Software fault prediction with imbalanced datasets using SMOTE-Tomek sampling technique and Genetic Algorithm models

Gupta, Mansi; Rajnish, Kumar; Bhattacharjee, Vandana

doi:10.1007/s11042-023-16788-7

Cited by 2 publications

(1 citation statement)

References 43 publications

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“…Genetic algorithms are commonly employed in optimization situations where conventional approaches may be unfeasible or inefficient, particularly in intricate optimization terrains with numerous local optima. Gupta et al applied GAs for optimizing machine learning models for software fault prediction [31].…”

Section: Initializationmentioning

confidence: 99%

Genetic Algorithm-Based Optimization of Clustering Algorithms for the Healthy Aging Dataset

Kouser,

Priyam,

Gupta

et al. 2024

Applied Sciences

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Clustering is a crucial and, at the same time, challenging task in several application domains. It is important to incorporate the optimum feature finding into our clustering algorithms for better exploration of features and to draw meaningful conclusions, but this is difficult when there is no or little information about the importance or relevance of features. To tackle this task in an efficient manner, we employ the natural evolution process inherent in genetic algorithms (GAs) to find the optimum features for clustering the healthy aging dataset. To empirically verify the findings, genetic algorithms were combined with a number of clustering algorithms, including partitional, density-based, and agglomerative clustering algorithms. A variant of the popular KMeans algorithm, named KMeans++, gave the best performance on all performance metrics when combined with GAs.

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Section: Initializationmentioning

confidence: 99%

Genetic Algorithm-Based Optimization of Clustering Algorithms for the Healthy Aging Dataset

Kouser,

Priyam,

Gupta

et al. 2024

Applied Sciences

Self Cite

View full text Add to dashboard Cite

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Leveraging meta-heuristic algorithms for effective software fault prediction: a comprehensive study

Dang,

Wang

2024

J. Eng. Appl. Sci.

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In large-scale software development, the increasing complexity of software products poses a daunting challenge to maintaining software quality. Given this challenge, software fault prediction (SFP) is a critical endeavor for effective budgeting and refinement of the testing process. Quantitative insights into software quality gained through measurements are crucial in enabling accurate SFP. With the proliferation of software in various fields, ensuring software reliability throughout the software life cycle has become paramount. Anticipating software bugs, which have the potential to reduce software maintenance costs dramatically, is a key approach to improving software reliability. In this regard, using nature-inspired metaheuristic algorithms is promising because of their ability to predict future conditions and identify software anomalies. This study examines the potential of various meta-heuristic algorithms, particularly particle swarm optimization, genetic, ant colony optimization, cuckoo search, lion optimization, firefly, moth-flame, whale optimization, and artificial bee colony algorithms, in addressing the SFP challenge. The study outlines the challenging problems, compares approaches based on fundamental variables, and offers suggestions for future studies, providing a comprehensive and systematic analysis of these algorithms in the context of SFP.

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Software fault prediction with imbalanced datasets using SMOTE-Tomek sampling technique and Genetic Algorithm models

Cited by 2 publications

References 43 publications

Genetic Algorithm-Based Optimization of Clustering Algorithms for the Healthy Aging Dataset

Genetic Algorithm-Based Optimization of Clustering Algorithms for the Healthy Aging Dataset

Leveraging meta-heuristic algorithms for effective software fault prediction: a comprehensive study

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