Time Series Forecasting during Software Project State Analysis

Author:

Romanov Anton1ORCID,Yarushkina Nadezhda1,Filippov Alexey1ORCID,Sergeev Pavel1ORCID,Andreev Ilya1,Kiselev Sergey1

Affiliation:

1. Department of Information Systems, Ulyanovsk State Technical University, Severny Venets Str., 32, Ulyanovsk 432027, Russia

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.

Funder

Ministry of Science and Higher Education of Russia

Publisher

MDPI AG

Subject

General Mathematics,Engineering (miscellaneous),Computer Science (miscellaneous)

Reference25 articles.

1. (2023, October 26). GitHub. Available online: https://github.com.

2. (2023, October 26). GitLab. Available online: https://gitlab.com.

3. (2023, October 26). Bitbucket. Available online: https://bitbucket.org.

4. (2023, October 26). Rating of Repository Services for Storing Code. Available online: https://tagline.ru/source-code-repository-rating/2016.

5. (2023, October 26). GitHub Repository to Learn Data Science. Available online: https://levelup.gitconnected.com/top-10-github-repository-to-learn-data-science-892935bcebdb.

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3