API method recommendation via explicit matching of functionality verb phrases

Abstract

Due to the lexical gap between functionality descriptions and user queries, documentation-based API retrieval often produces poor results. Verb phrases and their phrase patterns are essential in both describing API functionalities and interpreting user queries. Thus we hypothesize that API retrieval can be facilitated by explicitly recognizing and matching between the fine-grained structures of functionality descriptions and user queries. To verify this hypothesis, we conducted a large-scale empirical study on the functionality descriptions of 14,733 JDK and Android API methods. We identified 356 different functionality verbs from the descriptions, which were grouped into 87 functionality categories, and we extracted 523 phrase patterns from the verb phrases of the descriptions. Building on these findings, we propose an API method recommendation approach based on explicit matching of functionality verb phrases in functionality descriptions and user queries, called PreMA. Our evaluation shows that PreMA can accurately recognize the functionality categories (92.8%) and phrase patterns (90.4%) of functionality description sentences; and when used for API retrieval tasks, PreMA can help participants complete their tasks more accurately and with fewer retries compared to a baseline approach.