Holistic Parameter Optimization for Software Defect Prediction

Lee, Jae‐Wook; Choi, Jiho; Ryu, Duksan; Kim, Suntae

doi:10.1109/access.2022.3211978

Cited by 6 publications

(3 citation statements)

References 51 publications

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“…For analyzing superior performance of this research article, the quantitative analysis is performed by implementing different SDD models such as proposed model, HS‐CSDT, 9 LASSO‐SVM, 10 PC‐SVM, 11 E‐RF‐ADASYN, 13 GA, 14 and PSO 15…”

Section: Resultsmentioning

confidence: 99%

“…For analyzing superior performance of this research article, the quantitative analysis is performed by implementing different SDD models such as proposed model, HS-CSDT, 9 LASSO-SVM, 10 PC-SVM, 11 E-RF-ADASYN, 13 GA, 14 and PSO. 15 Figure 5 demonstrates an accuracy analysis by using different comparison techniques such as HS-CSDT, LASSO-SVM, PC-SVM, proposed, E-RF-ADASYN, GA, and PSO.…”

Section: Comparative Analysismentioning

confidence: 99%

“…They use a search algorithm to explore different feature subsets and select the one that optimizes the performance of the defect detection model. The Cost‐Sensitive Decision Tree based Harmony Search (HS‐CSDT) method was represented by Lee et al 9 for the prediction of software defects. Due to the normalization, class weight and model hyperparameters, this model was affected by the impact on performance.…”

Section: Related Workmentioning

confidence: 99%

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Mutation boosted salp swarm optimizer meets rough set theory: A novel approach to software defect detection

Sekaran,

Lawrence

2024

Trans Emerging Tel Tech

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Software defect detection (SDD) is crucial to ensure the reliability of software systems and identify defects in classification. One of the key challenges in defect detection is to select more informative and relevant features from the vast pool of available software metrics. A novel approach is proposed in this paper that leverages mutation boosted salp swarm optimizer (MBSSO) and Rough Set Theory for feature selection (FS) in SDD. It efficiently explores the search space and incorporates a mutation boosting mechanism to overcome local optima. Rough Set Theory provides a formal framework for analyzing feature relevance and dependency, while MBSSO optimizes the search for the optimal feature subset. The proposed approach involves encoding the feature subsets using binary representation and designing a fitness function. The MBSSO algorithm explores the feature space, iteratively improving the feature subset based on the fitness function. The selected feature subset is then used to train a defect detection model using the Kernel Extreme Learning Machine (KELM) algorithm. The experimental validation is performed by Project Repository for Software Engineering (PROMISE) dataset and compared the performance of proposed approach against other FS methods and baselines. The experimental validation demonstrates that the proposed approach achieves superior performance and selected feature subset improves the accuracy and efficiency. This research contributes to the advancement of SDD by providing an effective and efficient FS technique using MBSSO and Rough Set Theory.

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