论文标题
下注并运行测试案例生成
Bet and Run for Test Case Generation
论文作者
论文摘要
在技术领域工作的任何人都可能熟悉一个问题：“您是否尝试过重新将其关闭？”，因为这通常是技术支持提出的默认问题。同样，在基于搜索的测试中已知元启发式学可能在搜索过程中被困在高原中。作为一个人，可以查看健身曲线的梯度并决定重新启动搜索，以便希望通过下一次运行来改善优化结果。试图自动重新启动，必须通过编程方式决定元神经术是否遇到了高原，这是一个固有的困难问题。为了在理论搜索问题的背景下减轻这个问题，开发了BET和运行策略，在此策略中，同时启动了多个算法实例，除了一段时间之后，除了在健身价值方面，除了最有前途的实例外，还杀死了所有算法。在本文中，我们为测试案例生成问题采用并评估了BET和运行策略。我们的工作表明，当与文献中最佳参数实例化时，使用此重新启动策略通常不会导致质量指标的收益。
Anyone working in the technology sector is probably familiar with the question: "Have you tried turning it off and on again?", as this is usually the default question asked by tech support. Similarly, it is known in search based testing that metaheuristics might get trapped in a plateau during a search. As a human, one can look at the gradient of the fitness curve and decide to restart the search, so as to hopefully improve the results of the optimization with the next run. Trying to automate such a restart, it has to be programmatically decided whether the metaheuristic has encountered a plateau yet, which is an inherently difficult problem. To mitigate this problem in the context of theoretical search problems, the Bet and Run strategy was developed, where multiple algorithm instances are started concurrently, and after some time all but the single most promising instance in terms of fitness values are killed. In this paper, we adopt and evaluate the Bet and Run strategy for the problem of test case generation. Our work indicates that use of this restart strategy does not generally lead to gains in the quality metrics, when instantiated with the best parameters found in the literature.
