S-CASE Blog | S-CASE Development fuel booster

We have just finished hosting the 4th meeting of S-CASE here in Athens and feel excited to have a pilot contributing to our big and exciting development goals.

Not only that, the project and consortium itself has been a tank of inspiration, feeding us with new ideas and food for thought. As the first year comes soon to a wrap and after a lot of creative preparations, our pilot has now a clear positioning and connection to the Watchtower API’s offering, which will be soon available for use from our clients. The API’s environment is designed in a way that the Watchtower platform becomes the backend analytics for the customers, who need automated intelligence in their systems without having to develop their own analytics infrastructure. This way they go fast, low cost and with reliability, since the services consumed have been already tested and in production.

The vision of S-CASE is to provide tools for developers, along with the underpinning technologies that will support the insertion of rough system requirements in a variety of structured, semi-structured or unstructured formats for seamlessly generating draft software prototypes that will form the basis for complete software development.

S-CASE is seen as a rapid prototyping realm aiming at providing automated solutions for (a) the extraction of system specifications and low-level architecture, and (b) the discovery and synthesis of composite workflows of software artefacts from distributed open source and proprietary resources that fulfil the inserted system requirements in the best possible way.

Through the innovations it introduces, S-CASE is expected to have a significant impact on the reduction of the time that is required between the conceptualisation of a software system and its first prototype, thus improving the SE process in terms of development costs.

The S-CASE pilot – Unleashing the power of WISE

 

The power of the Watchtower lies to a big extend in our analytics engine called WISE (Watchtower’s Intelligent System Engine). Inside WISE lives all the automatic orchestration that takes of the shoulders of energy management teams the “discovery” part and lets them focus on their operations. The pilot will actually take our API’s one level down from today, inside the WISE. That means that our partner and customer developers will be able to create their own orchestrations instead of only using the ones that WISE exposes. This is taking us closer to a data analytics as a service model, where different customer needs and discard data sets can be analysed as customised as needed.

The Watchtower SAAS technology stack is an MVC architecture using javascript from end to end (MySQL, Express.js, Angular.js, Node.js) with the combination of WISE core functionality developed in Java. Every rule of the WISE core is being exposed with RESTful web services, creating an ecosystem of intelligence that depending on the functionality needs, separate orchestrations of the web services are being composed. The S-CASE platform like a fuel booster, will rapidly improve our productivity in developing these new web services and the development of new intelligent orchestrations inside WISE.

S-CASE Blog | The S-CASE concept

In our latest blog entry, project technical coordinator, Kyriakos Chatzidimitriou, takes us through the world of S-CASE, highlighting the project components and demonstrating how S-CASE will be realised.

The S-CASE project is about semi-automatically creating RESTful Web Services through multi-modal requirements using a Model Driven Engineering methodology. The world of web services is moving towards REST and S-CASE aims at facilitating developers implementing such web services by focusing mainly on requirements engineering. The figure below depicts the basic components and basic flow of events/data in S-CASE.

 

In order to better understand the practical application of the S-CASE solution, let’s take a look at a typical use case example.
Through the S-CASE IDE the user imports or creates multi-modal requirements for his/her envisioned application. The requirements may be:
  • Textual requirements in the form “The user/system must be able to …”,
  • UML activity and use case diagrams created in the platform or imported as images,
  • Storyboards for flow charting, and
  • Analysis class diagrams to improve the accuracy of the system to identify entities, their properties and their relationships.

The requirements are then processed through natural language processing and image analysis techniques in order to extract relevant software engineering concepts. These are mainly the identification of RESTful resources, their properties and relations and Create-Read-Update-Delete (CRUD) actions on resources. All these concepts are stored in the S-CASE ontology.
The above procedure also identifies action-resource tuples that can be created automatically by the system like the action-resource “create bookmark” (automatically built) or others that need more elaborate processes like “get the weather given geolocation coordinates” (semi-automatically build or composed). The latter are send into the Web Services Synthesis and Composition module.

The Web Services Synthesis and Composition module tries to synthesize elaborate processes by composing 3rd party web services into a single S-CASE composite web service. To perform such a computation, S-CASE provides a methodology for semantically annotating 3rd party web services using S-CASE domain ontologies, so that they can later be matched to the requirements of the composite service. The composite service is deployed to the YouREST deployment environment and registered in the directory of S-CASE web services for future reference and re-use.

Upon completing the stages above, the model driven engineering procedure initiates. The first step is to create the Computational Independent Model (CIM) out of the S-CASE ontology. The CIM contains the bare minimum information needed to scaffold a REST service that adheres to the requirements imposed by the user, i.e. it includes all the problem’s domain concepts.

After that model transformations take place transforming the CIM into PIM (incorporate design constraints, but platform independent) and PSM (Add support for implementing the PIM into a specific suite of software tools like: java, jax-rs, hibernate, json, jaxb, postgresql etc.). The final step is to automatically generate the code of the web service. Calls to composite services are wrapped inside the generated code. The code is build and deployed to YouREST for others to use.

In order to support software re-use, every software artefact created from this procedure is stored into the S-CASE repository for future retrieval.

Through S-CASE we plan to develop an ecosystem of services, along with the appropriate tools for service providers to develop quality software for SMEs with an affordable budget.

S-CASE News | OCEAN & ETICS Webinar

The OCEAN project recently organised a webinar on software testing and quality which took place on 15th October 2014. You can watch the whole webinar now online by clicking the link below.

OCEAN project simplifies testing and quality assurance activities – through ETICS – to improve the quality of the software being produced in FP7 projects. ETICS allows you to define, execute and analyse a large range of software testing cases: static code analysis, unit testing, compliance testing, deployment and functional testing in a multi-node environment. Testing results are processed and a quality rank is assigned accordingly with the SQALE method.

S-CASE Blog | Natural Language Processing

We recently got another research paper on our work in S-CASE accepted  at a conference on natural language processing. The accepted paper describes our efforts on improving a parsing model that can  automatically map software requirements written in natural language to formal representations based on semantic roles.

State-of-the-art semantic role labelling systems require large  annotated corpora to achieve full performance. Unfortunately, such  corpora are expensive to produce and often do not generalise well  across domains. Even in domain, errors are often made where syntactic  information does not provide sufficient cues. In this paper, we mitigate both of these problems by employing distributional word representations gathered from unlabelled data. The rationale for this  approach lies in the so-called distributional hypothesis by Zellig Harris, which states that words that occur in the same contexts tend  to have similar meanings.

While straight-forward word representations  of predicates and arguments have already been shown to be useful for  semantic analysis tasks, we show that further gains can be achieved by composing representations that model the interaction between predicate  and argument, and capture full argument spans.