Software Evolution Prediction Using Machine Learning Algorithms

Rajeeb Bal; Jibendu Mantri

Authors

Rajeeb Bal Ph.D Scholar, MSCB University https://orcid.org/0000-0002-3030-5417
Jibendu Mantri Professor, MSCB University

Keywords:

Software Engineering, Software Evolution, Artificial Intelligence (AI), Machine Learning (ML), Regression

Abstract

Software evolution represents the most time-consuming phase in the software development life cycle (SDLC) after a software release. Further, it covers an increasingly significant role in modern software development practices (e.g., Agile, DevOps, CI/CD) and web development (e.g., React, Next.js, etc.), where teams contribute more effort in improving and maintaining existing systems than building new ones. These evolutionary actions such as modifying and adding features, incorporating contributions, and fixing issues generate large software project data that demonstrate the dynamics of software development. Software evolution in machine learning (ML) techniques comprises the unending adaptation of models, data, and pipelines to continue performance under changing domains. In this paper, we are predicting the software evolution by analyzing features like Repository name, Repository link, Commits, Issues, Pull Requests, Stars, Forks, Total Contributors, Top Contributors, Code lines cover with Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, and Support Vector Regression (SVR), are methods of ML. Thus, it is based on the results obtained a comparative study is conducted to evaluate and analyze model accuracy.

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