学习优先考虑编译器测试的测试程序

Junjie Chen, Y. Bai, Dan Hao, Yingfei Xiong, Hongyu Zhang, Bing Xie
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引用次数: 70

摘要

编译器测试是保证编译器(以及一般的软件系统)可靠性的关键方法。已经提出了许多技术来促进自动化编译器测试。这些技术依赖于由一些测试生成工具(例如CSmith)生成的大量测试程序(它们是编译器的测试输入)。然而,这些编译器测试技术存在严重的效率问题,因为它们通常需要很长时间才能找到编译器错误。为了加速编译器测试,最好对生成的测试程序进行优先排序,以便更有可能触发编译器错误的测试程序被更早地执行。在本文中,我们提出了学习测试的思想,即从之前触发bug的测试程序中学习揭示bug的测试程序的特征。基于学习测试的思想,我们提出了LET,一种对编译器测试加速的测试程序进行优先排序的方法。LET由学习过程和调度过程组成。在学习过程中,LET识别测试程序的一组特征,训练一个能力模型来预测新测试程序触发编译器错误的概率,以及一个时间模型来预测测试程序的执行时间。在调度过程中,LET根据它们在单位时间内的漏洞暴露概率对新的测试程序进行优先级排序,这是基于两个训练好的模型计算的。我们的大量实验表明,LET显著地加速了编译器测试。特别是,在24.64%的病例中,LET减少了50%以上的检测时间,在36.23%的病例中,LET减少了25%到50%的检测时间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Learning to Prioritize Test Programs for Compiler Testing
Compiler testing is a crucial way of guaranteeing the reliability of compilers (and software systems in general). Many techniques have been proposed to facilitate automated compiler testing. These techniques rely on a large number of test programs (which are test inputs of compilers) generated by some test-generation tools (e.g., CSmith). However, these compiler testing techniques have serious efficiency problems as they usually take a long period of time to find compiler bugs. To accelerate compiler testing, it is desirable to prioritize the generated test programs so that the test programs that are more likely to trigger compiler bugs are executed earlier. In this paper, we propose the idea of learning to test, which learns the characteristics of bug-revealing test programs from previous test programs that triggered bugs. Based on the idea of learning to test, we propose LET, an approach to prioritizing test programs for compiler testing acceleration. LET consists of a learning process and a scheduling process. In the learning process, LET identifies a set of features of test programs, trains a capability model to predict the probability of a new test program for triggering compiler bugs and a time model to predict the execution time of a test program. In the scheduling process, LET prioritizes new test programs according to their bug-revealing probabilities in unit time, which is calculated based on the two trained models. Our extensive experiments show that LET significantly accelerates compiler testing. In particular, LET reduces more than 50% of the testing time in 24.64% of the cases, and reduces between 25% and 50% of the testing time in 36.23% of the cases.
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