Implementation of artificial intelligence methods to the processes of automated metrics forecasting for software systems development projects

Abstract

The subject matter of the article is the process of automated forecasting of project metrics for software development projects that are typically subject to evaluation. It also covers AI methods and models that can be used to generate basic roadmap templates and operational work lists, as well as alternative estimates depending on the context. The goal of the work is to study the foundations of creating a system for automated predicting of alternative evaluations of a software product. The following tasks were solved in the article: determining the stages of evaluation related to the assessment of alternatives in the life cycle of a software development project; investigating the problems of predicting and the main factors affecting the final indicators; exploring predicting methods that can be used to implement multivariate assessment of a software development project. The following methods are used: methods for evaluating and predicting labor costs in software development projects, machine and deep learning, and assessing their effectiveness for solving the prediction problem. The following results were obtained: the conceptual foundations for creating automated evaluation and prediction systems based on the analysis of the effectiveness of selected machine learning models were determined, the areas of application for artificial intelligence methods in the process of evaluating software development project indicators were identified, the performance indicators of various machine learning models were assessed based on certain model evaluation parameters that characterize prediction accuracy; a conceptual architecture of a project roadmap generation software tool based on the GPT language model was proposed. Conclusions: the use of machine and deep learning methods can improve the accuracy of predictions for key project indicators, provide the possibility of flexible generation of various alternative roadmap templates and operational work lists, making the planning and management process more efficient and transparent under conditions of high uncertainty of project requirements.