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Enhanced code coverage approach for regression testing

机译:增强的代码覆盖率方法用于回归测试

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摘要

Code coverage information is widely used to address problems in software development and maintenance phase. Researchers have developed different approaches and tools using code coverage concept to accomplish regression testing tasks. These tools did not provide runtime enhancement techniques. In this research, an implementation of a proposed tool is conducted to measure the code coverage percentage which will be known as phase 1. Phase 2 focuses on enhancing the code coverage keeping the performance degradation as minimum as possible. A suite of selective case studies was tested to calculate the percentage of statement coverage, the number of executed statements and the overall runtime. The design of the proposed tool in this research is based on computing the overall runtime as a single thread approach. When it was tested on multiple threads a race condition problem was discovered as all the threads where trying to access the files in the same time which might lead to a deadlock. This implies resources wasting and delaying overall runtime. So the calculation of the overall runtime is assigned to a single highly configured processor to avoid transition time between multiple processors. This enhances the regression testing performance while decreasing the overall cost of the regression testing process. The proposed tool enhancement results are compared to its relative results in case of no enhancement process to judge the robustness of the proposed approach for this research. The comparison shows a range of enhancement fluctuation between 49%???67% for different sized test cases.
机译:代码覆盖率信息被广泛用于解决软件开发和维护阶段的问题。研究人员使用代码覆盖率概念开发了不同的方法和工具来完成回归测试任务。这些工具没有提供运行时增强技术。在这项研究中,将执行一个建议的工具来测量代码覆盖率,这将被称为第1阶段。第2阶段着重于增强代码覆盖率,同时将性能下降降到最低。测试了一组选择性案例研究,以计算语句覆盖率,已执行语句的数量和总体运行时间。本研究中所建议工具的设计基于将整体运行时间作为单线程方法进行计算。在多线程上对其进行测试时,发现竞争条件问题,因为所有线程试图同时访问文件,这可能导致死锁。这意味着资源浪费和延迟了总体运行时间。因此,将整个运行时间的计算分配给单个高度配置的处理器,以避免在多个处理器之间进行转换。这可以提高回归测试性能,同时降低回归测试过程的总体成本。在没有增强过程的情况下,将所提出的工具增强结果与其相对结果进行比较,以判断所提出的方法对于本研究的可靠性。比较表明,对于不同大小的测试用例,增强波动的范围在49%〜67%之间。

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