Model-Driven Testing

A strategy for adaptive control and energetic optimization of aerobic fermentors was implemented, with both air flow and agitation speed as manipulated variables. This strategy is separable in its components: control, optimization, estimation. We optimized parameter’s estimation (from the usual KLa correlation) using sinusoidal excitation of air flow and agitation speed. We have implemented parameter’s estimation trough recursive least squares algorithm with forgetting factor. We carried separate essays on control, optimization and estimation algorithms. We carried our essays using an original computational simulation environment, with noise and delay generating facilities for data sampling and filtering.

Our results show the convergence and robustness of the estimation algorithm used, improved with use of both forgetting factor and KLa dead-band facilities. Control algorithm used in our work compares favorably with PID using the integrated area criteria for deviation between oxygen molarity and critical molarity (set point). Optimization algorithm clearly reduces energetic consumption, respecting critical molarity. Integration of control, optimization and adaptive algorithms was implemented, but future work is needed for stability. Methods were defined and implemented for stability improvement. We have implemented data acquisition and computer manipulation of air flow and agitation speed for actual fermentors.

Model-Driven Engineering (MDE) promises to enhance system development by reducing development time, and increasing productivity and quality. MDE is gaining popularity in several industry sectors, and is attractive also for critical systems where they can reduce efforts and costs for verification and validation (V&V), and can ease certification. Incorporating model-driven techniques into a legacy well-proven development cycle is not simply a matter of placing models and transformations in the design and implementation phases. We present the experience in the model-driven design and V&V of a safety-critical system in the railway domain, namely the Prolan Block, a railway interlocking system manufactured by the Hungarian company Prolan Co., required to be CENELEC SIL-4 compliant. The experience has been carried out in an industrial-academic partnership within the EU project CECRIS. We discuss the challenges and the lessons learnt in this pilot project of introducing MD design and testi...

System testing of real-time embedded systems (RTES) is a challenging task and only a fully automated testing approach can scale up to the testing requirements of industrial RTES. One such approach, which offers the advantage for testing teams to be black-box, is to use environment models to automatically generate test cases and oracles and an environment simulator to enable earlier and more practical testing. In this paper, we propose novel heuristics for search-based, RTES system testing which are based on these environment models. We evaluate the fault detection effectiveness of two search-based algorithms, i.e., Genetic Algorithms and (1+1) Evolutionary Algorithm, when using these novel heuristics and their combinations. Preliminary experiments on 13 carefully selected, non-trivial artificial problems, show that, under certain conditions, these novel heuristics are effective at bringing the environment into a state exhibiting a system fault. The heuristic combination that showed the best overall performance on the artificial problems was applied on an industrial case study where it showed consistent results.

Objective of this workshop is to discuss the current approaches and techniques as wide of illustrations and examples to clearly show the theory and practice of model driven engineering. It will be a crossroads of precious ideas into model driven engineering and will cover all of the major aspects and current trends of model driven engineering.

Embedded systems shape our world nowadays. It's almost impossible to imagine our day to day life without it. Examples can include cell phones, home appliances, energy generators, satellites, automotive components …etc. it is even far more complex if there are real-time and interface constraints. Developing real-time embedded systems is significantly challenging to developers. Results need not be only correct, but also in a timely manner. New software development approaches are needed due to the complexity of functional and non-functional requirements of embedded systems. Due to the complex context of embedded systems, defects can cause life threatening situations. Delays can create huge costs, and insufficient productivity can impact the entire industry. The rapid evolution of software engineering technologies will be a key factor in the successful future development of even more complex embedded systems. Software development is shifting from manual programming, to model-driven engineering (MDE). One of the most important challenges is to manage the increasing complexity of embedded software development, while maintaining the product's quality, reducing time to market, and reducing development cost. MDE is a promising approach that emerged lately. Instead of directly coding the software using programming languages, developers model software systems using expressive, graphical notations, which provide a higher abstraction level than programming languages. This is called Model Based Development (MBD). Model Based Development if accompanied by Model Based Validation (MBV), will help identify problems early thus reduce rework cost. Applying tests based on the designed models not only enable early detection of defects, but also continuous quality assurance. Testing can start in the first iteration of the development process. As a result of the model based approach, and in addition to the major advantage of early defects detection, several time consuming tasks within the classical software development life cycle will be excluded. For embedded systems development, it's really important to follow a more time efficient approach. KEYWORDS Embedded Systems,Model Driven Engineering, Model Driven Development, Model Driven Testing

