ARist: An effective API argument recommendation approach
نویسندگان
چکیده
Learning and remembering to use APIs are difficult. Several techniques have been proposed assist developers in using APIs. Most existing focus on recommending the right API methods call, but very few arguments. In this paper, we propose ARist, a novel automated argument recommendation approach which suggests arguments by predicting developers’ expectations when they define methods. To implement idea process, ARist combines program analysis (PA), language models (LMs), several features specialized for task consider functionality of formal parameters positional information code elements (e.g., variables or method calls) given context. LMs used suggest promising candidates identified PA. Meanwhile, PA navigates working set valid satisfy syntax, accessibility, type-compatibility constraints defined programming use. Our evaluation large dataset real-world projects shows that improves state-of-the-art 19% 18% top-1 precision recall frequently-used libraries. For general task, i.e., every outperforms baseline approaches up 125% accuracy. Moreover, newly-encountered projects, achieves more than 60% top-3 accuracy evaluating larger dataset. working/maintaining with personalized LM capture coding practice, can productively rank expected at position 7/10 requests.
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ژورنال
عنوان ژورنال: Journal of Systems and Software
سال: 2023
ISSN: ['0164-1212', '1873-1228']
DOI: https://doi.org/10.1016/j.jss.2023.111786