Using model-based diagnosis to improve software testing

We propose a combination of AI techniques to improve software testing. When a test fails, a model-based diagnosis (MBD) algorithm is used to propose a set of possible explanations. We call these explanations diagnoses. Then, a planning algorithm is used to suggest further tests to identify the correct diagnosis. A tester preforms these tests and reports their outcome back to the MBD algorithm, which uses this information to prune incorrect diagnoses. This iterative process continues until the correct diagnosis is returned. We call this testing paradigm Test, Diagnose and Plan (TDP). Several test planning algorithms are proposed to minimize the number of TDP iterations, and consequently the number of tests required until the correct diagnosis is found. Experimental results show the benefits of using an MDP-based planning algorithms over greedy test planning in three benchmarks.