Section: New Results
Reverse Engineering and Evolution
Model Driven Reverse Engineering (MDRE), and its applications such as software modernization, is a discipline in which model-driven development (MDD) techniques are used to treat legacy systems. During this year, Atlanmod has continued working actively on this research area. The main contributions are the following:
In the context of the ARTIST FP7 project, the work has been continued on reusing (and extending accordingly) MoDisco and several of its components to provide the Reverse Engineering support required within the project. At conceptual-level, the MoDisco Model Discovery + Model Understanding overall two-step approach  has been published and promoted as an important part of the ARTIST migration methodology and process  . At tooling-level, several (MoDisco-based) model discovery components from Java and SQL have been developed and made available as part of the official ARTIST OS Release (http://www.artist-project.eu/tools-of-toolbox/193 ). Directly related to some of these components, a promising work has been initiated on studying deeper the discovery of behavioral aspects of software and dealing with their further understanding based on the OMG FUML standard combined with different modeling techniques (transformation, slicing, etc.). Complementary work has also been performed in the context of the TEAP FUI project finishing by the end of this year. It concerns the related problem of data federation from heterogeneous sources in the domain of Enterprise Architecture. This has notably resulted in a prototype called EMF Views that can be practically used in such reverse engineering scenarios  and also in other cases to be further explored (cf. the MoNoGe FUI project dealing with (meta)model extension).
In a web context, in a previous work we shown how to discover the schema which is implicit in JSON data. This year we built on that contribution to study how schemas coming from different JSON-based web APIs can be composed  . Thus, we presented an approach able to identify composition links between schemas of different APIs. This composition information plus the API schemas are used to render a graph where paths represent API compositions and are used to easily identify how to compose the APIs. For instance, we illustrated one application based on generating sequence diagrams from graph paths, where the diagram includes the API calls (and their corresponding parameters) that web developers have to perform in order to compose one or more APIs.
In the context of our work around DSLs, we have been working on facilitating the definition of DSLs from existing APIs. Sometimes library developers prefer to provide their users with a DSL, instead of (or in addition to) an API. APIs and DSLs can be seen as alternative methods to access the library functionalities, and are characterized by specific advantages. We therefore proposed a method to automatically analyze an existing object-oriented API and generate a DSL out of it. Our approach leverages on model-driven techniques to analyze and represent APIs at high-level of abstraction (i.e., as metamodels) which are later used to automatically generate the DSL components and the corresponding tooling, including parser, compiler and development environment. Developers can influence the DSL generation by editing the model-based API representation and by specifying design choices about the structure of the DSL to generate. A proof-of-concept implementation of the method has been developed, called DSLit, that is able to analyze Java APIs and generate textual DSLs.
On the evolution side, we have been working on an approach to automatically resynchronize code-generation artefacts (in particular, model-to-text transformations) after changes on the target platform