The test case execution order affects the time at which the objectives of testing are met. If the objective is fault detection, an inappropriate execution order might reveal most faults late, thus delaying the bug fixing activity and eventually the delivery of the software. Prioritizing the test cases so as to optimize the achievement of the testing goal has potentially a positive impact on the testing costs, especially when the test execution time is long.

Test engineers often possess relevant knowledge about the relative priority of the test cases. However, this knowledge can be hardly expressed in the form of a global ranking or scoring. In this paper, we propose a test case prioritization technique that takes advantage of user knowledge through a machine learning algorithm, Case-Based Ranking (CBR). CBR elicits just relative priority information from the user, in the form of pairwise test case comparisons. User input is integrated with multiple prioritization indexes, in an iterative process that successively refines the test case ordering. Preliminary results on a case study indicate that CBR overcomes previous approaches and, for moderate suite size, gets very close to the optimal solution.