Ph.D
Group : Large-scale Heterogeneous DAta and Knowledge

A symbolic approach for the verification and the testing of service choreographies

Starts on 24/11/2009
Advisor : ZAIDI, Fatiha

Funding :
Affiliation : Université Paris-Saclay
Laboratory : LRI

Defended on 31/10/2013, committee :

Research activities :
   - Formal Model-Based Testing

Abstract :
Service-oriented engineering is an emerging software development paradigm for distributed collaborative applications. Such an application is made up of several entities abstracted as services, each of them being for example a Web application, a Web service, or even a human. The services can be developed independently and are composed to achieve common requirements through interactions among them. Service choreographies define such requirements from a global perspective, based on interactions among a set of participants.

This thesis aims to formalize the problems and attempts to develop a framework by which service choreographies can be developed correctly for both top-down or bottom-up approaches. It consists in analyzing relation between a choreography specification and a choreography implementation at both model level and real implementation level. Particularly, it concerns the composition/decomposition service design, the verification, and the testing of choreography implementation.

The first key point of our framework is to support value-passing among services by using symbolic technique and SMT solver. It overcomes false negatives or state space explosion issues due by abstracting or limiting data domain of value-passing in existing approaches.

The second key point is the black-box passive testing of choreography implementation. It does not require neither to access to source codes nor to make the implementation unavailable during testing process.

Our framework is fully implemented in our toolchains which can be downloaded or used online at address http://schora.lri.fr.

CAUSAL LEARNING FOR DIAGNOSTIC SUPPORT


CAUSAL UNCERTAINTY QUANTIFICATION UNDER PARTIAL KNOWLEDGE AND LOW DATA REGIMES


MICRO VISUALIZATIONS: DESIGN AND ANALYSIS OF VISUALIZATIONS FOR SMALL DISPLAY SPACES
The topic of this habilitation is the study of very small data visualizations, micro visualizations, in display contexts that can only dedicate minimal rendering space for data representations. For several years, together with my collaborators, I have been studying human perception, interaction, and analysis with micro visualizations in multiple contexts. In this document I bring together three of my research streams related to micro visualizations: data glyphs, where my joint research focused on studying the perception of small-multiple micro visualizations, word-scale visualizations, where my joint research focused on small visualizations embedded in text-documents, and small mobile data visualizations for smartwatches or fitness trackers. I consider these types of small visualizations together under the umbrella term ``micro visualizations.'' Micro visualizations are useful in multiple visualization contexts and I have been working towards a better understanding of the complexities involved in designing and using micro visualizations. Here, I define the term micro visualization, summarize my own and other past research and design guidelines and outline several design spaces for different types of micro visualizations based on some of the work I was involved in since my PhD.