Year: 2024
Type: article
Abstract: Software defect prediction is an important issue in the process of software development and maintenance, which is related to the overall success or failure of software. This is because early software failure prediction can improve software quality, reliability and efficiency, and reduce software cost. However, developing robust defect prediction models is a challenging task and many techniques have been proposed in the literature. In this paper, a software defect prediction model based on Novel Hybrid Genetics Software Defect Prediction (NHGSDP) is proposed. The supervised NHGSDP algorithm has been used to predict future software failures based on historical data. The evaluation process shows that the NHGSDP algorithm can be used effectively with high accuracy. The collected results show that the NHGSDP method has better performance.
Source: THE SCIENTIFIC TEMPER
Authors: Rajeev P. R., K. Aravinthan
Institution: Bharathidasan University
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4403141158