ACM Computing Surveys (CSUR)
Data mining library reuse patterns using generalized association rules
Proceedings of the 22nd international conference on Software engineering
Information delivery in support of learning reusable software components on demand
Proceedings of the 7th international conference on Intelligent user interfaces
Making the Reuse Business Work
Computer
Effects of Reuse on Quality, Productivity, and Economics
IEEE Software
Using structural context to recommend source code examples
Proceedings of the 27th international conference on Software engineering
Jungloid mining: helping to navigate the API jungle
Proceedings of the 2005 ACM SIGPLAN conference on Programming language design and implementation
Rascal: A Recommender Agent for Agile Reuse
Artificial Intelligence Review
Software Quality Control
Parseweb: a programmer assistant for reusing open source code on the web
Proceedings of the twenty-second IEEE/ACM international conference on Automated software engineering
Code Conjurer: Pulling Reusable Software out of Thin Air
IEEE Software
Learning from examples to improve code completion systems
Proceedings of the the 7th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering
MAPO: Mining and Recommending API Usage Patterns
Genoa Proceedings of the 23rd European Conference on ECOOP 2009 --- Object-Oriented Programming
An approach to context-based recommendation in software development
Proceedings of the sixth ACM conference on Recommender systems
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Reuse recommendation systems suggest functions or code snippets that are useful for the programming task at hand within the IDE. These systems utilize different aspects from the context of the cursor position within the source file being edited for inferring which functionality is needed next. Current approaches are based on structural information like inheritance relations or type/method usages. We propose a novel method that utilizes the knowledge embodied in the identifiers as a basis for the recommendation of API methods. This approach has the advantage that relevant recommendations can also be made in cases where no methods are called in the context or if contexts use distinct but semantically similar types or methods. First experiments show, that the correct method is recommended in about one quarter to one third of the cases.