Fog computing plays an important role in industrial and information process. The programs in fog computing, such as iFogSim toolkit, usually contain some infeasible paths (paths that cannot be covered), which makes it impossible to compare algorithm in models that require covering all paths. In this paper, we proposed a mathematical model to build automated test case generation based on path coverage (ATCG-PC) in fog computing programs as a single-objective problem. Single objective helps to reduce the cost of evaluation functions, which is proportional to the number of test cases. When infeasible paths are contained in tested programs, algorithms can also be compared in this model. In this paper, classical differential evolution (DE) is used to solve the ATCG-PC. However, it is difficult for DE to use generated test cases covering remaining paths in the ATCG-PC of fog computing. Therefore, we proposed a test-case-path relationship matrix to empower DE (RP-DE). Experiment results show that RP-DE uses significantly less test cases and achieves higher path coverage rate than compared state-of-the-art algorithms.

中文翻译：

---

基于 iFogSim 工具包关系矩阵的差分进化自动生成测试用例


雾计算在工业和信息化进程中发挥着重要作用。雾计算中的程序，例如iFogSim工具包，通常包含一些不可行路径（无法覆盖的路径），这使得无法在需要覆盖所有路径的模型中进行算法比较。在本文中，我们提出了一种数学模型，用于在雾计算程序中构建基于路径覆盖的自动测试用例生成（ATCG-PC）作为单目标问题。单一目标有助于降低评估功能的成本，该成本与测试用例的数量成正比。当被测程序中包含不可行路径时，也可以在此模型中对算法进行比较。本文采用经典差分进化（DE）来求解ATCG-PC。然而，DE很难使用生成的测试用例来覆盖雾计算的ATCG-PC中的剩余路径。因此，我们提出了测试用例路径关系矩阵来赋能DE（RP-DE）。实验结果表明，与最先进的算法相比，RP-DE 使用的测试用例明显减少，并实现了更高的路径覆盖率。