Williams, Byron J.
Date of Degree
Original embargo terms
Complete embargo for 6 months||forever||5/15/2021
Graduate Thesis - Open Access
Master of Science
James Worth Bagley College of Engineering
Software bugs prediction is one of the most active research areas in the software engineering community. The process of testing and debugging code proves to be costly during the software development life cycle. Software metrics measure the quality of source code to identify software bugs and vulnerabilities. Traceable code patterns are able to de- scribe code at a finer granularity level to measure quality. Micro patterns will be used in this research to mechanically describe java code at the class level. Machine learning has also been introduced for bug prediction to localize source code for testing and debugging. Deep Learning is a branch of Machine Learning that is relatively new. This research looks to improve the prediction of software bugs by utilizing micro patterns with deep learning techniques. Software bug prediction at a finer granularity level will enable developers to localize code to test and debug during the development process.
Brumfield, Marcus, "A Deep Learning approach to predict software bugs using micro patterns and software metrics" (2020). Theses and Dissertations MSU. 101.