API Recommendation Using Domain And Source Code Knowledge

The process of migration the old retired API(Application Programming Interface) with new and most to up to date one, know as API migration. Developers need to fully understand the documentation for the retired (replaced) library and the new (replacing) library to do the appropriate migration. This manual process is complex, error-prone, and costly for companies. There have been many studies focused on the automation recommendation between different method mapping for different libraries. These studies focused on the recommendations between methods from different programming languages while non of them focused on the recommendations between methods of libraries that belong to the same programming language. At times, one of the studies indicates automatic recommendation when mapping two different methods libraries that belong to the same programming language by using domain knowledge(method description, method parameters|name). In this thesis, we investigated the mapping between two methods of library migrations by using the domain knowledge and source code documentation. In order to be able to obtain these scenarios, we propose the RAPIM++ machine learning approach which recommends a correct mapping between source and target methods of three-party libraries using domain knowledge and source code knowledge. Our main contribution in this studywas, build a model which depends on existing library changes done manually from previous developers in different open source projects in java programming language then use features related to source code implementation, the similarity between method signatures and methods documentation to predict correct method mapping between two methods level library migration. Our result was RAPIM++ was able to successfully mapping between two methods from different third-party libraries with a rate of accuracy score of 84.4%. Additionally, our approach could able to recommend the libraries that absent the documentations since it relies on the source code knowledge along with the main knowledge. We can conclude from these results that RAPIM++ able to recommend third-party libraries with or without documentation, so though libraries that are not well known and do not belong to popular frameworks, can find comprehensive recommendations when using our model. Furthermore, RAPIM++ provides the research and industry community with a lightweight web service that available publicly to make method mapping between third - part libraries an easy task for developers