Policy-centric integration and dynamic composition of autonomic computing techniques

This paper presents innovative work in the development of policy-based autonomic computing. The core of the work is a powerful and flexible policy-expression language AGILE, which facilitates run-time adaptable policy configuration of autonomic systems. AGILE also serves as an integrating platform for other self-management technologies including signal processing, automated trend analysis and utility functions. Each of these technologies has specific advantages and applicability to different types of dynamic adaptation. The AGILE platform enables seamless interoperability of the different technologies to each perform various aspects of self-management within a single application. The various technologies are implemented as object components. Self-management behaviour is specified using the policy language semantics to bind the various components together as required. Since the policy semantics support run-time re-configuration, the self-management architecture is dynamically composable. Additional benefits include the standardisation of the application programmer interface, terminology and semantics, and only a single point of embedding is required

Measuring design compliance using neural language models: An automotive case study

As the modern vehicle becomes more software-defined, it is beginning to take significant effort to avoid serious regression in software design. This is because automotive software architects rely largely upon manual review of code to spot deviations from specified design principles. Such an approach is both inefficient and prone to error. In recent days, neural language models pre-trained on source code are beginning to be used for automating a variety of programming tasks. In this work, we extend the application of such a Programming Language Model (PLM) to automate the assessment of design compliance. Using a PLM, we construct a system that assesses whether a set of query programs comply with Controller-Handler, a design pattern specified to ensure hardware abstraction in automotive control software. The assessment is based upon measuring whether the geometrical arrangement of query program embeddings, extracted from the PLM, aligns with that of a set of known implementations of the pattern. The level of alignment is then transformed into an interpretable measure of compliance. Using a controlled experiment, we demonstrate that our technique determines compliance with a precision of 92%. Also, using expert review to calibrate the automated assessment, we introduce a protocol to determine the nature of the violation, helping eventual refactoring. Results from this work indicate that neural language models can provide valuable assistance to human architects in assessing and fixing violations in automotive software design

Languages for safety-certification related propertis

The Safety Certification of Software-Intensive Systems with Reusable Components project, in short SafeCer (www.safecer.eu),is targeting increased efficiency and reduced time-to-market by composable safety certification of safety- relevant embedded systems. The industrial domains targeted are within automotive and construction equipment, avionics, and rail. Some of the companies involved are: Volvo Tech- nology, Thales, TTTech, and Intecs among others. SafeCer includes more than 30 partners in six different countries and has a budget of e25.7 millions. A primary objective is to provide support for system safety arguments based on arguments and properties of system components as well as to provide support for generation of corresponding evidence in a similar compositional way. By providing support for efficient reuse of certification and stronger links between certification and development, compo- nent reuse will be facilitated, and by providing support for reuse across domains the amount of components available for reuse will increase dramatically. The resulting efficiency and reduced time to market will, together with increased quality and reduced risk, increase competitiveness and pave the way for a cross-domain market for software components qualified for certification

Swedish high school students' knowledge and attitudes regarding fertility and family building

<p>Abstract</p> <p>Background</p> <p>Infertility is a serious problem for those who suffer. Some of the risks for infertility are preventable and the individual should therefore have knowledge of them. The purposes of this study were to investigate high-school students' knowledge about fertility, plans for family building and to compare views and knowledge between female and male students.</p> <p>Methods</p> <p>A questionnaire containing 34 items was answered by 274 students. Answers from male and female students were compared using student's <it>t</it>-test for normally distributed variables and Mann-Whitney <it>U</it>-test for non-normal distributions. The chi-square test was used to compare proportions of male and female students who answered questions on nominal and ordinal scales. Differences were considered as statistically significant at a <it>p</it>-value of 0.05.</p> <p>Results</p> <p>Analyses showed that 234 (85%) intended to have children. Female students felt parenthood to be significantly more important than male students: <it>p </it>= <it><</it>0.01. The mean age at which the respondents thought they would like to start to build their family was 26 (± 2.9) years. Men believed that women's fertility declined significantly later than women did: <it>p </it>= <it><</it>0.01. Women answered that 30.7% couples were involuntarily infertile and men answered 22.5%: <it>p </it>= <it><</it>0.01. Females thought it significantly more likely that they would consider IVF or adoption than men, <it>p = </it>0.01. Men felt they were more likely to abstain from having children than women: <it>p = <</it>0.01. Women believed that body weight influenced fertility significantly more often than men: <it>p = <</it>0.01 and men believed significantly more often that smoking influenced fertility: <it>p </it>= 0.03. Both female and male students answered that they would like to have more knowledge about the area of fertility.</p> <p>Conclusions</p> <p>Young people plan to start their families when the woman's fertility is already in decline. Improving young people's knowledge about these issues would give them more opportunity to take responsibility for their sexual health and to take an active role in shaping political change to improve conditions for earlier parenthood.</p

