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This research paper investigates the impact of classification models on bug prediction performance and explores the use of bio-inspired optimization techniques to enhance model results. Through experiments, it is demonstrated that applying bio-inspired algorithms improves the accuracy of fault prediction models. The evaluation is based on multiple performance metrics and the results show that KNN with BACO (Binary Ant Colony Optimization) generally outperform the other models in terms of accuracy. The BACO-KNN fault prediction model attains the accuracy of 96.39% surpassing the previous work.<\/jats:p>","DOI":"10.3233\/idt-230427","type":"journal-article","created":{"date-parts":[[2024,3,22]],"date-time":"2024-03-22T15:58:32Z","timestamp":1711123112000},"page":"1355-1376","source":"Crossref","is-referenced-by-count":1,"title":["Development of optimised software fault prediction model using machine learning"],"prefix":"10.1177","volume":"18","author":[{"given":"Shallu","family":"Juneja","sequence":"first","affiliation":[{"name":"Computer Science Engineering Department, Punjabi University, Patiala, India"},{"name":"Computer Science Engineering Department, Maharaja Agrasen Institute of Technology, Delhi, India"}]},{"given":"Gurjit Singh","family":"Bhathal","sequence":"additional","affiliation":[{"name":"Computer Science Engineering Department, Punjabi University, Patiala, India"}]},{"given":"Brahmaleen K.","family":"Sidhu","sequence":"additional","affiliation":[{"name":"Computer Science Engineering Department, Punjabi University, Patiala, India"}]}],"member":"179","reference":[{"issue":"5","key":"10.3233\/IDT-230427_ref1","doi-asserted-by":"crossref","first-page":"826","DOI":"10.1109\/TSMC.2016.2521840","article-title":"Fuzzy Rule-Based Approach for Software Fault Prediction","volume":"47","author":"Singh","year":"2017","journal-title":"IEEE Trans Syst Man Cybern Syst."},{"key":"10.3233\/IDT-230427_ref2","doi-asserted-by":"crossref","first-page":"2844","DOI":"10.1109\/ACCESS.2017.2785445","article-title":"A Framework for Software Defect Prediction and Metric Selection","volume":"6","author":"Huda","year":"2017","journal-title":"IEEE Access."},{"key":"10.3233\/IDT-230427_ref3","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.ins.2018.02.027","article-title":"A novel approach for software defect prediction through hybridizing gradual relational association rules with artificial neural networks","volume":"441","author":"Miholca","year":"2018","journal-title":"Inf Sci (N Y)."},{"key":"10.3233\/IDT-230427_ref4","doi-asserted-by":"crossref","first-page":"9847","DOI":"10.1007\/s10586-018-1696-z","article-title":"Deep neural network based hybrid approach for software defect prediction using software metrics","volume":"22","author":"Manjula","year":"2019","journal-title":"Cluster Comput."},{"key":"10.3233\/IDT-230427_ref5","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1007\/s10586-018-1730-1","article-title":"Software defect prediction techniques using metrics based on neural network classifier","volume":"22","author":"Jayanthi","year":"2019","journal-title":"Cluster Comput."},{"issue":"3","key":"10.3233\/IDT-230427_ref6","doi-asserted-by":"crossref","first-page":"885","DOI":"10.1109\/TR.2018.2847353","article-title":"Ridge and Lasso Regression Models for Cross-Version Defect Prediction","volume":"67","author":"Yang","year":"2018","journal-title":"IEEE Trans Reliab."},{"key":"10.3233\/IDT-230427_ref7","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.compeleceng.2018.02.043","article-title":"Empirical analysis of change metrics for software fault prediction","volume":"67","author":"Choudhary","year":"2018","journal-title":"Computers and Electrical Engineering."},{"key":"10.3233\/IDT-230427_ref8","doi-asserted-by":"crossref","first-page":"8041","DOI":"10.1109\/ACCESS.2020.2964321","article-title":"Enhanced binary moth flame optimization as a feature selection algorithm to predict software fault prediction","volume":"8","author":"Tumar","year":"2020","journal-title":"IEEE Access."},{"key":"10.3233\/IDT-230427_ref9","doi-asserted-by":"crossref","unstructured":"Pan C, Lu M, Xu B, Gao H. An improved CNN model for within-project software defect prediction. Applied Sciences (Switzerland). 2019 May 1; 9(10).","DOI":"10.3390\/app9102138"},{"key":"10.3233\/IDT-230427_ref10","doi-asserted-by":"crossref","unstructured":"Fan G, Diao X, Yu H, Yang K, Chen L. Software Defect Prediction via Attention-Based Recurrent Neural Network. 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