Work in progress — Intelligent project failure analysis

Sophisticated course projects in senior computer science classes have raised great challenges to educators. Students now have stronger needs for instant and individualized guidance when exposed to the complexity of software development. In this paper, we describe the application of Bayesian Network, a probabilistic graphic model, to automated project failure analysis. Students can make multiple project submissions to an automated grader before the project deadline. Each submission will be graded immediately, by running a collection of test cases prepared by instructors in advance. Then failure behaviors of test cases are piped to a Bayesian Network (BN) inference engine. The BN engine generates a report on the most probable failure causes, and helps students in removing software bugs. The analysis of the BN engine can be improved by parameter learning. A case study is presented to illustrate the effectiveness of the approach.