Comparison of Various Machine Learning Models For Software Bug Prediction

Comparison of Various Machine Learning Models For Software Bug Prediction

Registration ID: IJNRD_181511

Published ID: IJNRD2206104

DOI: Click Here to Get

Shalaka Naik Dessai , Aparna Rane

Machine Learning, Software Bug Prediction, Dataset, Logistic Regression, Decision Tree, Random Forest, Adaboost , XGBoost

As internet users grow, the quantity of data available on the web increases with it. Virtually everything that needs human effort or human presence can be replaced by the Software. While developing an application it follows the Software Development Lifecycle (SDLC). Within the early stages of development, it's a compulsory task to take care of system or bugs to avoid wasting time and effort during initial development phase to forestall any runtime crisis. In this paper , we compare five machine learning models – Logistic Regression, Decision Tree, Random Forest, Adaboost and XGBoost for four datasets of NASA - KC2, PC3, JM1, CM1. Later on, new model was proposed based on tuning the existing XGBoost model by changing its parameter namely N_estimator, learning rate, max depth, and subsample. The results achieved were compared with state-of art models and the results showed that the tuned XGBoost model outperformed them for all datasets. This research will contribute in correctly detecting the bugs with machine learning approach.

Published Paper   E-Certificate

"Comparison of Various Machine Learning Models For Software Bug Prediction", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.7, Issue 6, page no.891-898, June-2022, Available :https://ijnrd.org/papers/IJNRD2206104.pdf

Volume 7 Issue 6, June-2022

Pages : 891-898

Paper Reg. ID: IJNRD_181511

Published Paper Id: IJNRD2206104

Downloads: 000121162

Research Area: Engineering

Country: Cuncolim, Goa, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2206104.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2206104

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJNRD (IJ Publication) Janvi Wave

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition

Call For Paper - Volume 10 | Issue 8 | August 2025

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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (IJNRD) aims to explore advances in research pertaining to applied, theoretical and experimental Technological studies. The goal is to promote scientific information interchange between researchers, developers, engineers, students, and practitioners working in and around the world. IJNRD will provide an opportunity for practitioners and educators of engineering field to exchange research evidence, models of best practice and innovative ideas.

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