be the most dominant contributor. The problem identified is that there are yet many unsolved queries associated with practical viability of such learning-based approach adoption in software quality management. Proposed approaches discussed in this paper contributes towards mitigating this challenge by introducing a simplified, compact, and crisp analysis of effectiveness associated with learning-based schemes. The paper presents its major findings of effectivity analysis of machine learning, deep learning, hybrid, and other miscellaneous approaches deployed for fault prediction followed by highlighting research trend. The major findings infer that feature selection, data imbalance, interpretability, and in adequate involvement of context are prime gaps in existing methods. The paper also contributes towards research gap as well as essential learning outcomes of present review work.

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Predictive Analytics and Software Defect Severity: A Systematic Review and Future Directions

Cited by 8 publications

References 58 publications

Analysis of Feature Selection Methods in Software Defect Prediction Models

Analysis of Feature Selection Methods in Software Defect Prediction Models

Improving Acute Leukemia Classification through Recursive Feature Elimination and Multilayer Perceptron Analysis of Gene Expression Data

Insights of effectivity analysis of learning-based approaches towards software defect prediction

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