ОЦІНКА ЗУСИЛЬ ДЛЯ МІГРАЦІЇ МІЖ АРХІТЕКТУРНИМИ ВАРІАНТАМИ DOMAIN-DRIVEN DESIGN

Context. The article addresses the issue of effort estimation of migration among variations of DDD architecture using a method based on specifications of requirements to increase the predictability of the software migration process.Objective. The goal of the work is to propose an effective method of effort estimation based on Use Case analysis.Method. First, a set of rules for rigorous Use Case description adapted for software effort estimation needs are provided. Second, the modified Use Case metamodel and the method of use cases classification based on frame-based knowledge representation model are also suggested. The rigorous description allows to make the estimation of the use cases more precise, using FUSP method, and to build individual predictors for each class of use cases. Thirdly, the method uses historical data taken from previous iterations of the same project, and is based on three trends (optimistic, pessimistic and mean based trend).Results. The result is the collection of functions used to predict the effort required for the next iteration (measured in personhours) for each class of use cases.Conclusions. FUSP method was adapted for task of gaining greater prediction accuracy of effort estimation for migration among variations of DDD architecture using a methodology based on specifications of requirements. The set of conditions to form the Use Case description rules adapted for software migration effort estimation needs is developed. The modified Use Case metamodel and the method of use cases classification based on frame-based knowledge representation model are suggested. It was proposed algorithm for building the individual predictors of each class and for corresponding effort estimation. The coefficient of FUSP personhours transformation is based on three trends achieved and updated considering the results from previous iterations: the most pessimistic prediction is based on the upper bound, the lower bound predictor plays the role of the optimistic predictor, and the main trend is the meaning among these. The coefficients are used to predict the effort in person-hours required for the next iteration for each class of use cases. The results of experiment, conducted using the test RTP project for this class of software, showed that MMRE for the proposal method is 0.0343, and for the standard method – 0.1094. The obtained results evidence that the classification of use cases along with their rigorous description according to provided rules, and modification of the method by separating prediction logic in accordance with the use case classes makes the prediction more accurate and can be effectively used for effort estimation for DDD architectural variations migration.