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To keep up with changes in requirements, frameworks, and coding practices, software organizations might need to migrate code from one language to another. Source-to-source migration, or transpilation, is often a complex, manual process. Transpilation requires expertise both in the source and target language, making it highly laborious and costly. Languages models for code generation and transpilation are becoming increasingly popular. However, despite capturing code-structure well, code generated by language models is often spurious and contains subtle problems. We proposeBatFix, a novel approach that augments language models for transpilation by leveraging program repair and synthesis to fix the code generated by these models.BatFixtakes as input both the original program, the target program generated by the machine translation model, and a set of test cases and outputs a repaired program that passes all test cases. Experimental results show that our approach is agnostic to language models and programming languages.BatFixcan locate bugs spawning multiple lines and synthesize patches for syntax and semantic bugs for programs migrated fromJavatoC++andPythontoC++from multiple language models, including, OpenAI’sCodex. 

Industrial deployments of automated program repair (APR), e.g., at Facebook and Bloomberg, signal a new milestone for this exciting and potentially impactful technology. In these deployments, developers use APR-generated patch suggestions as part of a human-driven debugging process. Unfortunately, little is known about how using patch suggestions affects developers during debugging. This paper conducts a controlled user study with 40 developers with a median of 6 years of experience. The developers engage in debugging tasks on nine naturally-occurring defects in real-world, open-source, Java projects, using Recoder, SimFix, and TBar, three state-of-the-art APR tools. For each debugging task, the developers either have access to the project's tests, or, also, to code suggestions that make all the tests pass. These suggestions are either developer-written or APR-generated, which can be correct or deceptive. Deceptive suggestions, which are a common APR occurrence, make all the available tests pass but fail to generalize to the intended specification. Through a total of 160 debugging sessions, we find that access to a code suggestion significantly increases the odds of submitting a patch. Correct APR suggestions increase the odds of debugging success by 14,000%, but deceptive suggestions decrease the odds of success by 65%. Correct suggestions also speed up debugging. Surprisingly, we observe no significant difference in how novice and experienced developers are affected by APR, suggesting that APR may find uses across the experience spectrum. Overall, developers come away with a strong positive impression of APR, suggesting promise for APR-mediated, human-driven debugging, despite existing challenges in APR-generated repair quality. 

Software developers often struggle to update APIs, leading to manual, time-consuming, and error-prone processes. We introduce Melt, a new approach that generates lightweight API migration rules directly from pull requests in popular library repositories. Our key insight is that pull requests merged into open-source libraries are a rich source of information sufficient to mine API migration rules. By leveraging code examples mined from the library source and automatically generated code examples based on the pull requests, we infer transformation rules in Comby, a language for structural code search and replace. Since inferred rules from single code examples may be too specific, we propose a generalization procedure to make the rules more applicable to client projects. Melt rules are syntax-driven, interpretable, and easily adaptable. Moreover, unlike previous work, our approach enables rule inference to seamlessly integrate into the library workflow, removing the need to wait for client code migrations. We evaluated Melt on pull requests from four popular libraries, successfully mining 461 migration rules from code examples in pull requests and 114 rules from auto-generated code examples. Our generalization procedure increases the number of matches for mined rules by 9×. We applied these rules to client projects and ran their tests, which led to an overall decrease in the number of warnings and fixing some test cases demonstrating MELT's effectiveness in real-world scenarios. 

The Linux Kernel is a world-class operating system controlling most of our computing infrastructure: mobile devices, Internet routers and services, and most of the supercomputers. Linux is also an example of low-level software with no comprehensive regression test suite (for good reasons). The kernel’s tremendous societal importance imposes strict stability and correctness requirements. These properties make Linux a challenging and relevant target for static automated program repair (APR). Over the past decade, a significant progress has been made in dynamic APR. However, dynamic APR techniques do not translate naturally to systems without tests. We present a static APR technique addressing sequentiallocking API misusebugs in the Linux Kernel. We attack the key challenge of static APR, namely, the lack of detailed program specification, by combining static analysis with machine learning to complement the information presented by the static analyzer. In experiments on historical real-world bugs in the kernel, we were able to automatically re-produce or propose equivalent patches in 85% of the human-made patches, and automatically rank them among the top three candidates for 64% of the cases and among the top five for 74%.