Software defect prediction using hybrid techniques: a systematic literature review

Malhotra, Ruchika; Chawla, Sonali; Sharma, Anjali

doi:10.1007/s00500-022-07738-w

Cited by 10 publications

(1 citation statement)

References 109 publications

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“…A systematic literature review to provide an exhaustive overview of software defect prediction techniques employing hybrid methodologies 37 is conducted. Their study encompasses a thorough examination of existing research in this domain, summarizing a diverse array of hybrid approaches utilized for software defect prediction.…”

Section: In-depth Review Of Existing Machine Learning Models Used For...mentioning

confidence: 99%

Feature importance feedback with Deep Q process in ensemble-based metaheuristic feature selection algorithms

Potharlanka,

2024

Sci Rep

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Feature selection is an indispensable aspect of modern machine learning, especially for high-dimensional datasets where overfitting and computational inefficiencies are common concerns. Traditional methods often employ either filter, wrapper, or embedded approaches, which have limitations in terms of robustness, computational load, or capability to capture complex interactions among features. Despite the utility of metaheuristic algorithms like Particle Swarm Optimization (PSO), Firefly Algorithm (FA), and Whale Optimization (WOA) in feature selection, there still exists a gap in efficiently incorporating feature importance feedback into these processes. This paper presents a novel approach that integrates the strengths of PSO, FA, and WOA algorithms into an ensemble model and further enhances its performance by incorporating a Deep Q-Learning framework for relevance feedbacks. The Deep Q-Learning module intelligently updates feature importance based on model performance, thereby fine-tuning the selection process iteratively. Our ensemble model demonstrates substantial gains in effectiveness over traditional and individual metaheuristic approaches. Specifically, the proposed model achieved a 9.5% higher precision, an 8.5% higher accuracy, an 8.3% higher recall, a 4.9% higher AUC, and a 5.9% higher specificity across multiple software bug prediction datasets and samples. By resolving some of the key issues in existing feature selection methods and achieving superior performance metrics, this work paves the way for more robust and efficient machine learning models in various applications, from healthcare to natural language processing scenarios. This research provides an innovative framework for feature selection that promises not only superior performance but also offers a flexible architecture that can be adapted for a variety of machine learning challenges.

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Section: In-depth Review Of Existing Machine Learning Models Used For...mentioning

confidence: 99%

Feature importance feedback with Deep Q process in ensemble-based metaheuristic feature selection algorithms

Potharlanka,

2024

Sci Rep

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Optimizing beyond boundaries: empowering the salp swarm algorithm for global optimization and defective software module classification

Kassaymeh,

Al-Betar,

Rjoubd

et al. 2024

Neural Comput & Applic

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Survey of software defect prediction features

Qiu,

et al. 2024

Neural Comput & Applic

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Software defect prediction using hybrid techniques: a systematic literature review

Cited by 10 publications

References 109 publications

Feature importance feedback with Deep Q process in ensemble-based metaheuristic feature selection algorithms

Feature importance feedback with Deep Q process in ensemble-based metaheuristic feature selection algorithms

Optimizing beyond boundaries: empowering the salp swarm algorithm for global optimization and defective software module classification

Survey of software defect prediction features

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