Spotting automatically cross-language relations

Nowadays most of the software projects are coded using several formal languages, either spread on different artifacts or even embedded in the same one. These formal languages are linked each other using cross-language relations, mainly framework specific and established at runtime. In this work we present a language agnostic approach to automatically detect cross-language relations to ease re-factoring, validation and to allow navigation support to the developer. We map a project in a set of Syntax Trees (ASTs); pair-wise we compute the intersection of the nodes and we pre-select potential candidates that can hold cross-relations. We then factorize the ASTs according to the nodes which surround the candidate and pairwise we compute the semantic similarity of the factorized trees. We narrow down a set of statistically significant features and we map them into a predictive model. We apply such a procedure to an AngularJS application and we show that this approach spots cross-language relations at fine grained level with 93.2% of recall and a F-measure of 92.2%.