Candidate: Song Wang
Title: Leveraging Machine Learning to Improve Software Reliability
Date: November 13, 2018
Time: 1:30 PM
Place: DC 1331
Supervisor(s): Tan, Lin
Abstract:
Finding software faults is a critical task during the life-cycle of a software system. While traditional software quality control practices such as statistical defect prediction, static bug detection, regression test, and code review are often inefficient and time-consuming, which cannot keep up with the increasing complexity of modern software systems. We argue that machine learning with its capability in knowledge representation, learning, natural language processing, classification, etc., can be used to extract invaluable information from software artifacts that may be difficult to obtain with other research methodologies to improve existing software reliability practices such as statistical defect prediction, static bug detection, regression test, and code review. This study presents a suite of machine learning based novel techniques to improve existing software reliability practices for helping developers find software bugs more effective and efficient.