Applying Machine Learning Techniques to Predict the Maintainability of Open Source Software 
Madhwaraj Kango Gopal1, Amirthavalli M.2
1Dr. Madhwaraj Kango Gopal, Professor, Department of Computer Applications, New Horizon College of Engineering, Bengaluru (Karnataka), India.
2Ms. Amirthavalli, PhD Scholar, Department of Information Technology, SSN College of Engineering, Kalavakkam (Tamil Nadu), India.
Manuscript received on 25 August 2019 | Revised Manuscript received on 01 September 2019 | Manuscript Published on 14 September 2019 | PP: 192-195 | Volume-8 Issue-5S3, July 2019 | Retrieval Number: E10450785S319/19©BEIESP | DOI: 10.35940/ijeat.E1045.0785S319
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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: Software maintainability is a vital quality aspect as per ISO standards. This has been a concern since decades and even today, it is of top priority. At present, majority of the software applications, particularly open source software are being developed using Object-Oriented methodologies. Researchers in the earlier past have used statistical techniques on metric data extracted from software to evaluate maintainability. Recently, machine learning models and algorithms are also being used in a majority of research works to predict maintainability. In this research, we performed an empirical case study on an open source software jfreechart by applying machine learning algorithms. The objective was to study the relationships between certain metrics and maintainability.
Keywords: Machine Learning, Maintainability, Open Source Software, Object-Oriented, Design Metrics.
Scope of the Article: Machine Learning