API parameter recommendation based on language model and program analysis

Tran-Manh Cuong, T. Tran, Tan M. Nguyen, Thu-Trang Nguyen, Son Nguyen, H. Vo
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引用次数: 2

Abstract

APIs are extensively and frequently used in source code to leverage existing libraries and improve programming productivity. However, correctly and effectively using APIs, especially from unfamiliar libraries, is a non-trivial task. Although various approaches have been proposed for recommending API method calls in code completion, suggesting actual parameters for such APIs still needs further investigating. In this paper, we introduce FLUTE, an efficient and novel approach combining program analysis and language models for recommending API parameters. With FLUTE, the source code of programs is first analyzed to generate syntactically legal and type-valid candidates. Then, these candidates are ranked using language models. Our empirical results on two large real-world projects Netbeans and Eclipse indicate that FLUTE achieves 80% and +90% in Top-1 and Top-5 Precision, which means the tool outperforms the state-of-the-art approach.
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基于语言模型和程序分析的API参数推荐
api在源代码中广泛且频繁地使用,以利用现有库并提高编程效率。然而,正确有效地使用api,特别是来自不熟悉的库的api,是一项重要的任务。尽管已经提出了各种方法来推荐在代码完成中调用API方法,但是为这些API建议实际参数仍然需要进一步研究。本文介绍了一种结合程序分析和语言模型的高效新颖的API参数推荐方法——FLUTE。使用FLUTE,首先分析程序的源代码以生成语法上合法且类型有效的候选程序。然后,使用语言模型对这些候选对象进行排名。我们在两个大型现实世界项目Netbeans和Eclipse上的经验结果表明,FLUTE在Top-1和Top-5精度上分别达到80%和+90%,这意味着该工具优于最先进的方法。
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