Software Defect Prediction through Neural Network and Feature Selections

Alkhasawneh, Mutasem Sh.

doi:10.1155/2022/2581832

Cited by 5 publications

(2 citation statements)

References 44 publications

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“…Innovative approaches such as gated hierarchical LSTM networks were proposed in [ 28 ], though computational complexity remained a concern, particularly for extensive projects. Authors in [ 29 ] developed an effective model using AI-based techniques on the NASA MDP repository dataset, achieving promising results with adaptive neuron fuzzy inference system, SVM, and ANN. The FILTER technique proposed in [ 30 ] improved SVM-based SDP accuracy, although with sensitivity to hyperparameter tuning and overfitting.…”

Section: Literature Reviewmentioning

confidence: 99%

A trustworthy hybrid model for transparent software defect prediction: SPAM-XAI

Mustaqeem,

Mustajab,

Alam

et al. 2024

PLoS ONE

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Maintaining quality in software development projects is becoming very difficult because the complexity of modules in the software is growing exponentially. Software defects are the primary concern, and software defect prediction (SDP) plays a crucial role in detecting faulty modules early and planning effective testing to reduce maintenance costs. However, SDP faces challenges like imbalanced data, high-dimensional features, model overfitting, and outliers. Moreover, traditional SDP models lack transparency and interpretability, which impacts stakeholder confidence in the Software Development Life Cycle (SDLC). We propose SPAM-XAI, a hybrid model integrating novel sampling, feature selection, and eXplainable-AI (XAI) algorithms to address these challenges. The SPAM-XAI model reduces features, optimizes the model, and reduces time and space complexity, enhancing its robustness. The SPAM-XAI model exhibited improved performance after experimenting with the NASA PROMISE repository’s datasets. It achieved an accuracy of 98.13% on CM1, 96.00% on PC1, and 98.65% on PC2, surpassing previous state-of-the-art and baseline models with other evaluation matrices enhancement compared to existing methods. The SPAM-XAI model increases transparency and facilitates understanding of the interaction between features and error status, enabling coherent and comprehensible predictions. This enhancement optimizes the decision-making process and enhances the model’s trustworthiness in the SDLC.

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Section: Literature Reviewmentioning

confidence: 99%

A trustworthy hybrid model for transparent software defect prediction: SPAM-XAI

Mustaqeem,

Mustajab,

Alam

et al. 2024

PLoS ONE

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“…This process streamlines the process and reduces the traditional back-and-forth between development and QA. This shift promotes a more efficient and cost-effective software development life cycle [11]. A visual representation highlighting the reduced feedback loop when the SDP model is in place is shown in Figure2.…”

Section: Introductionmentioning

confidence: 99%

Software Defect Prediction Using an Intelligent Ensemble-Based Model

Ali,

Mazhar,

Arif

et al. 2024

IEEE Access

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Software defect prediction plays a crucial role in enhancing software quality while achieving cost savings in testing. Its primary objective is to identify and send only defective modules to the testing stage. This research introduces an intelligent ensemble-based software defect prediction model that combines diverse classifiers. The proposed model employs a two-stage prediction process to detect defective modules. In the first stage, four supervised machine learning algorithms are employed: Random Forest, Support Vector Machine, Naïve Bayes, and Artificial Neural Network. These algorithms are optimized through iterative parameter optimization to achieve the highest accuracy possible. In the second stage, the predictive accuracy of the individual classifiers is integrated into a voting ensemble to make the final predictions. This ensemble approach further improves the accuracy and reliability of the defect predictions. Seven historical defect datasets from the NASA MDP repository, namely CM1, JM1, MC2, MW1, PC1, PC3, and PC4, were utilized to implement and evaluate the proposed defect prediction system. The results demonstrate that each dataset's proposed intelligent system achieved remarkable accuracy, outperforming twenty state-of-the-art defect prediction techniques, including base classifiers and ensemble methods.

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