title: ÐÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ðµ ÑÐµÑ Ð½Ð¾Ð»Ð¾Ð³Ð¸Ð¹ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð¸ Ð¸ÑÐºÑÑÑÑÐ²ÐµÐ½Ð½Ð¾Ð³Ð¾ Ð¸Ð½ÑÐµÐ»Ð»ÐµÐºÑÐ° Ð´Ð»Ñ Ð¾ÑÐµÐ½ÐºÐ¸ ÑÑÑÐ´Ð¾ÐµÐ¼ÐºÐ¾ÑÑÐ¸ (Ð´Ð»Ð¸ÑÐµÐ»ÑÐ½Ð¾ÑÑÐ¸) ÑÐ°Ð±Ð¾Ñ Ð² Ð¿ÑÐ¾ÐµÐºÑÐ°Ñ Ð¿Ð¾ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐµ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÐµÑÐ¿ÐµÑÐµÐ½Ð¸Ñ
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Machine learning and artificial intelligence technologies application for software development project efforts (duration) estimation
Creators: name: ÐÐ°ÑÑÑ, Ð Ð¾Ð¼Ð°Ð½
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2021
Subjects: ÐÑÐºÑÑÑÑÐ²ÐµÐ½Ð½ÑÐ¹ Ð¸Ð½ÑÐµÐ»Ð»ÐµÐºÑ
ÐÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ ÑÐ·ÑÐºÐ¸
ÐÑÑÐ¸ÑÐ»Ð¸ÑÐµÐ»ÑÐ½ÑÐµ Ð¼Ð°ÑÐ¸Ð½Ñ ÑÐ»ÐµÐºÑÑÐ¾Ð½Ð½ÑÐµ
ÐÐ¾Ð´ÐµÐ»Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ
Ð¸Ð½Ð½Ð¾Ð²Ð°ÑÐ¸ÐºÐ°
Ð¸Ð½Ð½Ð¾Ð²Ð°ÑÐ¸Ð¾Ð½Ð½ÑÐ¹ Ð¿ÑÐ¾ÐµÐºÑ
innovatics
innovation project

description: Ð Ð°ÑÑÐ¼Ð¾ÑÑÐµÐ½Ð¾ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ðµ ÑÐµÑÐ½Ð¾Ð»Ð¾Ð³Ð¸Ð¹ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð¸ Ð¸ÑÐºÑÑÑÑÐ²ÐµÐ½Ð½Ð¾Ð³Ð¾ Ð¸Ð½ÑÐµÐ»Ð»ÐµÐºÑÐ° Ðº Ð¿ÑÐ¾ÑÐµÑÑÑ Ð¿ÑÐµÐ´Ð²Ð°ÑÐ¸ÑÐµÐ»ÑÐ½Ð¾Ð³Ð¾ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð´Ð»Ð¸ÑÐµÐ»ÑÐ½Ð¾ÑÑÐ¸ Ð¿ÑÐ¾ÐµÐºÑÐ° Ð¿Ð¾ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐµ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÐµÑÐ¿ÐµÑÐµÐ½Ð¸Ñ Ñ ÑÐµÐ»ÑÑ ÑÐ½Ð¸Ð¶ÐµÐ½Ð¸Ñ Ð¾ÑÐ½Ð¾Ð²Ð½ÑÑ Ð¿ÑÐ¾ÐµÐºÑÐ½ÑÑ ÑÐ¸ÑÐºÐ¾Ð². ÐÐ¿ÑÐµÐ´ÐµÐ»ÐµÐ½Ñ ÑÐ°ÐºÑÐ¾ÑÑ, ÐºÐ¾ÑÐ¾ÑÑÐµ Ð½Ð°Ð¸Ð±Ð¾Ð»ÐµÐµ ÑÐ¸Ð»ÑÐ½Ð¾ Ð²Ð»Ð¸ÑÑÑ Ð½Ð° Ð´Ð»Ð¸ÑÐµÐ»ÑÐ½Ð¾ÑÑÑ ÑÐ°Ð±Ð¾Ñ Ð² Ð¿Ð¾Ð´Ð¾Ð±Ð½ÑÑ Ð¿ÑÐ¾ÐµÐºÑÐ°Ñ. ÐÐ¾Ð´Ð¾Ð±ÑÐ°Ð½Ñ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ñ Ð´Ð»Ñ Ð°Ð½ÑÐ°Ð¼Ð±Ð»ÐµÐ²Ð¾Ð¹ Ð¼Ð¾Ð´ÐµÐ»Ð¸, Ð¾Ð±ÐµÑÐ¿ÐµÑÐ¸Ð²Ð°ÑÑÐ¸Ðµ Ð²ÑÑÐ¾ÐºÐ¾Ðµ ÐºÐ°ÑÐµÑÑÐ²Ð¾ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¾Ð². Ð¡ Ð¿Ð¾Ð¼Ð¾ÑÑÑ ÑÐ·ÑÐºÐ° Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Python Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð¸ Ð¿ÑÐ¾Ð¸Ð·Ð²ÐµÐ´ÐµÐ½Ð° Ð¿ÐµÑÐµÐºÑÐµÑÑÐ½Ð°Ñ Ð¿ÑÐ¾Ð²ÐµÑÐºÐ° ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐ¾Ð² Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ñ ÑÐµÐ»ÑÑ Ð¿Ð¾Ð²ÑÑÐµÐ½Ð¸Ñ Ð´Ð¾ÑÑÐ¾Ð²ÐµÑÐ½Ð¾ÑÑÐ¸ Ð·Ð½Ð°ÑÐµÐ½Ð¸Ð¹ Ð¼ÐµÑÑÐ¸Ðº Ð¾ÑÐµÐ½ÐºÐ¸ ÐºÐ°ÑÐµÑÑÐ²Ð°. ÐÑÐ¾Ð²ÐµÐ´ÐµÐ½Ð° ÐºÐ¾Ð¼Ð¿Ð»ÐµÐºÑÐ½Ð°Ñ Ð¾ÑÐµÐ½ÐºÐ° ÐºÐ°ÑÐµÑÑÐ²Ð° Ð¿Ð¾Ð»ÑÑÐµÐ½Ð½Ð¾Ð¹ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ Ð¿Ð¾Ð»ÑÑÐµÐ½Ð½ÑÑ Ð´Ð»Ñ Ð½ÐµÐµ Ð·Ð½Ð°ÑÐµÐ½Ð¸Ð¹ Ð¾Ð±ÑÐµÐ¿ÑÐ¸Ð½ÑÑÑÑ Ð¼ÐµÑÑÐ¸Ðº Ð´Ð»Ñ Ð¾ÑÐµÐ½ÐºÐ¸ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ. ÐÐ¾Ð´ÐµÐ»Ñ Ð±ÑÐ»Ð° Ð¿ÑÐ¸Ð·Ð½Ð°Ð½Ð° Ð°Ð´ÐµÐºÐ²Ð°ÑÐ½Ð¾Ð¹ Ð¸ Ð¾Ð±ÐµÑÐ¿ÐµÑÐ¸Ð²Ð°ÑÑÐµÐ¹ Ð²ÑÑÐ¾ÐºÑÑ ÑÐ¾ÑÐ½Ð¾ÑÑÑ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¾Ð². ÐÑÑÑÐµÑÑÐ²Ð»ÐµÐ½Ð¾ ÑÑÐ°Ð²Ð½ÐµÐ½Ð¸Ðµ Ð¿Ð¾Ð»ÑÑÐµÐ½Ð½Ð¾Ð¹ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ñ Ð°Ð½Ð°Ð»Ð¾Ð³Ð¸ÑÐ½ÑÐ¼Ð¸ Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ Ð¸Ð·Ð²ÐµÑÑÐ½ÑÑ Ð·Ð½Ð°ÑÐµÐ½Ð¸Ð¹ Ð¼ÐµÑÑÐ¸Ðº ÐºÐ°ÑÐµÑÑÐ²Ð°. ÐÐ¾Ð»ÑÑÐµÐ½Ñ ÑÐ¸ÑÐ»ÐµÐ½Ð½ÑÐµ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÑ, ÑÐ°ÑÐ°ÐºÑÐµÑÐ¸Ð·ÑÑÑÐ¸Ðµ Ð¿Ð¾Ð»ÑÑÐµÐ½Ð½ÑÐ¹ Ð²ÑÐ¸Ð³ÑÑÑ Ð² ÑÐ¾ÑÐ½Ð¾ÑÑÐ¸ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¾Ð² Ð¿Ð¾Ð»ÑÑÐµÐ½Ð½Ð¾Ð¹ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð¿Ð¾ ÑÑÐ°Ð²Ð½ÐµÐ½Ð¸Ñ Ñ Ð°Ð½Ð°Ð»Ð¾Ð³Ð°Ð¼Ð¸. ÐÐ¿ÑÐµÐ´ÐµÐ»ÐµÐ½Ñ Ð³ÑÐ°Ð½Ð¸ÑÑ Ð¿ÑÐ¸Ð¼ÐµÐ½Ð¸Ð¼Ð¾ÑÑÐ¸ Ð¿Ð¾Ð»ÑÑÐµÐ½Ð½Ð¾Ð¹ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð² Ð¾ÑÐ³Ð°Ð½Ð¸Ð·Ð°ÑÐ¸ÑÑ, Ð·Ð°Ð½Ð¸Ð¼Ð°ÑÑÐ¸ÑÑÑ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ¾Ð¹ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÐµÑÐ¿ÐµÑÐµÐ½Ð¸Ñ. ÐÐ°Ð½Ñ ÑÐµÐºÐ¾Ð¼ÐµÐ½Ð´Ð°ÑÐ¸Ð¸ Ð¿Ð¾ Ð²Ð½ÐµÐ´ÑÐµÐ½Ð¸Ñ Ð¸ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸Ñ Ð¿ÑÐµÐ´Ð»Ð¾Ð¶ÐµÐ½Ð½Ð¾Ð¹ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð² Ð¸Ñ Ð¿ÑÐ¾Ð¼ÑÑÐ»ÐµÐ½Ð½ÑÑ Ð¿ÑÐ¾ÑÐµÑÑÐ°Ñ.
description: The application of machine learning and artificial intelligence technologies for the project duration predicting aimed to avoiding main software development project risks was considered. The factors that have huge influence on the duration of work in such projects have been identified. Algorithms for the ensemble model have been selected that provide high quality forecasts. Using the Python programming language, the model was trained, and the forecasting results were cross-validated in order to increase the reliability of the values of the quality assessment metrics. A comprehensive assessment of the quality of the model was performed using the values of conventional metrics for evaluating machine learning models obtained for it. The model was found to be adequate and fast in forecasting accuracy. A comparison to similar models using the known values of quality metrics was performed. Numerical characteristics were received that characterize the obtained gain in the accuracy of the forecasts of the model in comparison with analogs. The applicability of the model in organizations from software development domain has been determined. Recommendations are given for the implementation and use of the proposed model in their industrial processes.