ing WS1S Systems to Verify Parameterized Networks . . . . . . . . . . . . 188 Kai Baukus, Saddek Bensalem, Yassine Lakhnech and Karsten Stahl FMona: A Tool for Expressing Validation Techniques over Infinite State Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204 J.-P. Bodeveix and M. Filali Transitive Closures of Regular Relations for Verifying Infinite-State Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 220 Bengt Jonsson and Marcus Nilsson Diagnostic and Test Generation Using Static Analysis to Improve Automatic Test Generation . . . . . . . . . . . . . 235 Marius Bozga, Jean-Claude Fernandez and Lucian Ghirvu Efficient Diagnostic Generation for Boolean Equation Systems . . . . . . . . . . . . 251 Radu Mateescu Efficient Model-Checking Compositional State Space Generation with Partial Order Reductions for Asynchronous Communicating Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 266 Jean-Pierre Krimm and Laurent Mounier Checking for CFFD-Preorder with Tester Processes . . . . . . . . . . . . . . . . . . . . . . . 283 Juhana Helovuo and Antti Valmari Fair Bisimulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 299 Thomas A. Henzinger and Sriram K. Rajamani Integrating Low Level Symmetries into Reachability Analysis . . . . . . . . . . . . . 315 Karsten Schmidt Model-Checking Tools Model Checking Support for the ASM High-Level Language . . . . . . . . . . . . . . 331 Giuseppe Del Castillo and Kirsten Winter Table of

Tools and Algorithms for the Construction and Analysis of Systems. Proc. TACAS 2009

http://user.it.uu.se/~jarst116/slides/week4.pdf

Graph-Based Algorithms for Boolean Function Manipulation

Over the last three decades, a large number of evolutionary algorithms have been developed for solving multi-objective optimization problems. However, there lacks an upto-date and comprehensive software platform for researchers to properly benchmark existing algorithms and for practitioners to apply selected algorithms to solve their real-world problems. The demand of such a common tool becomes even more urgent, when the source code of many proposed algorithms has not been made publicly available. To address these issues, we have developed a MATLAB platform for evolutionary multi-objective optimization in this paper, called PlatEMO, which includes more than 50 multiobjective evolutionary algorithms and more than 100 multi-objective test problems, along with several widely used performance indicators. With a user-friendly graphical user interface, PlatEMO enables users to easily compare several evolutionary algorithms at one time and collect statistical results in Excel or LaTeX files. More importantly, PlatEMO is completely open source, such that users are able to develop new algorithms on the basis of it. This paper introduces the main features of PlatEMO and illustrates how to use it for performing comparative experiments, embedding new algorithms, creating new test problems, and developing performance indicators. Source code of PlatEMO is now available at: http://bimk.ahu.edu.cn/index.php?s=/Index/Software/index.html.

PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization [Educational Forum]

Over the last three decades, a large number of evolutionary algorithms have been developed for solving multiobjective
optimization problems. However, there lacks an up-to-date and comprehensive software platform for
researchers to properly benchmark existing algorithms and for practitioners to apply selected algorithms to solve
their real-world problems. The demand of such a common tool becomes even more urgent, when the source code
of many proposed algorithms has not been made publicly available. To address these issues, we have developed
a MATLAB platform for evolutionary multi-objective optimization in this paper, called PlatEMO, which includes
more than 50 multi-objective evolutionary algorithms and more than 100 multi-objective test problems, along with
several widely used performance indicators. With a user-friendly graphical user interface, PlatEMO enables users
to easily compare several evolutionary algorithms at one time and collect statistical results in Excel or LaTeX files.
More importantly, PlatEMO is completely open source, such that users are able to develop new algorithms on the
basis of it. This paper introduces the main features of PlatEMO and illustrates how to use it for performing comparative
experiments, embedding new algorithms, creating new test problems, and developing performance indicators.
Source code of PlatEMO is now available at: http://bimk.ahu.edu.cn/index.php?s=/Index/Software/index.html.

PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization

IEEE transactions on computer-aided design of integrated circuits and systems : a publication of the IEEE Circuits and Systems Society

This paper presents a modular framework for meta-heuristic optimization of complex optimization tasks by decomposing them into subtasks that may be designed and developed separately. Since these subtasks are generally correlated, a separate optimization is prohibited and the framework has to be capable of optimizing the subtasks concurrently. For this purpose, a distinction of genetic representation (genotype) and representation of a solution of the optimization problem (phenotype) is imposed. A compositional genotype and appropriate operators enable the separate development and testing of the optimization of subtasks by a strict decoupling. The proposed concept is implemented as open source reference OPT4J [6]. The architecture of this implementation is outlined and design decisions are discussed that enable a maximal decoupling and flexibility. A case study of a complex real-world optimization problem from the automotive domain is introduced. This case study requires the concurrent optimization of several heterogeneous aspects. Exemplary, it is shown how the proposed framework allows to efficiently optimize this complex problem by decomposing it into subtasks that are optimized concurrently.

