A Lightweight Multilevel Markup Language for Connecting Software Requirements and Simulations

[Context] Simulation is a powerful tool to validate specified requirements especially for complex systems that constantly monitor and react to characteristics of their environment. The simulators for such systems are complex themselves as they simulate multiple actors with multiple interacting functions in a number of different scenarios. To validate requirements in such simulations, the requirements must be related to the simulation runs. [Problem] In practice, engineers are reluctant to state their requirements in terms of structured languages or models that would allow for a straightforward relation of requirements to simulation runs. Instead, the requirements are expressed as unstructured natural language text that is hard to assess in a set of complex simulation runs. Therefore, the feedback loop between requirements and simulation is very long or non-existent at all. [Principal idea] We aim to close the gap between requirements specifications and simulation by proposing a lightweight markup language for requirements. Our markup language provides a set of annotations on different levels that can be applied to natural language requirements. The annotations are mapped to simulation events. As a result, meaningful information from a set of simulation runs is shown directly in the requirements specification. [Contribution] Instead of forcing the engineer to write requirements in a specific way just for the purpose of relating them to a simulator, the markup language allows annotating the already specified requirements up to a level that is interesting for the engineer. We evaluate our approach by analyzing 8 original requirements of an automotive system in a set of 100 simulation runs

Incremental modular testing for AOP

By designing systems as sets of modules that can be composed into larger applications, developers unleasha multitude of advantages. The promise of AOP (Aspect-Oriented Programming) is to enable developers toorganize crosscutting concerns into separate units of modularity making it easier to accomplish this vision.However, AOP does not allow unit tests to be untangled, which impairs the development of properly testedindependent modules. This paper presents a technique that enables developers to encapsulate crosscuttingconcerns using AOP and still be able to develop reusable unit tests. Our approach uses incremental testingand invasive aspects to modify and adapt tests. The approach was evaluated in a medium scale project withpromising results. Without using the proposed technique, due to the presence of invasive aspects, some unittests would have to be discarded or modified to accommodate the changes made by them. This would havea profound impact on the overall modularity and, in particular, on the reusability of those modules. We willshow that this technique enables proper unit tests that can be reused even when coupled with aspect-orientedcode

Towards a novel framework for the assessment of enterprise application integration packages

In addressing enterprise integration problems, a diversity of technologies such as CORBA and XML were promoted, yet no single integration technology solves all integration problems. As a result, a new generation of software called Enterprise Application Integration (EAI) is emerging to addresses many integration problems by combining a diversity of integration technologies (e.g. message brokers, adapters, XML). Since EAI is a new research area, there is an absence of literature discussing issues like its adoption, evaluation and implementation. This paper, examines the application of two frameworks for the evaluation of EAI packages in the practical arena. In doing so, the authors use case study strategy to investigate integration issues. Empirical data derived from the case study suggest additions to the two evaluation frameworks. Therefore, the authors revised and extend previous works by proposing a novel evaluation framework for the assessment of EAI packages. The proposed framework makes novel contribution at two levels. First, at the conceptual level, as it incorporates criteria identified separately in previous studies as evaluation criteria. The proposed framework can be used as a decision-making tool and, supports management when taking decisions regarding the adoption of EAI. Additionally, it can be used by researchers to analyse and understand the capabilities o

The NASA SBIR product catalog

The purpose of this catalog is to assist small business firms in making the community aware of products emerging from their efforts in the Small Business Innovation Research (SBIR) program. It contains descriptions of some products that have advanced into Phase 3 and others that are identified as prospective products. Both lists of products in this catalog are based on information supplied by NASA SBIR contractors in responding to an invitation to be represented in this document. Generally, all products suggested by the small firms were included in order to meet the goals of information exchange for SBIR results. Of the 444 SBIR contractors NASA queried, 137 provided information on 219 products. The catalog presents the product information in the technology areas listed in the table of contents. Within each area, the products are listed in alphabetical order by product name and are given identifying numbers. Also included is an alphabetical listing of the companies that have products described. This listing cross-references the product list and provides information on the business activity of each firm. In addition, there are three indexes: one a list of firms by states, one that lists the products according to NASA Centers that managed the SBIR projects, and one that lists the products by the relevant Technical Topics utilized in NASA's annual program solicitation under which each SBIR project was selected

Integrating EEG and MEG signals to improve motor imagery classification in brain-computer interfaces

We propose a fusion approach that combines features from simultaneously recorded electroencephalographic (EEG) and magnetoencephalographic (MEG) signals to improve classification performances in motor imagery-based brain-computer interfaces (BCIs). We applied our approach to a group of 15 healthy subjects and found a significant classification performance enhancement as compared to standard single-modality approaches in the alpha and beta bands. Taken together, our findings demonstrate the advantage of considering multimodal approaches as complementary tools for improving the impact of non-invasive BCIs