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The suggestions made by current IDE's code completion features are based exclusively on static type system of the programming language. As a result, often proposals are made which are irrelevant for a particular working context. Also, these suggestions are ordered alphabetically rather than by their relevance in a particular context. In this paper, we(More)
This preprint is provided by the contributing authors to ensure timely dissemination of scholarly and technical work. Abstract. When using object-oriented frameworks it is easy to overlook certain important method calls that are required at particular places in code. In this paper, we provide a comprehensive set of empirical facts on this problem, starting(More)
Today's Integrated Development Environments (IDEs) only integrate the tools and knowledge of a single user and workstation. This neglects the fact that the way in which we develop and maintain a piece of software and interact with our IDE provides a rich source of information that can help ourselves and other programmers to avoid mistakes in the future, or(More)
To help developers in using frameworks, good documentation is crucial. However, it is a challenge to create high quality documentation especially of hotspots in white-box frameworks. This paper presents an approach to documentation of object-oriented white-box frameworks which mines from client code four different kinds of documentation items, which we call(More)
To ease framework understanding, tools have been developed that analyze existing framework instantiations to extract API usage patterns and present them to the user. However, detailed quantitative evaluations of such recommender systems are lacking. In this paper we present an automated evaluation process which extracts queries and expected results from(More)
Code recommender systems ease the use and learning of software frameworks and libraries by recommending calls based on already present code. Typically, code recommender tools have been based on rather simple rule based systems while many of the recent advances in Recommender Systems and Collaborative Filtering have been largely focused on rating data. While(More)