A Novel Approach to Improve Software Defect Prediction Accuracy Using Machine Learning

Mehmood, Iqra; Shahid, Sidra; Hussain, Hameed; Khan, I.B.; Ahmad, Shafiq; Rahman, Shahid; Ullah, Najeeb; Huda, Shamsul

doi:10.1109/access.2023.3287326

Cited by 7 publications

(1 citation statement)

References 55 publications

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“…1 Detecting and resolving process of identifying and fixing defects contributes to a significant portion of the total cost of software development and deployment. 2,29 One key aspect of defect detection is feature selection (FS), which involves identifying the most relevant and informative features from a large set of available software metrics and attributes. 3,30 FS helps to reduce dimensionality, eliminate irrelevant or redundant features, and improve the performance.…”

Section: Introductionmentioning

confidence: 99%

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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