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Formal Verification (FV) and Machine Learning (ML) can seem incompatible due to their opposite mathematical foundations and their use in real-life problems: FV mostly relies on discrete mathematics and aims at ensuring correctness; ML often relies on probabilistic models and consists of learning patterns from training data. In this paper, we postulate that they are complementary in practice, and explore how ML helps FV in its classical approaches: static analysis, model-checking, theorem-proving, and SAT solving. We draw a landscape of the current practice and catalog some of the most prominent uses of ML inside FV tools, thus offering a new perspective on FV techniques that can help researchers and practitioners to better locate the possible synergies. We discuss lessons learned from our work, point to possible improvements and offer visions for the future of the domain in the light of the science of software and systems modeling., Comment: 13 pages, no figures, 3 tables

In incremental development strategies, modelers frequently refine Statecharts models to satisfy requirements and changes. Although several solutions exist to the problem of Statecharts refinement, they provide such levels of freedom that a statechart cannot make assumptions or guarantees about its future structure. In this paper, we propose a set of bounding rules to limit the allowable Statecharts refinement operations such that certain assumptions will hold., Comment: In Poster Proceedings of 6th Conference on Software Language Engineering (SLE) 2013 (http://www.sleconf.org/2013/)

We describe the architecture and initial implementation of the next-generation of Grid Data Management Middleware in the EU DataGrid (EDG) project. The new architecture stems out of our experience and the users requirements gathered during the two years of running our initial set of Grid Data Management Services. All of our new services are based on the Web Service technology paradigm, very much in line with the emerging Open Grid Services Architecture (OGSA). We have modularized our components and invested a great amount of effort towards a secure, extensible and robust service, starting from the design but also using a streamlined build and testing framework. Our service components are: Replica Location Service, Replica Metadata Service, Replica Optimization Service, Replica Subscription and high-level replica management. The service security infrastructure is fully GSI-enabled, hence compatible with the existing Globus Toolkit 2-based services; moreover, it allows for fine-grained authorization mechanisms that can be adjusted depending on the service semantics., Comment: Talk from the 2003 Computing in High Energy and Nuclear Physics (CHEP03), La Jolla,Ca, USA, March 2003 8 pages, LaTeX, the file contains all LaTeX sources - figures are in the directory "figures"