ProRE: An ACO- based programmer recommendation model to precisely manage software bugs

Abstract

The process of assigning bugs to particular programmers is called bug assignment in software engineer ing. The programmer can fix the bugs by applying their knowledge. This research article presents an Ant colony optimization-based programmer recommendation model (ProRE) to manage software bugs pre cisely. The proposed ProRE model performs four operations: data pre-processing, i.e., data Pre processing, extraction, feature selection, and programmer recommendation process. The feature selection stage utilized the Ant colony optimization (ACO) method to determine the appropriate subsets of features from all features. In the programmer recommendation stages, three programmer metrics, i.e., function ality ranking, bug occurrence, and mean Bug fixing time, are utilized for the recommendation assignment. The effectiveness of the proposed programmer recommendation system is assessed using datasets from Mozilla, Eclipse, Firefox, JBoss, and OpenFOAM. It is noted that the proposed model offers a better recom mendation strategy over the other available systems. The simulation findings of the proposed ProRE model are also analyzed with well-known available ML methods, i.e., SVM, NB, and C4.5. It is observed that the recommendation results have improved by an average of 4%, 10%, and 12% compared to SVM, C4.5, and NB-based models. Programmer recommendation software is implemented for allocating the bugs to accurate programmers. It has been found that the proposed ProRE model generates more opti mistic outcomes than existing ones.