An Improved Firefly-Based Feature Selection Method for Software Fault Identification and Classification

doi:10.23940/ijpe.23.11.p5.744752

Abstract

Abstract: With the increase in the number of software components, it becomes difficult to identify the faults manually. Automated fault detection has gained much attention in the last couple of years. This paper presents an improved firefly algorithm for detecting and evaluating software faults by incorporating an improved firefly algorithm for feature selection. The tuned algorithm has a better classification rate due to its novel fitness function. The objective is to minimize the losses and maximize the classification accuracy. The proposed algorithm is also compared with other state-of-the-art algorithms and has shown significant improvement in evaluating quantitative parameters. The proposed work has been evaluated for precision, recall and f-measure and is significantly better than the comparisons.

Key words: firefly, fitness function, optimization, software fault prediction

Ashima Arya and Sanjay Kumar Malik. An Improved Firefly-Based Feature Selection Method for Software Fault Identification and Classification [J]. Int J Performability Eng, 2023, 19(11): 744-752.

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