Prediction of Software Defects using Ensemble Machine Learning Techniques
Sowjanya Jindam1, Sai Teja Challa2, Sai Jahnavi Chada3, Navya Sree B4, Srinidhi Malgireddy5
1Sowjanya Jindam, Assistant Professor, Department of Information Technology, Maturi Venkata Subba Rao (MVSR) Engineering College, Osmania University, Hyderabad (Telangana), India.
2Sai Teja Challa, Student, Department of Information Technology, Maturi Venkata Subba Rao (MVSR) Engineering College, Osmania University, Hyderabad (Telangana), India.
3Sai Jahnavi Chada, Student, Department of Information Technology, Maturi Venkata Subba Rao (MVSR) Engineering College, Osmania University, Hyderabad (Telangana), India.
4Navya Sree B, Student, Department of Information Technology, Maturi Venkata Subba Rao (MVSR) Engineering College, Osmania University, Hyderabad (Telangana), India.
5Srinidhi Malgireddy, Student, Department of Information Technology, Maturi Venkata Subba Rao (MVSR) Engineering College, Osmania University, Hyderabad (Telangana), India.
Manuscript received on 23 December 2022 | Revised Manuscript received on 30 December 2022 | Manuscript Accepted on 15 January 2023 | Manuscript published on 30 January 2023 | PP: 58-65 | Volume-11 Issue-5, January 2023 | Retrieval Number: 100.1/ijrte.E74210111523 | DOI: 10.35940/ijrte.E7421.0111523
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: During software development and maintenance, predicting software bugs becomes critical. Defect prediction early in the software development life cycle is an important aspect of the quality assurance process that has received a lot of attention in the previous two decades. Early detection of defective modules in software development can support the development team in efficiently and effectively utilizing available resources to provide high-quality software products in a short amount of time. The machine learning approach, which works by detecting hidden patterns among software features, is an excellent way to identify problematic modules. The software flaws in NASA datasets MC1, MW1, KC3, and PC4 are predicted using multiple machine learning classification algorithms in this work. A new model was developed based on altering the parameters of the previous XGBoost model, including N_estimator, learning rate, max depth, and subsample. The results were compared to those obtained by state-of-the-art models, and our model outperformed them across all datasets.
Keywords: Machine Learning, Dataset, Supervised Learning, Random Forest, XgBoost, Ada Boost, Decision Tree.
Scope of the Article: Machine Learning