Time Series Forecasting during Software Project State Analysis

Anton Romanov (), Nadezhda Yarushkina, Alexey Filippov, Pavel Sergeev, Ilya Andreev and Sergey Kiselev
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Anton Romanov: Department of Information Systems, Ulyanovsk State Technical University, Severny Venets Str., 32, Ulyanovsk 432027, Russia
Nadezhda Yarushkina: Department of Information Systems, Ulyanovsk State Technical University, Severny Venets Str., 32, Ulyanovsk 432027, Russia
Alexey Filippov: Department of Information Systems, Ulyanovsk State Technical University, Severny Venets Str., 32, Ulyanovsk 432027, Russia
Pavel Sergeev: Department of Information Systems, Ulyanovsk State Technical University, Severny Venets Str., 32, Ulyanovsk 432027, Russia
Ilya Andreev: Department of Information Systems, Ulyanovsk State Technical University, Severny Venets Str., 32, Ulyanovsk 432027, Russia
Sergey Kiselev: Department of Information Systems, Ulyanovsk State Technical University, Severny Venets Str., 32, Ulyanovsk 432027, Russia

Mathematics, 2023, vol. 12, issue 1, 1-16

Abstract: Repositories of source code and their hosting platforms are important data sources for software project development and management processes. These sources allow for the extraction of historical data points for the product development process evaluation. Extracted data points reflect the previous development experience and allow future planning and active development tracking. The aim of this research is to create a predictive approach to control software development based on a time series extracted from repositories and hosting platforms. This article describes the method of extracting parameters from repositories, the approach to creating time series models and forecasting their behavior. Also, the article represents the proposed approach for software project analyses based on fuzzy logic principles. The novelty of this approach is the ability to perform an expert evaluation of different stages of software product development based on the forecasted values of interested parameters and a fuzzy rule base.

Keywords: time series; predicting; forecasting; neural networks (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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