Statistical Translation of English Texts to API Code Templates

摘要

We develop T2API, a context-sensitive, graph-based statistical translation approach that takes as input an English description of a programming task and synthesizes the corresponding API code template for the task. We train T2API to statistically learn the alignments between English and API elements and determine the relevant API elements. The training is done on StackOverflow, a bilingual corpus on which developers discuss programming problems in two types of language: English and programming language. T2API considers both the context of the words in the input query and the context of API elements that often go together in the corpus. The derived API elements with their relevance scores are assembled into an API usage by GraSyn, a novel graph-based API synthesis algorithm that generates a graph representing an API usage from a large code corpus. Importantly, it is capable of generating new API usages from previously seen sub-usages. We curate a test benchmark of 250 real-world StackOverflow posts. Across the benchmark, T2API's synthesized snippets have the correct API elements with a median top-1 precision and recall of 67% and 100%, respectively. Four professional developers and five graduate students judged that 77% of our top synthesized API code templates are useful to solve the problem presented in the StackOverflow posts.