Opt4J: a modular framework for meta-heuristic optimization

The FlexRay bus is the prospective automotive standard communication system. For the sake of a high exibility, the protocol includes a static time-triggered and a dynamic event-triggered segment. This paper is dedicated to the scheduling of the static segment in compliance with the automotive-specific AUTOSAR standard. For the determination of an optimal schedule in terms of the number of used slots, a fast greedy heuristic as well as a complete approach based on Integer Linear Programming are presented. For this purpose, a scheme for the transformation of the scheduling problem into a bin packing problem is proposed. Moreover, a metric and optimization method for the extensibility of partially used slots is introduced. Finally, the provided experimental results give evidence of the benefits of the proposed methods. On a realistic case study, the proposed methods are capable of obtaining better results in a significantly smaller amount of time compared to a commercial tool. Additionally, the experimental results provide a case study on incremental scheduling, a scalability analysis, an exploration use case, and an additional test case to emphasis the robustness and exibility of the proposed methods.

/pdf/flexray-schedule-optimization-of-the-static-segment-3kbpjt5zs4.pdf

FlexRay schedule optimization of the static segment

This paper is an introduction to security challenges for the design of automotive hardware/software architectures State-of-the-art automotive architectures are highly heterogeneous and complex systems that rely on distributed functions based on electronics and software As cars are getting more connected with their environment, the vulnerability to attacks is rapidly growing Examples for such wireless communication are keyless entry systems, WiFi, or Bluetooth Despite this increasing vulnerability, the design of automotive architectures is still mainly driven by safety and cost issues rather than security In this paper, we present potential threats and vulnerabilities, and outline upcoming security challenges in automotive architectures In particular, we discuss the challenges arising in electric vehicles, like the vulnerability to attacks involving tampering with the battery safety Finally, we discuss future automotive architectures based on Ethernet/IP and how formal verification methods might be used to increase their security

/pdf/security-challenges-in-automotive-hardware-software-30kylcu6h0.pdf

Security challenges in automotive hardware/software architecture design

We present an automatic schedule synthesis framework for applications that are mapped onto distributed time-triggered automotive platforms where multiple Electronic Control Units (ECUs) are synchronized over a FlexRay bus. We classify applications into two categories (i) safety-critical control applications with stability and performance constraints, and (ii) time-critical applications with only deadline constraints. Our proposed framework can handle such mixed constraints arising from timing, control stability, and performance requirements. In particular, we synthesize schedules that optimize control performance and respects the timing requirements of the real-time applications. An Integer Linear Programming (ILP) problem is formulated by modeling the ECU and bus schedules as a set of constraints for optimizing both linear or quadratic control performance functions.

Time-triggered implementations of mixed-criticality automotive software

Nowadays many design space exploration tools are based on Multi-Objective Evolutionary Algorithms (MOEAs). Beside the advantages of MOEAs, there is one important drawback as MOEAs might fail in design spaces containing only a few feasible solutions or as they are often afflicted with premature convergence, i.e., the same design points are revisited again and again. Exact methods, especially Pseudo Boolean solvers (PB solvers) seem to be a solution. However, as typical design spaces are multi-objective, there is a need for multi-objective PB solvers. In this paper, we will formalize the problem of design space exploration as multi-objective 0--1 ILP. We will propose (1) a heuristic approach based on PB solvers and (2) a complete multi-objective PB solver based on a backtracking algorithm that incorporates the non-dominance relation from multi-objective optimization and is restricted to linear objective functions. First results from applying our novel multi-objective PB solver to synthetic problems will show its effectiveness in small sized design spaces as well as in large design spaces only containing a few feasible solutions. For non-linear and large problems, the proposed heuristic approach is outperforming common MOEA approaches. Finally, a real world example from the automotive area will emphasize the efficiency of the proposed algorithms.

/pdf/efficient-symbolic-multi-objective-design-space-exploration-4cmuwyqimh.pdf

Martin Lukasiewycz

Papers

Opt4J: a modular framework for meta-heuristic optimization

FlexRay schedule optimization of the static segment

Security challenges in automotive hardware/software architecture design

Time-triggered implementations of mixed-criticality automotive software

Efficient symbolic multi-objective design space exploration