ML-Driven Compiler Testing: Optimization Methods and Technical Approaches
Keywords:
Machine learning, Compiler testing, Method optimizationAbstract
Within the domain of system software, compilers play a critically important role. If a compiler contains defects or malfunctions, the resulting executable files can be severely compromised, introducing errors that propagate throughout the software stack. In compiler quality management, rigorous testing through appropriate methodologies is therefore essential. Recent technological advances have facilitated the evolution of automated compiler testing techniques. However, most contemporary testing approaches rely on test case generation tools such as Csmith, implementing functional testing by generating large volumes of test cases during the testing process. Moreover, as compilers constitute large-scale software systems, the associated testing processes also perform stress testing through the execution of extensive test case suites. Nevertheless, this approach suffers from significant efficiency limitations.
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