Scheduling of Embedded Real-Time Systems: A Constraint Programming Approach

Thesis for the degree of Licentiate of Engineering, a Swedish degree between M.Sc. and Ph.D. For embedded real-time systems, a correct timing behavior must be guaranteed as a result of the design. Therefore, an important part of the design process is the allocation and scheduling of software tasks onto the hardware architecture. However, the various application constraints typically found in embedded systems significantly complicates this task. Furthermore, to be costeffective, the design of an embedded system must often be optimal, in terms of objectives such as cost and performance. In this thesis I have studied how the constraint programming paradigm can be applied to the problem of optimal allocation and scheduling for embedded real-time systems. In particular, this work addresses two major problems that appear in the construction of allocation and scheduling algorithms for such systems. First, it must be possible to model the system accurately enough to allow the expression of real-world constraints. Second, the run-time performance of the allocation and scheduling algorithm must be reasonable for typical applications. This thesis demonstrates that constraint programming offers support for the development of a scheduling framework that provide the necessary features

An optimization framework for scheduling of embedded real-time systems

Embedded real-time systems - appearing in products such as cars and mobile phones - are nowadays common in our everyday lives. Despite this fact, the design process of such systems is still cumbersome due to the large variety of design constraints that must be considered to ensure a safe operation of the system. In particular, present scheduling techniques - that analyze the timing behavior of the system - typically assume a too limited model to truly represent the system. In addition, to make the system cost-effective its design should be optimized regarding performance measures such as resource utilization, energy consumption and robustness. Unfortunately, optimization in general is very time-consuming process, often without guarantee that the best solution will be found. This thesis addresses these problems by proposing a scheduling framework that not only enables arbitrary design constraints to be modelled but also allows for design optimization. The framework is based on constraint programming, and this thesis presents how the problem of scheduling embedded real-time systems can be modeled and solved using this technique. In addition, a number of novel techniques for reducing the runtime of the optimization algorithm are presented. This includes the identification and exclusion of symmetries in the solution space as well as fast and tight estimates of how good a solution may get. Finally, this thesis contains a performance comparison between the proposed framework and other state-of-the-art scheduling algorithms. The evaluation shows that both the quality of the solutions and the optimization time is improved over previous approaches - in many cases the order of the solution time is reduced from minutes to seconds

The Performance of Constraint Programming for Off-line Scheduling of Distributed Real-Time Systems

Many real-time systems are distributed in the sense that they consist of tasks that execute on dierent nodes. As a consequence of the distribution, additional task constraints concerning communication and resource sharing are imposed to the traditional timing constraints. Unfortunately, these constraints increase the computational complexity involved in finding a feasible distributed schedule for the tasks, making an off-line approach to the scheduling problem the only viable alternative. Off-line analysis is also required if the constraints must be guaranteed to always hold and if the distributed schedule should be optimal regarding some objective. State-of-the-art scheduling algorithms for these kind of systems include the application of techniques such as branch-and-bound and simulated annealing. In this paper, we present a scheduling algorithm based on constraint programming which is a technique that originates from the area of artificial intelligence. To demonstrate its usefulness for the scheduling of distributed real-time systems, we compare the performance of our algorithm with previously proposed algorithms through a number of experiments. The results from our evaluation show that the constraint programming approach not only results in faster average runtimes but also produces more and better solutions in terms of optimality