基于蒙特卡洛的超启发式工业嵌入式软件交互测试方法研究
An Evaluation of Monte Carlo-Based Hyper-Heuristic for Interaction Testing of Industrial Embedded Software Applications
摘要：
超启发式算法是一种新的自适应混合元启发式算法，以获得一个通用的算法来解决优化问题。这项工作的重点是选择类型的超启发式，称为指数蒙特卡罗计数器（EMCQ）。当前实现依赖于无记忆选择，这可能是适得其反的，因为所选择的搜索运算符可能（历史上）不是当前搜索实例的最佳执行运算符。为了解决这个问题，我们建议将内存集成到EMCQ中，使用基于Q学习机制的强化学习（称为Q-EMCQ）进行组合t-wise测试集生成。组合测试生成在工业程序中的有限应用可能会影响Q-EMCQ等技术的使用。因此，有必要针对相关的工业软件评估这种方法，目的是显示覆盖代码以及查找错误所需的交互程度。我们将Q-EMCQ应用于37个用功能框图（FBD）语言编写的实际工业程序，这些程序用于在Bombardier Transportation Sweden AB开发训练控制管理系统。研究结果表明，Q-EMCQ是一种有效的测试用例生成技术。此外，与处理最小化问题的t-明智测试集生成不同，我们还将Q-EMCQ进行了涉及一般模块聚类的最大化问题，以证明我们方法的有效性。
英文摘要：
Hyper-heuristic is a new methodology for the adaptive hybridization of meta-heuristic algorithms to derive a general algorithm for solving optimization problems. This work focuses on the selection type of hyper-heuristic, called the Exponential Monte Carlo with Counter (EMCQ). Current implementations rely on the memory-less selection that can be counterproductive as the selected search operator may not (historically) be the best performing operator for the current search instance. Addressing this issue, we propose to integrate the memory into EMCQ for combinatorial t-wise test suite generation using reinforcement learning based on the Q-learning mechanism, called Q-EMCQ. The limited application of combinatorial test generation on industrial programs can impact the use of such techniques as Q-EMCQ. Thus, there is a need to evaluate this kind of approach against relevant industrial software, with a purpose to show the degree of interaction required to cover the code as well as finding faults. We applied Q-EMCQ on 37 real-world industrial programs written in Function Block Diagram (FBD) language, which is used for developing a train control management system at Bombardier Transportation Sweden AB. The results of this study show that Q-EMCQ is an efficient technique for test case generation. Additionally, unlike the t-wise test suite generation, which deals with the minimization problem, we have also subjected Q-EMCQ to a maximization problem involving the general module clustering to demonstrate the effectiveness of our approach.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
