Software Bugs Detection Using Supervised Machine Learning Techniques
Year: 2023
Type: conference-paper
Abstract: A software bug is some sort of a fault in the source code or a computer program. These bugs work in unusual and unintended ways which is a serious problem for a programmer and the company. Detecting bugs in software has been tried and tested through multiple means, the most recent of which is Machine Learning algorithms. Using a revolutionary dataset consisting of real software code snippets of the C language into key values, we were able to train the algorithms based on numerical parameters. This in turn simplified our algorithmic process. Furthermore, we run multiple classification algorithms to gain precision, recall and Fn scores, and improve upon these scores using key hyperparameter tuning techniques. Our observations revealed an increase in accuracy and were able to create an end module which can directly take the source code as an input from which the metrics and features are extracted and give the output if the code has a software bug or not.
DOI: 10.4028/p-of2831
Authors: Manthan Shah, Ayushi Sharma, Rizon Kumar
Institution: SRM Institute of Science and Technology
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4322492045