Embedded systems shape our world nowadays. It’s almost impossible to imagine our day to day life without it. Examples can include cell phones, home appliances, energy generators, satellites, automotive components …etc. it is even far more complex if there are real-time and interface constraints. Developing real-time embedded systems is significantly challenging to developers. Results need not be only correct, but also in a timely manner. New software development approaches are needed due to the
complexity of functional and non-functional requirements of embedded systems. Due to the complex context of embedded systems, defects can cause life threatening situations. Delays can create huge costs, and insufficient productivity can impact the entire industry. The rapid evolution of software engineering technologies will be a key factor in the successful future development of even more
complex embedded systems. Software development is shifting from manual programming, to model-driven engineering (MDE). One of the most important challenges is to manage the increasing complexity of embedded software development, while maintaining the product’s quality, reducing time to market, and reducing development cost.
MDE is a promising approach that emerged lately. Instead of directly coding the software using programming languages, developers model software systems using expressive, graphical notations, which provide a higher abstraction level than programming languages. This is called Model Based Development (MBD). Model Based Development if accompanied by Model Based Validation (MBV), will help identify problems early thus reduce rework cost. Applying tests based on the designed models not only enable early detection of defects, but also continuous quality assurance. Testing can start in the first iteration of the development
process. As a result of the model based approach, and in addition to the major advantage of early defects detection,
several time consuming tasks within the classical software development life cycle will be excluded. For embedded systems development, it’s really important to follow a more time efficient approach.

Agent-based modeling and simulation (ABMS) had an increasing attention during the last decade. However, the weak validation and verification of agent-based simulation models makes ABMS hard to trust. There is no comprehensive tool set for verification and validation of agent-based simulation models which demonstrates that inaccuracies exist and/or which reveals the existing errors in the model. Moreover, on the practical side, there are many ABMS frameworks in use. In this sense, we designed and developed a generic testing framework for agent-based simulation models to conduct validation and verification of models. This paper presents our testing framework in detail and demonstrates its effectiveness by showing its applicability on a realistic agent-based simulation case study.

Embedded systems shape our world nowadays. It’s almost impossible to imagine our day to day life without
it. Examples can include cell phones, home appliances, energy generators, satellites, automotive
components …etc. it is even far more complex if there are real-time and interface constraints.
Developing real-time embedded systems is significantly challenging to developers. Results need not be only
correct, but also in a timely manner. New software development approaches are needed due to the
complexity of functional and non-functional requirements of embedded systems.
Due to the complex context of embedded systems, defects can cause life threatening situations. Delays can
create huge costs, and insufficient productivity can impact the entire industry. The rapid evolution of
software engineering technologies will be a key factor in the successful future development of even more
complex embedded systems.
Software development is shifting from manual programming, to model-driven engineering (MDE). One of
the most important challenges is to manage the increasing complexity of embedded software development,
while maintaining the product’s quality, reducing time to market, and reducing development cost.
MDE is a promising approach that emerged lately. Instead of directly coding the software using
programming languages, developers model software systems using expressive, graphical notations, which
provide a higher abstraction level than programming languages. This is called Model Based Development
(MBD).
Model Based Development if accompanied by Model Based Validation (MBV), will help identify problems
early thus reduce rework cost. Applying tests based on the designed models not only enable early detection
of defects, but also continuous quality assurance. Testing can start in the first iteration of the development
process.
As a result of the model based approach, and in addition to the major advantage of early defects detection,
several time consuming tasks within the classical software development life cycle will be excluded. For
embedded systems development, it’s really important to follow a more time efficient approach.