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Mobile app developers rely heavily on standard API frameworks and libraries. However, learning API usages is often challenging due to the fast-changing nature of API frameworks for mobile systems and the insufficiency of API documentation and source code examples. In this paper, we propose a novel approach to learn API usages from bytecode of Android mobile apps. Our core contributions include HAPI,...
Mobile app reviews often contain useful user opinions like bug reports or suggestions. However, looking for those opinions manually in thousands of reviews is ineffective and time-consuming. In this paper, we propose PUMA, an automated, phrase-based approach to extract user opinions in app reviews. Our approach includes a technique to extract phrases in reviews using part-of-speech (PoS) templates;...
Mobile apps often rely heavily on standard API frameworks and libraries. However, learning to use those APIs is often challenging due to the fast-changing nature of API frameworks and the insufficiency of documentation and code examples. This paper introduces DroidAssist, a recommendation tool for API usages of Android mobile apps. The core of DroidAssist is HAPI, a statistical, generative model of...
Mobile app reviews often contain useful user opinions for app developers. However, manual analysis of those reviews is challenging due to their large volume and noisynature. This paper introduces MARK, a supporting tool for review analysis of mobile apps. With MARK, an analyst can describe her interests of one or more apps via a set of keywords. MARK then lists the reviews most relevant to those keywords...
User reviews of mobile apps often contain complaints or suggestions which are valuable for app developers to improve user experience and satisfaction. However, due to the large volume and noisy-nature of those reviews, manually analyzing them for useful opinions is inherently challenging. To address this problem, we propose MARK, a keyword-based framework for semi-automated review analysis. MARK allows...
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