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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 propose BatFix, a novel approach that augments language models for transpilation by leveraging program repair and synthesis to fix the code generated by these models. BatFix takes 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. BatFix can locate bugs spawning multiple lines and synthesize patches for syntax and semantic bugs for programs migrated from Java to C++ and Python to C++ from multiple language models, including, OpenAI's Codex. [ABSTRACT FROM AUTHOR]

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 sequential locking API misuse bugs 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%. [ABSTRACT FROM AUTHOR]

The decompiler is one of the most common tools for examining executable binaries without the corresponding source code. It transforms binaries into high-level code, reversing the compilation process. Unfortunately, decompiler output is far from readable because the decompilation process is often incomplete. State-of-the-art techniques use machine learning to predict missing information like variable names. While these approaches are often able to suggest good variable names in context, no existing work examines how the selection of training data influences these machine learning models. We investigate how data provenance and the quality of training data affect performance, and how well, if at all, trained models generalize across software domains. We focus on the variable renaming problem using one such machine learning model, DIRE. We first describe DIRE in detail and the accompanying technique used to generate training data from raw code. We also evaluate DIRE’s overall performance without respect to data quality. Next, we show how training on more popular, possibly higher quality code (measured using GitHub stars) leads to a more generalizable model because popular code tends to have more diverse variable names. Finally, we evaluate how well DIRE predicts domain-specific identifiers, propose a modification to incorporate domain information, and show that it can predict identifiers in domain-specific scenarios 23% more frequently than the original DIRE model. [ABSTRACT FROM AUTHOR]