Biblio

Problem. Code migration between languages is challenging partly because different languages require developers to use different software libraries and frameworks. For example, in Java, Java Development Kit library (JDK) is a popular toolkit while .NET is the main framework used in C\# software development. Code migration requires not only the mappings between the language constructs (e.g., statements, expressions) but also the mappings among the APIs of the libraries/frameworks used in two languages. For example, in Java, to write to a file, one can use FileWriter.write of FileWriter, and in C\#, one can achieve the same function with StreamWriter.Write of StreamWriter. Such mapping is called API mapping.

Learning and remembering how to use APIs is difficult. While code-completion tools can recommend API methods, browsing a long list of API method names and their documentation is tedious. Moreover, users can easily be overwhelmed with too much information. We present a novel API recommendation approach that taps into the predictive power of repetitive code changes to provide relevant API recommendations for developers. Our approach and tool, APIREC, is based on statistical learning from fine-grained code changes and from the context in which those changes were made. Our empirical evaluation shows that APIREC correctly recommends an API call in the first position 59% of the time, and it recommends the correct API call in the top five positions 77% of the time. This is a significant improvement over the state-of-the-art approaches by 30-160% for top-1 accuracy, and 10-30% for top-5 accuracy, respectively. Our result shows that APIREC performs well even with a one-time, minimal training dataset of 50 publicly available projects.