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.