Refactoring and converting a TTCN-2 test suite

When deploying TTCN-3 at Nokia, we converted two TTCN-2 test systems to TTCN-3. We explain to which extend we have been able to do the conversion automatically. The conversion tool used provided a syntactically and semantically correct conversion of the TTCN-2 code. We define some improvements made on the tool to increase readability and maintainability of the resulting code. We cover aspects of converting also the non-TTCN-2 parts and describe the experiences we made as a set of lessons learnt.     

Wasserstofffreie amorphe Kohlenstoffschichten ‐ abgeschieden durch plasmaaktivierte Hochratebedampfung

Hydrogen free amorphous carbon coatings ‐ deposited with plasma‐activated high‐rate evaporation By using the technology of the plasmaactivated electron beam evaporation we deposited hydrogen free amorphous carbon coatings with deposition rates of up to 100 μm/h. The results of the mechanical and tribological investigations show the potential of these layers for the use in case of wear resistance. Hardnesses of up to 16GPa were proved. The actual state of the characterisation of the coatings is described and the outlook onto possibilities of further optimisation is given.     

丰富多彩的亚洲卫星电信市场

随着移动电视在美国的开始应用,新应用逐渐站稳了脚跟,并被美国消费者的要求所推动,而消费者的这些更丰富体验的要求在以前受到过抑制。为了在卫星电信领域更好契合这一趋势,注意亚洲发展并从其经验中总结教训这一做法是有意义的。     

Hardware development for the Bone Proteomics experiment

This paper gives a quick overview of how various scientific, operational and safety related requirements drove the development of hardware for the Bone Proteomics (BOP) experiment that was conducted on the International Space Station during the Italian Soyuz Mission in 2005. The experiment objectives will be highlighted briefly and design solutions will be presented. Comments will be given regarding the choice of a particular design solution and the impact on the complexity, cost and development time. Conclusions in this paper are based on the experience gained when developing the BOP experiment hardware and other relatively small experiment hardware packages, typical of European Soyuz Missions. The observations should not be extrapolated to large payloads. The objective of this paper is not to produce a recipe for developing experiment hardware; dedicated documents for that purpose are available elsewhere. Rather, the objective is to help others profit from experience gained in the development of relatively small experiment hardware packages, and to highlight where time and cost saving decisions can be made.     

On the effectiveness of early life cycle defect prediction with Bayesian Nets

Standard practice in building models in software engineering normally involves three steps: collecting domain knowledge (previous results, expert knowledge); building a skeleton of the model based on step 1 including as yet unknown parameters; estimating the model parameters using historical data. Our experience shows that it is extremely difficult to obtain reliable data of the required granularity, or of the required volume with which we could later generalize our conclusions. Therefore, in searching for a method for building a model we cannot consider methods requiring large volumes of data. This paper discusses an experiment to develop a causal model (Bayesian net) for predicting the number of residual defects that are likely to be found during independent testing or operational usage. The approach supports (1) and (2), does not require (3), yet still makes accurate defect predictions (an R ² of 0.93 between predicted and actual defects). Since our method does not require detailed domain knowledge it can be applied very early in the process life cycle. The model incorporates a set of quantitative and qualitative factors describing a project and its development process, which are inputs to the model. The model variables, as well as the relationships between them, were identified as part of a major collaborative project. A dataset, elicited from 31 completed software projects in the consumer electronics industry, was gathered using a questionnaire distributed to managers of recent projects. We used this dataset to validate the model by analyzing several popular evaluation measures (R ², measures based on the relative error and Pred). The validation results also confirm the need for using the qualitative factors in the model. The dataset may be of interest to other researchers evaluating models with similar aims. Based on some typical scenarios we demonstrate how the model can be used for better decision support in operational environments. We also performed sensitivity analysis in which we identified the most influential variables on the number of residual defects. This showed that the project size, scale of distributed communication and the project complexity cause the most of variation in number of defects in our model. We make both the dataset and causal model available for research use.