Title: Program synthesis from natural language using recurrent neural networks
Advisors: Luke Zettlemoyer and Michael Ernst
Abstract: Even if a competent programmer knows what she wants to do and can describe it in English, it can still be difficult to write code to achieve the goal. Existing resources, such as question-and-answer websites, tabulate specific operations that someone has wanted to perform in the past, but they are not effective in generalizing to new tasks, to compound tasks that require combining previous questions, or sometimes even to variations of listed tasks.
Our goal is to make programming easier and more productive by letting programmers use their own words and concepts to express the intended operation, rather than forcing them to accommodate the machine by memorizing its grammar. We have built a system that lets a programmer describe a desired operation in natural language, then automatically translates it to a programming language for review and approval by the programmer. Our system, Tellina, does the translation using recurrent neural networks (RNNs), a state-of-the-art natural language processing technique that we augmented with slot (argument) filling and other enhancements.
We evaluated Tellina in the context of shell scripting. We trained Tellina's RNNs on textual descriptions of file system operations and bash one-liners, scraped from the web. Although recovering completely correct commands is challenging, Tellina achieves top-3 accuracy of 80% for producing the correct command structure. In a controlled study, programmers who had access to Tellina outperformed those who did not, even when Tellina's predictions were not completely correct, to a statistically significant degree.