面向类集成测试序列生成的强化学习研究

集成测试是软件测试过程中不可缺少的步骤, 针对在集成测试中如何对系统中的类合理排序的问题, 国内外研究者提出了多种生成类集成测试序列的方法, 然而他们大多没有将测试桩复杂度作为评估测试代价的指标.针对该问题, 提出面向类集成测试序列生成的强化学习研究方法, 以总体测试桩复杂度为评价测试代价的指标, 生成测试代价尽可能低...

Generation Method of Class Integration Test Order Based on Reinforcement Learning

Integration testing is an indispensable step in the software testing process. In response to the problem of how to rationally sort the classes in the system in integration testing, researchers at home and abroad have proposed a variety of methods to generate class integration test orders. However, most of them didn''t take the stubbing complexity as the indicator, which is an important factor in evaluating the test cost. In order to solve this problem, this paper proposes a method of generating a class integration test order based on reinforcement learning, using the overall stubbing complexity as the indicator to evaluate the test cost, and generating a class integration test order with the stubbing complexity as low as possible. First, we define the reinforcement learning task and set the pursuit goal of the algorithm according to the task; second, we perform the static analysis of the program and calculate the stubbing complexity according to the results of the analysis; then, we integrate the calculation of the stubbing complexity into the design of the reward function to provide information and basis for choosing the next action; finally, the value function is fed back through the reward function, and the value function is set to ensure that the cumulative reward is maximized. When the agent completes the specified number of training times, the system will select the class integration test order that obtains the largest cumulative reward value for output, which costs the lowest stubbing complexity we pursue. The experimental results show that the results obtained by this method are better than those obtained by other existing methods in terms of the overall stubbing complexity as the evaluation indicator.