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Negative Effects of Bytecode Instrumentation on Java Source Code Coverage

机译:字节码仪器对Java源代码覆盖率的负面影响

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Code coverage measurement is an important element in white-box testing, both in industrial practice and academic research. Other related areas are highly dependent on code coverage as well, including test case generation, test prioritization, fault localization, and others. Inaccuracies of a code coverage tool sometimes do not matter that much but in certain situations they can lead to serious confusion. For Java, the prevalent approach to code coverage measurement is to use bytecode instrumentation due to its various benefits over source code instrumentation. However, if the results are to be mapped back to source code this may lead to inaccuracies due to the differences between the two program representations. In this paper, we systematically investigate the amount of differences in the results of these two Java code coverage approaches, enumerate the possible reasons and discuss the implications on various applications. For this purpose, we relied on two widely used tools to represent the two approaches and a set of benchmark programs from the open source domain.
机译:代码覆盖测量是工业实践和学术研究的白盒测试中的重要因素。其他相关区域的高度依赖于代码覆盖,包括测试案例生成,测试优先级,故障定位等。代码覆盖工具的不准确性有时并不重要,但在某些情况下,他们可能会导致严重混乱。对于Java,普遍的代码覆盖测量方法是由于其在源代码仪器上的各种优势,因此使用字节码仪器。但是,如果要将结果映射回源代码,这可能导致由于两个程序表示之间的差异导致不准确性。在本文中,我们系统地调查了这两个Java代码覆盖方法结果的差异量,枚举了可能的原因,并讨论对各种应用的影响。为此,我们依赖于两个广泛使用的工具来代表来自开源域的两种方法和一组基准测试程序。

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