Progress beyond the state-of-the-art

2.2 Progress beyond the state-of-the-art

The objective of DECISIVE is to deliver an integrated tool chain consolidating novel technology for evolutionary software development and run-time platform support for monitoring. As such a tool chain is not yet available; the project will set a new standard in this field.

DECISIVE will extend state-of-the-art with respect to the above identified aspects as follows:

1. 
   Existing modelling languages, such as MARTE and SysML, lack a structure to model both requirements and actual properties of the system. They all focus on modelling desired behaviours and properties, but methods to store properties obtained from the actual system are not supported. A modelling language, which allows storage of empirical properties, must be able to express that these properties may be attained at different stages during the development process, and with varying degree of quality and confidence. In fact, it is often the case that conflicting, or at least non-consistent, data is obtained during the life cycle of a product (e.g. the longest response-time for an event could be quite different when obtained through scheduling analysis, simulation, testing or observation of the final system).
   

For early impact analysis, contemporary techniques lack the support to analyze subsystems of various abstraction levels. E.g. scheduling analysis and execution-time analysis typically depend on clock-cycle accurate representation of the final executable, whereas high-level analysis with e.g. Petri-nets and time-automata cannot accurately and safely model execution characteristics of modern hardware platforms. Thus, hybrid techniques are called for.

2. 
   Existing model-to-model, and model-to-code, transformations focus on semantic preservation, and sometimes, also property-preservation (e.g. the CHESS modelling language, currently developed in an ARTEMIS-project ending in 2011). However, there is no support in the transformations that allow back-tracing of properties from the target to the source. E.g. there are no methods that allow the memory associated to a signal-queue to be traced back to a particular connection between two components, or more complex, to associate the execution time of a task to the response-time of a signal path in the model. In order to obtain such traceability, we need both to associate meta-data to the target-models, indicating their sources, and provide for structured and automated insertions of probes in the target to allow tracing of interesting properties. Furthermore, processing of the probe-data in order to extract relevant properties and relate them back to the model needs new model-guided analysis technologies and text-to-model transformation techniques.
   

3. 
   An advantage of graphical models is their intuitiveness. However, the graphical modelling of realistic applications often results in very large and unmanageable graphics, severely compromising their readability and practical use. The DECISIVE Project will provide a methodology, which seeks to support a system developer in modelling, simulation and comprehending complex system models and their analysis results.
   

The classical paradigm to animate a simulated system is to highlight active components, e.g., by marking them in a particular colour. When the total number of graphical elements goes significantly beyond what can be visible on a screen simultaneously, keeping track of the active objects of a system becomes a rather frustrating exercise. In the DECISIVE project we will develop methods to simulate a system, which allows keeping track of the system simulation state. These methods will allow observing the simulation and analysis results simultaneously and with respect to their meaning for a better system comprehension.

When decisions are made for product portfolios, the best available information must be used. The same is true for architectural and design decisions. Decision-making can be improved through better ways to extract, collect, and present information that is requested for well-informed decisions. The technical information available in models and through simulations can improve the decision-making in early phases. Several aspects need to be further developed to improve the situation. Firstly, the needs from decision-makers should be made clear in the light of available and emerging modelling techniques, i.e. decision-makers are not always aware of what information actually is available early in the development process. Secondly, the extraction of the information must to be automated based on the specified needs from the decision-makers. Finally, the collection and presentation of the information must be made easily retrievable when required.

Relate following table to themes!

Topic	State of the art	Decisive target
Modelling languages
Model transformations
Modelling methodology
Design decisions