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  1. Gonnord, Laure ; Titolo, Laura (Ed.)
    Just-in-Time (JIT) compilers are widely used to improve the performance of interpreter-based language implementations by creating optimized code at runtime. However, bugs in the JIT compiler’s code manipulation and optimization can result in the generation of incorrect code. Such bugs can be difficult to diagnose and fix, and can result in exploitable vulnerabilities. Unfortunately, existing approaches to automatic bug localization do not carry over well to such bugs. This paper discusses a different approach to analyzing JIT compiler optimization behaviors, based on using dynamic analysis to construct abstract models of the JIT compiler’s optimizer and back end. By comparing the models obtained for buggy and non-buggy executions of the JIT compiler, we can pinpoint the components of the JIT compiler’s internal representation that have been affected by the bug; this can then be mapped back to identify the buggy code. Our ex- periments with two real bugs for Google V8 JIT compiler, TurboFan, show the utility and practicality of our approach.
  2. Just-in-time (JIT) compilers are used by many modern programming systems in order to improve performance. Bugs in JIT compilers provide exploitable security vulnerabilities and debugging them is difficult as they are large, complex, and dynamic. Current debugging and visualization tools deal with static code and are not suitable in this domain. We describe a new approach for simplifying the large and complex intermediate representation, generated by a JIT compiler and visualize it with a metro map metaphor to aid developers in debugging.
  3. Many widely-deployed modern programming systems use just-in-time (JIT) compilers to improve performance. The size and complexity of JIT-based systems, combined with the dynamic nature of JIT-compiler optimizations, make it challenging to locate and fix JIT compiler bugs quickly. At the same time, JIT compiler bugs can result in exploitable security vulnerabilities, making rapid bug localization important. Existing work on automated bug localization focuses on static code, i.e., code that is not generated at runtime, and so cannot handle bugs in JIT compilers that generate incorrect code during optimization. This paper describes an approach to automated bug localization in JIT compilers, down to the level of distinct optimization phases, starting with a single initial Proof-of-Concept (PoC) input that demonstrates the bug. Experiments using a prototype implementation of our ideas on Google’s V8 JavaScript interpreter and TurboFan JIT compiler demonstrates that it can successfully identify buggy optimization phases.