Abstract
Software technologies are constantly evolving to facilitate the development, deployment, and maintenance of applications in different areas. In parallel, these applications evolve continuously to guarantee an adequate quality of service, and they become more and more complex. Such evolution often involves increased development and maintenance costs, that can become even higher when these applications are deployed in recent execution infrastructures such as the cloud. Nowadays, reducing these costs and improving the quality of applications are main objectives of software engineering. Recently, microservices have emerged as an example of a technology or architectural style that helps to achieve these objectives.While microservices can be used to develop new applications, there are monolithic ones (i.e., monoliths) built as a single unit and their owners (e.g., companies, etc.) want to maintain and deploy them in the cloud. In this case, it is common to consider rewriting these applications from scratch or migrating them towards recent architectural styles. Rewriting an application or migrating it manually can quickly become a long, error-prone, and expensive task. An automatic migration appears as an evident solution.The ultimate aim of our dissertation is contributing to automate the migration of monolithic Object-Oriented (OO) applications to microservices. This migration consists of two steps: microservice identification and microservice packaging. We focus on microservice identification based on source code analysis. Specifically, we propose two approaches.The first one identifies microservices from the source code of a monolithic OO application relying on code structure, data accesses, and software architect recommendations. The originality of our approach can be viewed from three aspects. Firstly, microservices are identified based on the evaluation of a well-defined function measuring their quality. This function relies on metrics reflecting the "semantics" of the concept "microservice". Secondly, software architect recommendations are exploited only when they are available. Finally, two algorithmic models have been used to partition the classes of an OO application into microservices: clustering and genetic algorithms.The second approach extracts from an OO source code a workflow that can be used as an input of some existing microservice identification approaches. A workflow describes the sequencing of tasks constituting an application according to two formalisms: control flow and /or data flow. Extracting a workflow from source code requires the ability to map OO conceptsinto workflow ones.To validate both approaches, we implemented two prototypes and conducted experiments on several case studies. The identified microservices have been evaluated qualitatively and quantitatively. The extracted workflows have been manually evaluated relying on test suites. The obtained results show respectively the relevance of the identified microservices and the correctness of the extracted workflows.