Research on hyper‐level of hyper‐heuristic framework for MOTCP

Abstract

Heuristic algorithms are widely used to solve multi‐objective test case prioritization (MOTCP) problems. However, they perform differently for different test scenarios, which conducts difficulty in applying a suitable algorithm for new test requests in the industry. A concrete hyper‐heuristic framework for MOTCP (HH‐MOTCP) is proposed for addressing this problem. It mainly has two parts: low‐level encapsulating various algorithms and hyper‐level including an evaluation and selection mechanism that dynamically selects low‐level algorithms. This framework performs good but still difficult to keep in the best three. If the evaluation mechanism can more accurately analyse the current results, it will help the selection strategy to find more conducive algorithms for evolution. Meanwhile, if the selection strategy can find a more suitable algorithm for the next generation, the performance of the HH‐MOTCP framework will be better. In this paper, we first propose new strategies for evaluating the current generation results, then perform an extensive study on the selection strategies which decide the heuristic algorithm for the next generation. Experimental results show that the new evaluation and selection strategies proposed in this paper can make the HH‐MOTCP framework more effective and efficient, which makes it almost the best two except for one test object and ahead in about 40% of all test objects.