Mining Natural APIs from Large Code Corpora using a Mixture of Hidden Markov Models

[摘要] A Natural API is a collection of API methods that tend to be used followingcertain discernible statistical patterns in real-world code. In this thesis, I present a method for learning an interpretable statistical model for such natural APIs. My model is trained on sequences of API calls produced from large software repositories through program analysis. Once trained, the model is able to recognize complextemporal dependences between methods, including methods that technically belong to different APIs, and can be used as a proxy for formal correctness specifications.Our experiments train the model on sequences of method calls generated from over 150 million lines of Android code. We evaluate the learned model by measuring accuracy in learnt specifications from the corpus, completing code with missing APIcalls, and searching for code that uses APIs in a way that matches a query. Our encouraging results indicate that statistical models of API calls learned from large code corpora can have broad value in software engineering.

[发布机构] Rice University

[效力级别] Markov