Feature selection using Tasmanian devil optimisation algorithm for software fault prediction

Software quality is improved through early software flaw identification, which is made possible by software fault prediction (SFP). Early software problem identification reduces the amount of money and work needed to fix those vulnerabilities. This paper presents a novel FSTDO model, a feature selection (FS) approach built upon Tasmanian devil optimisation (TDO). The proposed FSTDO helps to select the optimal feature subset that yields maximum classification accuracy. The proposed FSTDO algorithm is juxtaposed with four well-known FS algorithms, such as FS using ACO, FS using GA, FS using PSO and FS using DE. Four popular classifiers KNN, NB, DT, and QDA, were used with the FSTDO to assess the quality of its outcomes. The experimental outcomes reveal that the proposed FSTDO has more potential in comparison with the competing FS models as it delivers enhanced performance in terms of accuracy, and the selection of optimal features is also significantly viable.