Open Access. Powered by Scholars. Published by Universities.®
- Discipline
Articles 1 - 3 of 3
Full-Text Articles in Computer Sciences
Api Recommendation For Event-Driven Android Application Development, Weizhao Yuan, Huu Hoang Nguyen, Lingxiao Jiang, Yuting Chen, Jianjun Zhao, Haibo Yu
Api Recommendation For Event-Driven Android Application Development, Weizhao Yuan, Huu Hoang Nguyen, Lingxiao Jiang, Yuting Chen, Jianjun Zhao, Haibo Yu
Research Collection School Of Computing and Information Systems
Context: Software development is increasingly dependent on existing libraries. Developers need help to find suitable library APIs. Although many studies have been proposed to recommend relevant functional APIs that can be invoked for implementing a functionality, few studies have paid attention to an orthogonal need associated with event-driven programming frameworks, such as the Android framework. In addition to invoking functional APIs, Android developers need to know where to place functional code according to various events that may be triggered within the framework.Objective: This paper aims to develop an API recommendation engine for Android application development that can recommend both (1) …
Automatic Query Reformulation For Code Search Using Crowdsourced Knowledge, Mohammad M. Rahman, Chanchal K. Roy, David Lo
Automatic Query Reformulation For Code Search Using Crowdsourced Knowledge, Mohammad M. Rahman, Chanchal K. Roy, David Lo
Research Collection School Of Computing and Information Systems
Traditional code search engines (e.g., Krugle) often do not perform well with natural language queries. They mostly apply keyword matching between query and source code. Hence, they need carefully designed queries containing references to relevant APIs for the code search. Unfortunately, preparing an effective search query is not only challenging but also time-consuming for the developers according to existing studies. In this article, we propose a novel query reformulation technique–RACK–that suggests a list of relevant API classes for a natural language query intended for code search. Our technique offers such suggestions by exploiting keyword-API associations from the questions and answers …
Automatic Query Reformulation For Code Search Using Crowdsourced Knowledge, Mohammad M. Rahman, Chanchal K. Roy, David Lo
Automatic Query Reformulation For Code Search Using Crowdsourced Knowledge, Mohammad M. Rahman, Chanchal K. Roy, David Lo
Research Collection School Of Computing and Information Systems
Traditional code search engines (e.g., Krugle) often do not perform well with natural language queries. They mostly apply keyword matching between query and source code. Hence, they need carefully designed queries containing references to relevant APIs for the code search. Unfortunately, preparing an effective search query is not only challenging but also time-consuming for the developers according to existing studies. In this article, we propose a novel query reformulation technique–RACK–that suggests a list of relevant API classes for a natural language query intended for code search. Our technique offers such suggestions by exploiting keyword-API associations from the questions and answers …