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Automatic code generation is an essential cornerstone of today's model-driven approaches to software engineering. Thus a key requirement for the success of this technique is the reliability and correctness of code generators. This article describes how we employ standard model checking-based verification to check that code generator models developed within our code generation framework Genesys conform to (temporal) properties. Genesys is a graphical framework for the high-level construction of code generators on the basis of an extensible library of well-defined building blocks along the lines of the Extreme Model-Driven Development paradigm. We will illustrate our verification approach by examining complex constraints for code generators, which even span entire model hierarchies. We also show how this leads to a knowledge base of rules for code generators, which we constantly extend by e.g. combining constraints to bigger constraints, or by deriving common patterns from structurally similar constraints. In our experience, the development of code generators with Genesys boils down to re-instantiating patterns or slightly modifying the graphical process model, activities which are strongly supported by verification facilities presented in this article.
In this paper we describe the recent state of our research
project concerning computer science teachers’ knowledge on students’
cognition. We did a comprehensive analysis of textbooks, curricula
and other resources, which give teachers guidance to formulate assignments.
In comparison to other subjects there are only a few concepts
and strategies taught to prospective computer science teachers in university.
We summarize them and given an overview on our empirical
approach to measure this knowledge.
Although Boolean Constraint Technology has made tremendous progress over the last decade, the efficacy of state-of-the-art solvers is known to vary considerably across different types of problem instances, and is known to depend strongly on algorithm parameters. This problem was addressed by means of a simple, yet effective approach using handmade, uniform, and unordered schedules of multiple solvers in ppfolio, which showed very impressive performance in the 2011 Satisfiability Testing (SAT) Competition. Inspired by this, we take advantage of the modeling and solving capacities of Answer Set Programming (ASP) to automatically determine more refined, that is, nonuniform and ordered solver schedules from the existing benchmarking data. We begin by formulating the determination of such schedules as multi-criteria optimization problems and provide corresponding ASP encodings. The resulting encodings are easily customizable for different settings, and the computation of optimum schedules can mostly be done in the blink of an eye, even when dealing with large runtime data sets stemming from many solvers on hundreds to thousands of instances. Also, the fact that our approach can be customized easily enabled us to swiftly adapt it to generate parallel schedules for multi-processor machines.
Manufacturing industries are undergoing a major paradigm shift towards more autonomy. Automated planning and scheduling then becomes a necessity. The Planning and Execution Competition for Logistics Robots in Simulation held at ICAPS is based on this scenario and provides an interesting testbed. However, the posed problem is challenging as also demonstrated by the somewhat weak results in 2017. The domain requires temporal reasoning and dealing with uncertainty. We propose a novel planning system based on Answer Set Programming and the Clingo solver to tackle these problems and incentivize robot cooperation. Our results show a significant performance improvement, both, in terms of lowering computational requirements and better game metrics.
We present the hybrid ASP solver clingcon, combining the simple modeling language and the high performance Boolean solving capacities of Answer Set Programming (ASP) with techniques for using non-Boolean constraints from the area of Constraint Programming (CP). The new clingcon system features an extended syntax supporting global constraints and optimize statements for constraint variables. The major technical innovation improves the interaction between ASP and CP solver through elaborated learning techniques based on irreducible inconsistent sets. A broad empirical evaluation shows that these techniques yield a performance improvement of an order of magnitude.