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1. 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)

   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 aboutmore »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.« less
2. Inferring Program Transformations From Singular Examples via Big Code

   https://doi.org/10.1109/ASE.2019.00033  

   Jiang, Jiajun ; Ren, Luyao ; Xiong, Yingfei ; Zhang, Lingming ( November 2019 , IEEE/ACM International Conference on Automated Software Engineering)

   Inferring program transformations from concrete program changes has many potential uses, such as applying systematic program edits, refactoring, and automated program repair. Existing work for inferring program transformations usually rely on statistical information over a potentially large set of program-change examples. However, in many practical scenarios we do not have such a large set of program-change examples. In this paper, we address the challenge of inferring a program transformation from one single example. Our core insight is that "big code" can provide effective guide for the generalization of a concrete change into a program transformation, i.e., code elements appearing in many files are general and should not be abstracted away. We first propose a framework for transformation inference, where programs are represented as hypergraphs to enable fine-grained generalization of transformations. We then design a transformation inference approach, GENPAT, that infers a program transformation based on code context and statistics from a big code corpus. We have evaluated GENPAT under two distinct application scenarios, systematic editing and program repair. The evaluation on systematic editing shows that GENPAT significantly outperforms a state-of-the-art approach, SYDIT, with up to 5.5x correctly transformed cases. The evaluation on program repair suggests that GENPAT has the potentialmore »to be integrated in advanced program repair tools-GENPAT successfully repaired 19 real-world bugs in the Defects4J benchmark by simply applying transformations inferred from existing patches, where 4 bugs have never been repaired by any existing technique. Overall, the evaluation results suggest that GENPAT is effective for transformation inference and can potentially be adopted for many different applications.« less
3. On the Naturalness of Fuzzer-Generated Code

   https://doi.org/10.1145/3524842.3527972  

   Kambhamettu, Rajeswari Hita ; Billos, John ; Oluwaseun-Apo, Tomi ; Gafford, Benjamin ; Padhye, Rohan ; Hellendoorn, Vincent J. ( May 2022 , 19th International Conference on Mining Software Repositories)

   Compiler fuzzing tools such as Csmith have uncovered many bugs in compilers by randomly sampling programs from a generative model. The success of these tools is often attributed to their ability to generate unexpected corner case inputs that developers tend to overlook during manual testing. At the same time, their chaotic nature makes fuzzer-generated test cases notoriously hard to interpret, which has lead to the creation of input simplification tools such as C-Reduce (for C compiler bugs). In until now unrelated work, researchers have also shown that human-written software tends to be rather repetitive and predictable to language models. Studies show that developers deliberately write more predictable code, whereas code with bugs is relatively unpredictable. In this study, we ask the natural questions of whether this high predictability property of code also, and perhaps counter-intuitively, applies to fuzzer-generated code. That is, we investigate whether fuzzer-generated compiler inputs are deemed unpredictable by a language model built on human-written code and surprisingly conclude that it is not. To the contrary, Csmith fuzzer-generated programs are more predictable on a per-token basis than human-written C programs. Furthermore, bug-triggering tended to be more predictable still than random inputs, and the C-Reduce minimization tool did notmore »substantially increase this predictability. Rather, we find that bug-triggering inputs are unpredictable relative to Csmith's own generative model. This is encouraging; our results suggest promising research directions on incorporating predictability metrics in the fuzzing and reduction tools themselves.« less
4. Bug synthesis: challenging bug-finding tools with deep faults

   https://doi.org/10.1145/3236024.3236084  

   Roy, Subhajit ; Pandey, Awanish ; Dolan-Gavitt, Brendan ; Hu, Yu ( November 2017 , Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering)

   In spite of decades of research in bug detection tools, there is a surprising dearth of ground-truth corpora that can be used to evaluate the efficacy of such tools. Recently, systems such as LAVA and EvilCoder have been proposed to automatically inject bugs into software to quickly generate large bug corpora, but the bugs created so far differ from naturally occurring bugs in a number of ways. In this work, we propose a new automated bug injection system, Apocalypse, that uses formal techniques—symbolic execution, constraint-based program synthesis and model counting—to automatically inject fair (can potentially be discovered by current bug-detection tools), deep (requiring a long sequence of dependencies to be satisfied to fire), uncorrelated (each bug behaving independent of others), reproducible (a trigger input being available) and rare (can be triggered by only a few program inputs) bugs in large software code bases. In our evaluation, we inject bugs into thirty Coreutils programs as well as the TCAS test suite. We find that bugs synthesized by Apocalypse are highly realistic under a variety of metrics, that they do not favor a particular bug-finding strategy (unlike bugs produced by LAVA), and that they are more difficult to find than manually injectedmore »bugs, requiring up around 240× more tests to discover with a state-of-the-art symbolic execution tool.« less
5. 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))

   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.