A Comparative Study of Three Test Effort Estimation Methods

Effort estimation is a big challenge for those trying to manage a project. In a software development project, testing is essential to assure product quality. However, it is a time consuming activity, and its work must be estimated for successful project execution. In our research, we concentrate our efforts on comparing some known methods of test effort estimation. So, this paper aims to analyze three different test effort estimation methods and compare them with the effort spent on real projects. Firstly we compare two widely used effort estimation methods: Test Point Analysis (TPA) and Use Case Points (UCP). Thereafter, we create an artificial neural network (ANN) based on the TPA, trained to estimate the testing work in software development projects, and compare it with pure TPA, to check which of them results in better estimates. Analyzing the experiment results, we concluded that the neural networks gave the best results, followed by TPA and then UCP.