A comparative reliability analysis of ETCS train radio communications

StoCharts have been proposed as a UML statechart extension for performance and dependability evaluation, and were applied in the context of train radio reliability assessment to show the principal tractability of realistic cases with this approach. In this paper, we extend on this bare feasibility result in two important directions. First, we sketch the cornerstones of a mechanizable translation of StoCharts to MoDeST. The latter is a process algebra-based formalism supported by the Motor/Möbius tool tandem. Second, we exploit this translation for a detailed analysis of the train radio case study

From StoCharts to MoDeST: a comparative reliability analysis of train radio communications

StoCharts have been proposed as a UML statechart extension for performance and dependability evaluation, and have been applied in the context of train radio reliability assessment to show the principal tractability of realistic cases with this approach. In this paper, we extend on this bare feasibility result in two important directions. First, we sketch the cornerstones of a mechanizable translation of StoCharts to MoDeST. The latter is a process algebra-based formalism supported by the Motor/Möbius tool tandem. Second, we exploit this translation for a detailed analysis of the train radio case study

Distributed Parametric and Statistical Model Checking

Statistical Model Checking (SMC) is a trade-off between testing and formal verification. The core idea of the approach is to conduct some simulations of the system and verify if they satisfy some given property. In this paper we show that SMC is easily parallelizable on a master/slaves architecture by introducing a series of algorithms that scale almost linearly with respect to the number of slave computers. Our approach has been implemented in the UPPAAL SMC toolset and applied on non-trivial case studies.Comment: In Proceedings PDMC 2011, arXiv:1111.006

Revisiting Underapproximate Reachability for Multipushdown Systems

Boolean programs with multiple recursive threads can be captured as pushdown automata with multiple stacks. This model is Turing complete, and hence, one is often interested in analyzing a restricted class that still captures useful behaviors. In this paper, we propose a new class of bounded under approximations for multi-pushdown systems, which subsumes most existing classes. We develop an efficient algorithm for solving the under-approximate reachability problem, which is based on efficient fix-point computations. We implement it in our tool BHIM and illustrate its applicability by generating a set of relevant benchmarks and examining its performance. As an additional takeaway, BHIM solves the binary reachability problem in pushdown automata. To show the versatility of our approach, we then extend our algorithm to the timed setting and provide the first implementation that can handle timed multi-pushdown automata with closed guards.Comment: 52 pages, Conference TACAS 202

Inferring Different Types of Lindenmayer Systems Using Artificial Intelligence

Lindenmayer systems (L-systems) are a formal grammar system which consist of a set of rewriting rules. Each rewriting rule is comprised of a symbol to replace (predecessor), a replacement string (successor), and an optional condition that is necessary for replacement. Starting with an initial string, every symbol in the string is replaced in parallel in accordance with the conditions on the rewriting rules, to produce a new string. The replacement process iterates as needed to produce a sequence of strings. There are different types of L-systems, which allow for different types of conditions, and methods of selecting the rules to apply. Some symbols of the alphabet can be interpreted as instructions for simulation software towards process modelling, where each string describes another step of the simulated process. Typically, creating an L-system for a specific process is done by experts by making meticulous measurements and using a priori knowledge about the process. It would be desirable to have a method to automatically learn the L-systems (the simulation program) from data, such as from a temporal sequence of images. This thesis presents a suite of tools, collectively called the Plant Model Inference Tools or PMIT (despite the name, the tools are domain agnostic), for inferring different types of L-systems using only a sequence of strings describing the process over some initial time period. Variants of PMIT are created for deterministic context-free L-systems, stochastic L-systems, and parametric L-systems. They are each evaluated using existing known deterministic and parametric L-systems from the literature, and procedurally generated stochastic L-systems. Accuracy can be detected in various ways, such as checking whether the inferred L-system is equal to the original one. PMIT is able to correctly infer deterministic L-systems with up to 31 symbols in the alphabet compared to the previous state-of-the-art algorithm's limit of 2 symbols. Stochastic L-systems allow symbols in the alphabet to have multiple rewriting rules each with an associated probability of being selected. Evaluating stochastic L-system inference with 960 procedurally generated L-systems with multiple sequences of strings as input found the following: 1) when 3 input sequences are used, the inferred successors always matched the original successors for systems with up to 9 rewriting rules, 2) when 6 sequences of strings are used, the difference between the associated probabilities of the inferred and the original L-system is approximately 1%. Parametric L-systems allow symbols to have multiple rewriting rules with parameters that get passed during rewriting. Rule selection is based on an associated Boolean condition over the parameters that gets evaluated to choose the rule to be applied. Inference is done in two steps. In the first step, the successors are inferred, and in the second step, appropriate Boolean conditions are found. Parametric L-system inference was evaluated on 20 known parametric L-systems. For 18 of the 20 L-systems where all successors were non-empty, the successors were correctly identified, but the time taken was up to 26 days on a single core CPU for the largest L-system. The second step, inferring the Boolean conditions, was successful for all 20 systems in the test set. No previous algorithm from the literature had implemented stochastic or parametric L-system inference. Inferring L-systems of greater complexity algorithmically can save considerable time and effort versus constructing them manually; however, perhaps more importantly rather than relying on existing knowledge, inferring a simulation of a process from data can help reveal the underlying scientific principles of the process

Crafting chaos: computational design of contraptions with complex behaviour

The 2010s saw the democratisation of digital fabrication technologies. Although this phenomenon made fabrication more accessible, physical assemblies displaying a complex behaviour are still difficult to design. While many methods support the creation of complex shapes and assemblies, managing a complex behaviour is often assumed to be a tedious aspect of the design process. As a result, the complex parts of the behaviour are either deemed negligible (when possible) or managed directly by the software, without offering much fine-grained user control. This thesis argues that efficient methods can support designers seeking complex behaviours by increasing their level of control over these behaviours. To demonstrate this, I study two types of artistic devices that are particularly challenging to design: drawing machines, and chain reaction contraptions. These artefacts’ complex behaviour can change dramatically even as their components are moved by a small amount. The first case study aims to facilitate the exploration and progressive refinement of complex patterns generated by drawing machines under drawing-level user-defined constraints. The approach was evaluated with a user study, and several machines drawing the expected pattern were fabricated. In the second case study, I propose an algorithm to optimise the layout of complex chain reaction contraptions described by a causal graph of events in order to make them robust to uncertainty. Several machines optimised with this method were successfully assembled and run. This thesis makes the following contributions: (1) support complex behaviour specifications; (2) enable users to easily explore design variations that respect these specifications; and (3) optimise the layout of a physical assembly to maximise the probability of real-life success