An approach to quality engineering of TTCN-3 test specifications

Experience with the development and maintenance of large test suites specified using the Testing and Test Control Notation (TTCN-3) has shown that it is difficult to construct tests that are concise with respect to quality aspects such as maintainability or usability. The ISO/IEC standard 9126 defines a general software quality model that substantiates the term “quality” with characteristics and subcharacteristics. The domain of test specifications, however, requires an adaption of this general model. To apply it to specific languages such as TTCN-3, it needs to be instantiated. In this paper, we present an instantiation of this model as well as an approach to assess and improve test specifications. The assessment is based on metrics and the identification of code smells. The quality improvement is based on refactoring. Example measurements using our TTCN-3 tool TRex demonstrate how this procedure is applied in practise. B. Zeiss is supported by a Ph.D. scholarship from Siemens AG, Corporate Technology.     

Efficient and Flexible Matching of Recursive Types

Equality and subtyping of recursive types were studied in the 1990s by Amadio and Cardelli; Kozen, Palsberg, and Schwartzbach; Brandt and Henglein; and others. Potential applications include automatic generation of bridge code for multilanguage systems and type-based retrieval of software modules from libraries. In this paper, we present an efficient decision procedure for a notion of type equality that includes unfolding of recursive types, and associativity and commutativity of product types. Advocated by Auerbach, Barton, and Raghavachari, these properties enable flexible matching of types. For two types of size at most n, our algorithm takes O(n) iterations each of which takes O(n) time, for a total of O(n²) time.     

ClearView: An interactive context preserving hotspot visualization technique

Volume rendered imagery often includes a barrage of 3D information like shape, appearance and topology of complex structures, and it thus quickly overwhelms the user. In particular, when focusing on a specific region a user cannot observe the relationship between various structures unless he has a mental picture of the entire data. In this paper we present ClearView, a GPU-based, interactive framework for texture-based volume ray-casting that allows users which do not have the visualization skills for this mental exercise to quickly obtain a picture of the data in a very intuitive and user-friendly way. ClearView is designed to enable the user to focus on particular areas in the data while preserving context information without visual clutter. ClearView does not require additional feature volumes as it derives any features in the data from image information only. A simple point-and-click interface enables the user to interactively highlight structures in the data. ClearView provides an easy to use interface to complex volumetric data as it only uses transparency in combination with a few specific shaders to convey focus and context information.     

Reinforcement learning to adjust parametrized motor primitives to new situations

Humans manage to adapt learned movements very quickly to new situations by generalizing learned behaviors from similar situations. In contrast, robots currently often need to re-learn the complete movement. In this paper, we propose a method that learns to generalize parametrized motor plans by adapting a small set of global parameters, called meta-parameters. We employ reinforcement learning to learn the required meta-parameters to deal with the current situation, described by states. We introduce an appropriate reinforcement learning algorithm based on a kernelized version of the reward-weighted regression. To show its feasibility, we evaluate this algorithm on a toy example and compare it to several previous approaches. Subsequently, we apply the approach to three robot tasks, i.e., the generalization of throwing movements in darts, of hitting movements in table tennis, and of throwing balls where the tasks are learned on several different real physical robots, i.e., a Barrett WAM, a BioRob, the JST-ICORP/SARCOS CBi and a Kuka KR?6.     

Efficient data management in a large-scale epidemiology research project

This article describes the concept of a "Central Data Management" (CDM) and its implementation within the large-scale population-based medical research project "Personalized Medicine". The CDM can be summarized as a conjunction of data capturing, data integration, data storage, data refinement, and data transfer. A wide spectrum of reliable "Extract Transform Load" (ETL) software for automatic integration of data as well as "electronic Case Report Forms" (eCRFs) was developed, in order to integrate decentralized and heterogeneously captured data. Due to the high sensitivity of the captured data, high system resource availability, data privacy, data security and quality assurance are of utmost importance. A complex data model was developed and implemented using an Oracle database in high availability cluster mode in order to integrate different types of participant-related data. Intelligent data capturing and storage mechanisms are improving the quality of data. Data privacy is ensured by a multi-layered role/right system for access control and de-identification of identifying data. A well defined backup process prevents data loss. Over the period of one and a half year, the CDM has captured a wide variety of data in the magnitude of approximately 5terabytes without experiencing any critical incidents of system breakdown or loss of data. The aim of this article is to demonstrate one possible way of establishing a Central Data Management in large-scale medical and epidemiological studies.     

