A transpiler converts code from one programming language to another. Many practical uses of transpilers require the user to be able to guide or customize the program produced from a given input program. This customizability is important for satisfying many application-specific goals for the produced code such as ensuring performance, readability, ease of exposition or maintainability, compatibility with external environment or analysis tools, and so on. Conventional transpilers are deterministic rule-driven systems often written without offering customizability per user and per program. Recent advances in transpilers based on neural networks offer some customizability to users, e.g. through interactive prompts, but they are still difficult to precisely control the production of a desired output. Both conventional and neural transpilation also suffer from the “last mile” problem: they produce correct code on average, i.e., on most parts of a given program, but not necessarily for all parts of it. We propose a new transpilation approach that offers fine-grained customizability and reusability of transpilation rules created by others, without burdening the user to understand the global semantics of the given source program. Our approach is mostly automatic and incremental, i.e., constructs translation rules needed to transpile the given program as per the user’s guidance piece-by-piece. Users can rely on existing transpilation rules to translate most of the program correctly while focusing their effort locally, only on parts that are incorrect or need customization. This improves the correctness of the end result. We implement the transpiler as a tool called DuoGlot, which translates Python to Javascript programs, and evaluate it on the popular GeeksForGeeks benchmarks. DuoGlot achieves $90%$ translation accuracy and so it outperforms all existing translators (both handcrafted and neural-based), while it produces readable code. We evaluate DuoGlot on two additional benchmarks, containing more challenging and longer programs, and similarly observe improved accuracy compared to the other transpilers.
Fri 27 OctDisplayed time zone: Lisbon change
16:00 - 17:30 | |||
16:00 18mTalk | Aliasing Limits on Translating C to Safe Rust OOPSLA Mehmet Emre University of San Francisco, Peter Boyland University of California at Santa Barbara, Aesha Parekh University of California at Santa Barbara, Ryan Schroeder University of California at Santa Barbara, Kyle Dewey California State University, Ben Hardekopf University of California at Santa Barbara DOI Pre-print | ||
16:18 18mTalk | Adventure of a Lifetime: Extract Method Refactoring for Rust OOPSLA Sewen Thy Ahrefs Research, Yale-NUS College, Andreea Costea National University of Singapore, Kiran Gopinathan National University of Singapore, Ilya Sergey National University of Singapore DOI Pre-print | ||
16:36 18mTalk | Inductive Program Synthesis Guided by Observational Program Similarity OOPSLA Jack Feser Hamilton College, Işıl Dillig University of Texas at Austin, Armando Solar-Lezama Massachusetts Institute of Technology DOI | ||
16:54 18mTalk | Automated Translation of Functional Big Data Queries to SQL OOPSLA Guoqiang Zhang North Carolina State University, Benjamin Mariano University of Texas at Austin, Xipeng Shen North Carolina State University, Işıl Dillig University of Texas at Austin DOI | ||
17:12 18mTalk | User-Customizable Transpilation of Scripting Languages OOPSLA Bo Wang National University of Singapore, Aashish Kolluri National University of Singapore, Ivica Nikolić National University of Singapore, Teodora Baluta National University of Singapore, Prateek Saxena National University of Singapore DOI | ||