BAUT: A Bayesian Driven Tutoring System

—This paper presents the design of BAUT, a tutoring system that explores statistical approach for providing instant project failure analysis. Driven by a Bayesian Network (BN) inference engine, BAUT analyzes the test cases failed by a student project submission and provides instant diagnosis that guides students in identifying and removing software bugs. Using a parameter learning process, BAUT is able to improve the quality of its analysis. The initial case study with the prototype demonstrates the potential of the system. Keywords-Bayesian Network; Automated Tutoring; Verification; Testing; Web Application.

• Type: Conference
• Year: 2010
• Where: ITNG
• Authors: Song Tan, Kai Qian, Xiang Fu, Prabir Bhattacharya
• Keywords: Instant Project Failure, ITNG 2010, Parameter Learning Process, Student Project Submission