The effect of flooring on musculoskeletal symptoms in the lower extremities and low back among female nursing assistants

This study examines the effect of changing the floor from a 2-mm homogenous vinyl floor to a 4-mm heterogeneous vinyl floor (1.5-mm wear layer and 2.5-mm foam layer) on musculoskeletal symptoms in the lower extremities and low back among nursing assistants in a geriatric care centre. A pre-post design with a reference group consisting of nursing assistants from a similar geriatric care centre was used. Follow-up measurements were carried out 12 and 24 months after the intervention by means of questionnaires. At the 1-year follow-up, the pain intensity score in the feet of the intervention group had decreased compared with the baseline value and remained statistically significant at the 2-year follow-up. The decrease in pain intensity score of the feet in the intervention group was statistically significantly different from the reference group, both after 1 and 2 years. PRACTITIONER SUMMARY: The results show the importance of flooring in the workplace with regard to reducing musculoskeletal symptoms. Appropriate flooring is especially important in the female-dominated health care sector, where workers must stand or walk for long periods.     

Large-Scale Hydrogen Production

There is a growing need for hydrogen in processing heavier and dirtier fossil fuels and a future hydrogen economy is widely suggested as the next generation fuel/energy source once fossil fuels diminish in availability. Sustainable fuels are still regarded as too expensive given the large amounts of natural gas and a projected, ample supply of fossil fuels beyond the next twenty-plus years. Today, the steam reforming of hydrocarbons is the most favorable route to H₂. If CO₂ sequestration were ever to become widely practiced, fossil fuels would continue to play an important role in the future hydrogen economy.     

Interface engineering of short-period Ni/V multilayer X-ray mirrors

Low-energy ion-assisted magnetron sputter deposition has been used for the synthesis of highly reflective Ni/V multilayer soft X-ray mirrors. A low ion energy and a high ion-to-metal flux ratio were employed in order to stimulate the adatom mobility while minimizing ion-induced intermixing at the interfaces. An analytic model, based on the binary collision approximation, was used in order to gain insight into low-energy ion–surface interactions as a function of ion energy and ion-to-metal flux ratio. The model predicted a favorable region in the ion energy-flux parameter space where only surface atomic displacements are stimulated during growth of Ni and V for multilayers. For a series of Ni/V multilayer mirrors with multilayer periods about Λ = 1.2 nm, grown with a continuous ion assistance using energies in the range 7–36 eV and with ion-to-metal flux ratios Φ_Ni = 4.7 and Φ_V=20.9, specular and diffuse X-ray scattering analyses revealed that ion energies of 27–31 eV produced the best trade-off between reduced interfacial roughness and intermixing. However, it was also concluded that an interface mixing of about ± 1 atomic distance is unavoidable when a continuous flux of assisting ions is used.

To overcome this limitation, a sophisticated interface engineering technique was employed, where the first 0.3 nm of each layer was grown with a high-flux low-energy ion assistance and the remaining part was grown with a slightly higher ion energy. This method was demonstrated to largely eliminate the intermixing while maintaining the smoothening effect of ion assistance. Two Ni/V multilayer soft X-ray mirror structures, one with 500 periods designed for near-normal incidence and one 150 periods reflecting polarizer at the Brewster angle, were grown utilizing the interface engineering concept. Both the near-normal incidence reflectivity as well as polarizability were improved by a factor of 2 as compared to previously reported data for an X-ray energy of E = 511 eV.