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1. Automated bug localization in JIT compilers

   https://doi.org/10.1145/3453933.3454021  

   Lim, HeuiChan ; Debray, Saumya ( April 2021 , Proceedings of the 17th ACM SIGPLAN/SIGOPS International Conference on Virtual Execution Environments (VEE ’21))

   null (Ed.)

   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. 

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2. Automatically Localizing Dynamic Code Generation Bugs in JIT Compiler Back-End

   https://doi.org/10.1145/3578360.3580260  

   Lim, HeuiChan ; Debray, Saumya ( February 2023 , ACM)

   Just-in-Time (JIT) compilers are ubiquitous in modern computing systems and are used in a wide variety of software. Dynamic code generation bugs, where the JIT compiler silently emits incorrect code, can result in exploitable vulnerabilities. They, therefore, pose serious security concerns and make quick mitigation essential. However, due to the size and complexity of JIT compilers, quickly locating and fixing bugs is often challenging. In addition, the unique characteristics of JIT compilers make existing bug localization approaches inapplicable. Therefore, this paper proposes a new approach to automatic bug localization, explicitly targeting the JIT compiler back-end. The approach is based on explicitly modeling architecture-independent back-end representation and architecture-specific code-generation. Experiments using a prototype implementation on a widely used JIT compiler (Turbofan) indicate that it can successfully localize dynamic code generation bugs in the back-end with high accuracy. 

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3. Enhanced compiler bug isolation via memoized search

   https://doi.org/10.1145/3324884.3416570  

   Chen, Junjie ; Ma, Haoyang ; Zhang, Lingming ( December 2020 , IEEE/ACM International Conference on Automated Software Engineering)

   null (Ed.)

   Compiler bugs can be disastrous since they could affect all the software systems built on the buggy compilers. Meanwhile, diagnosing compiler bugs is extremely challenging since usually limited debugging information is available and a large number of compiler files can be suspicious. More specifically, when compiling a given bug-triggering test program, hundreds of compiler files are usually involved, and can all be treated as suspicious buggy files. To facilitate compiler debugging, in this paper we propose the first reinforcement compiler bug isolation approach via structural mutation, called RecBi. For a given bug-triggering test program, RecBi first augments traditional local mutation operators with structural ones to transform it into a set of passing test programs. Since not all the passing test programs can help isolate compiler bugs effectively, RecBi further leverages reinforcement learning to intelligently guide the process of passing test program generation. Then, RecBi ranks all the suspicious files by analyzing the compiler execution traces of the generated passing test programs and the given failing test program following the practice of compiler bug isolation. The experimental results on 120 real bugs from two most popular C open-source compilers, i.e., GCC and LLVM, show that RecBi is able to isolate about 23%/58%/78% bugs within Top-1/Top-5/Top-10 compiler files, and significantly outperforms the state-of-the-art compiler bug isolation approach by improving 92.86%/55.56%/25.68% isolation effectiveness in terms of Top-1/Top-5/Top-10 results. 

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4. Compiler bug isolation via effective witness test program generation

   https://doi.org/10.1145/3338906.3338957  

   Chen, Junjie ; Han, Jiaqi ; Sun, Peiyi ; Zhang, Lingming ; Hao, Dan ; Zhang, Lu ( January 2019 , ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering)

   Compiler bugs are extremely harmful, but are notoriously difficult to debug because compiler bugs usually produce few debugging information. Given a bug-triggering test program for a compiler, hundreds of compiler files are usually involved during compilation, and thus are suspect buggy files. Although there are lots of automated bug isolation techniques, they are not applicable to compilers due to the scalability or effectiveness problem. To solve this problem, in this paper, we transform the compiler bug isolation problem into a search problem, i.e., searching for a set of effective witness test programs that are able to eliminate innocent compiler files from suspects. Based on this intuition, we propose an automated compiler bug isolation technique, DiWi, which (1) proposes a heuristic-based search strategy to generate such a set of effective witness test programs via applying our designed witnessing mutation rules to the given failing test program, and (2) compares their coverage to isolate bugs following the practice of spectrum-based bug isolation. The experimental results on 90 real bugs from popular GCC and LLVM compilers show that DiWi effectively isolates 66.67%/78.89% bugs within Top-10/Top-20 compiler files, significantly outperforming state-of-the-art bug isolation techniques. 

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5. Permchecker: a toolchain for debugging memory managers with typestate

   https://doi.org/10.1145/3485526  

   Cronburg, Karl ; Guyer, Samuel Z. ( October 2021 , Proceedings of the ACM on Programming Languages)

   Dynamic memory managers are a crucial component of almost every modern software system. In addition to implementing efficient allocation and reclamation, memory managers provide the essential abstraction of memory as distinct objects, which underpins the properties of memory safety and type safety. Bugs in memory managers, while not common, are extremely hard to diagnose and fix. One reason is that their implementations often involve tricky pointer calculations, raw memory manipulation, and complex memory state invariants. While these properties are often documented, they are not specified in any precise, machine-checkable form. A second reason is that memory manager bugs can break the client application in bizarre ways that do not immediately implicate the memory manager at all. A third reason is that existing tools for debugging memory errors, such as Memcheck, cannot help because they rely on correct allocation and deallocation information to work. In this paper we present Permchecker, a tool designed specifically to detect and diagnose bugs in memory managers. The key idea in Permchecker is to make the expected structure of the heap explicit by associating typestates with each piece of memory. Typestate captures elements of both type (e.g., page, block, or cell) and state (e.g., allocated, free, or forwarded). Memory manager developers annotate their implementation with information about the expected typestates of memory and how heap operations change those typestates. At runtime, our system tracks the typestates and ensures that each memory access is consistent with the expected typestates. This technique detects errors quickly, before they corrupt the application or the memory manager itself, and it often provides accurate information about the reason for the error. The implementation of Permchecker uses a combination of compile-time annotation and instrumentation, and dynamic binary instrumentation (DBI). Because the overhead of DBI is fairly high, Permchecker is suitable for a testing and debugging setting and not for deployment. It works on a wide variety of existing systems, including explicit malloc/free memory managers and garbage collectors, such as those found in JikesRVM and OpenJDK. Since bugs in these systems are not numerous, we developed a testing methodology in which we automatically inject bugs into the code using bug patterns derived from real bugs. This technique allows us to test Permchecker on hundreds or thousands of buggy variants of the code. We find that Permchecker effectively detects and localizes errors in the vast majority of cases; without it, these bugs result in strange, incorrect behaviors usually long after the actual error occurs. 

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