Compilers should not miscompile. Our work addresses problems in developing peephole optimizations that perform local rewriting to improve the efficiency of LLVM code. These optimizations are individually difficult to get right, particularly in the presence of undefined behavior; taken together they represent a persistent source of bugs. This paper presents Alive, a domain-specific language for writing optimizations and for automatically either proving them correct or else generating counterexamples. Furthermore, Alive can be automatically translated into C++ code that is suitable for inclusion in an LLVM optimization pass. Alive is based on an attempt to balance usability and formal methods; for example, it capturesbut largely hides-the detailed semantics of three different kinds of undefined behavior in LLVM. We have translated more than 300 LLVM optimizations into Alive and, in the process, found that eight of them were wrong.

Compilers should not miscompile. Our work addresses problems in developing peephole optimizations that perform local rewriting to improve the efficiency of LLVM code. These optimizations are individually difficult to get right, particularly in the presence of undefined behavior; taken together they represent a persistent source of bugs. This paper presents Alive, a domain-specific language for writing optimizations and for automatically either proving them correct or else generating counterexamples. Furthermore, Alive can be automatically translated into C++ code that is suitable for inclusion in an LLVM optimization pass. Alive is based on an attempt to balance usability and formal methods; for example, it captures---but largely hides---the detailed semantics of three different kinds of undefined behavior in LLVM. We have translated more than 300 LLVM optimizations into Alive and, in the process, found that eight of them were wrong.

Peephole optimizations are a common source of compiler bugs. Compiler developers typically transform an incorrect peephole optimization into a valid one by strengthening the precondition. This process is challenging and tedious. This paper proposes ALIVE-INFER, a data-driven approach that infers preconditions for peephole optimizations expressed in Alive. ALIVE-INFER generates positive and negative examples for an optimization, enumerates predicates on-demand, and learns a set of predicates that separate the positive and negative examples. ALIVE-INFER repeats this process until it finds a precondition that ensures the validity of the optimization. ALIVE-INFER reports both a weakest precondition and a set of succinct partial preconditions to the developer. Our prototype generates preconditions that are weaker than LLVM's preconditions for 73 optimizations in the Alive suite. We also demonstrate the applicability of this technique to generalize 54 optimization patterns generated by Souper, an LLVM IR-based superoptimizer.

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Compilers should not miscompile. Peephole optimizations, which perform local rewriting of the input program to improve the efficiency of generated code, are a persistent source of compiler bugs. We created Alive, a domain-specific language for writing optimizations and for automatically either proving them correct or else generating counterexamples. Furthermore, Alive can be automatically translated into C++ code that is suitable for inclusion in an LLVM optimization pass. Alive is based on an attempt to balance usability and formal methods; for example, it captures---but largely hides---the detailed semantics of the various kinds of undefined behavior. Alive has found numerous bugs in the LLVM compiler and is being used by LLVM developers.