Predicting number of bugs before launch: An investigation based on machine learning
Document Type
Conference Proceeding
Publication Date
7-1-2021
Abstract
Identifying and minimizing the number of bugs before release is a high priority of any team working on software development. This can be achieved by Machine Learning (ML) models By using particular aspects of the code, ML Models can predict the number of bugs that are possible post launch. We use a public dataset consisting of 15 Java projects from GitHub as our training and test dataset. We use five ML models for our investigation: Multilayer Perceptron, K-Nearest Neighbors, Linear Regression, Logistic Regression, and Decision Trees We conduct a preliminary investigation to evaluate how these ML models perform in predicting bugs. The results show that Linear Regression outperforms the other four ML models in finding the number of bugs post release.
Publication Title
Proceedings - 2021 IEEE 45th Annual Computers, Software, and Applications Conference, COMPSAC 2021
First Page
1403
Last Page
1404
Digital Object Identifier (DOI)
10.1109/COMPSAC51774.2021.00205
ISBN
9781665424639
Citation Information
S. Rajendren and S. Reddivari, "Predicting Number of Bugs before Launch: An Investigation based on Machine Learning," 2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC), 2021, pp. 1403-1404, doi: 10.1109/COMPSAC51774.2021.00205.