Titolo della tesi: Estimating Function Points through conceptuals models
The estimation of software has been an issue in the last decades. Being able to estimate the size and, therefore, the cost in the early stages of the software life cycle has always been an added value of a project. Since the end of the 70s, the function points have been the most used metric for measuring the functional size of an application, and recently, the European Parliament confirmed their importance. The function points have the problem that are not objective because they are estimated starting from natural language, and every analyst can write the requirements differently. Using more formal models, e.g., conceptual model, as input for the function points analysis can mitigate this issue, opening the possibility to automate such an activity. In this thesis, a systematic literature review (SLR) is conducted to discover if and how this approach has been explored in the past.
Using the results of the SLR, some techniques for estimating function points starting from conceptual models, essentially as ER and BPMN, are illustrated. These techniques include multiple linear regression, neural networks, and Large Language Models. Results are promising, but a larger dataset is still needed to validate the first two techniques better.