Analysis: The application of different Machine Learning Techniques for Software effort Estimation | Original Article
The purpose of providing estimates in software projects is to aid professionals in making more accurate predictions about the costs of software development, which in turn affects the efficacy of the software process's preparatory and operational activities. However, software development organizations sometimes struggle to provide estimates that accurately reflect the true work required to carry out the tasks associated with a software project. Despite the fact that methods for estimating labor have been presented in the literature, doing so remains difficult. Machine learning (ML) methods have recently been used to address this issue. With the use of ML methods, datasets containing the results of previously completed projects may be used to generate more accurate estimates. In this research, we have explored the application of machine learning methods to the problem of estimating the time required to develop software. The need for software projects is growing, necessitating the constant evolution of both computer software and hardware. Competition among businesses to provide highquality goods in a timely manner at a reasonable price has intensified as demand for software projects has grown. The purpose of this study is to carefully examine ML models by looking at them from four different angles ML method, estimate accuracy, model comparison, and estimation context. Researchers will find this paper's review of effort estimate using machine learning methods helpful in charting the course of future work on the use of machine learning to the estimation of software development efforts.