ABSTRACT

Many software bug detection measurement models and frameworks exist, however most of these approaches and tools are process-based and suffer from many limitations. We propose a framework to detect software bugs based on code pattern detection. Our framework will mine and generate bug patterns, detect those patterns in code, and calculate a reliability measure of software. Based on our knowledge we are the first pattern-based model for the detection and measurement of bugs in software. Advancement in machine learning techniques in recent years have lead research to consider applying deep learning to source code. While there is little research available on the subject, the work that has been done shows great potential. We believe that this work can be leveraged to obtain new insight into helping automate parts of our framework for better practicality in the real world.

BIO

John Heaps is a third year Computer Science PhD student at the University of Texas at San Antonio. His work is mainly focused on Artificial Intelligence with a focus on Deep Learning.