Software Engineering and Petri Nets

This booklet contains the proceedings of the Workshop on Software Engineering and Petri Nets (SEPN), held on June 26, 2000. The workshop was held in conjunction with the 21st International Conference on Application and Theory of Petri Nets (ICATPN-2000), organised by the CPN group of the Department of Computer Science, University of Aarhus, Denmark. The SEPN workshop papers are available in electronic form via the web page:http://www.daimi.au.dk/pn2000/proceeding

Proceedings of Monterey Workshop 2001 Engineering Automation for Sofware Intensive System Integration

The 2001 Monterey Workshop on Engineering Automation for Software Intensive System Integration was sponsored by the Office of Naval Research, Air Force Office of Scientific Research, Army Research Office and the Defense Advance Research Projects Agency. It is our pleasure to thank the workshop advisory and sponsors for their vision of a principled engineering solution for software and for their many-year tireless effort in supporting a series of workshops to bring everyone together.This workshop is the 8 in a series of International workshops. The workshop was held in Monterey Beach Hotel, Monterey, California during June 18-22, 2001. The general theme of the workshop has been to present and discuss research works that aims at increasing the practical impact of formal methods for software and systems engineering. The particular focus of this workshop was "Engineering Automation for Software Intensive System Integration". Previous workshops have been focused on issues including, "Real-time & Concurrent Systems", "Software Merging and Slicing", "Software Evolution", "Software Architecture", "Requirements Targeting Software" and "Modeling Software System Structures in a fastly moving scenario".Office of Naval ResearchAir Force Office of Scientific Research Army Research OfficeDefense Advanced Research Projects AgencyApproved for public release, distribution unlimite

Methodologies synthesis

This deliverable deals with the modelling and analysis of interdependencies between critical infrastructures, focussing attention on two interdependent infrastructures studied in the context of CRUTIAL: the electric power infrastructure and the information infrastructures supporting management, control and maintenance functionality. The main objectives are: 1) investigate the main challenges to be addressed for the analysis and modelling of interdependencies, 2) review the modelling methodologies and tools that can be used to address these challenges and support the evaluation of the impact of interdependencies on the dependability and resilience of the service delivered to the users, and 3) present the preliminary directions investigated so far by the CRUTIAL consortium for describing and modelling interdependencies

Proceedings of the 1st Doctoral Consortium at the European Conference on Artificial Intelligence (DC-ECAI 2020)

1st Doctoral Consortium at the European Conference on Artificial Intelligence (DC-ECAI 2020), 29-30 August, 2020 Santiago de Compostela, SpainThe DC-ECAI 2020 provides a unique opportunity for PhD students, who are close to finishing their doctorate research, to interact with experienced researchers in the field. Senior members of the community are assigned as mentors for each group of students based on the student’s research or similarity of research interests. The DC-ECAI 2020, which is held virtually this year, allows students from all over the world to present their research and discuss their ongoing research and career plans with their mentor, to do networking with other participants, and to receive training and mentoring about career planning and career option

Testing Self-Adaptive Systems

Autonomy is the most demanded yet hard-to-achieve feature of recent and future software systems. Self-driving cars, mail-delivering drones, automated guided vehicles in production sites, and housekeeping robots need to decide autonomously during most of their operation time. As soon as human intervention becomes necessary, the cost of ownership increases, and this must be avoided. Although the algorithms controlling autonomous systems become more and more intelligent, their hardest opponent is their inflexibility. The more environmental situations such a system is confronted with, the more complexity the control of the autonomous system will have to master. To cope with this challenge, engineers have approached a system design, which adopts feedback loops from nature. The resulting architectural principle, which they call self-adaptive systems, follows the idea of iteratively gathering sensor data, analyzing it, planning new adaptations of the system, and finally executing the plan. Often, adaptation means to alter the system setup, re-wire components, or even exchange control algorithms to keep meeting goals and requirements in the newly appeared situation. Although self-adaptivity helps engineers to organize the vast amount of information in a self-deciding system, it remains hard to deal with the variety of contexts, which involve both environmental influences and knowledge about the system\'s internals. This challenge not only holds for the construction phase but also for verification and validation, including software test. To assure sufficient quality of a system, it must be tested under an enormous and, thus, unmanageable, number of different contextual situations and manual test-cases. This thesis proposes a novel set of methods and model types, which help test engineers to specify precisely what they expect from a self-adaptive system under test. The formal nature of the introduced artifacts allows for automatically generating test-suites or running simulations in the loop so that a qualitative verdict on the system\'s correctness can be gained. Additional to these conceptional contributions, the thesis describes a model-based adaptivity test environment, which test engineers can use for testing actual self-adaptive systems. The implementation includes comprehensive tooling for creating the introduced types of models, generating test-cases, simulating them in the loop, automating tests, and reporting. Composing all enabling components for these tasks constitutes a reference architecture of integrated test environments for self-adaptive systems. We demonstrate the completeness and accuracy of the technical approach together with the underlying concepts by evaluating them in an experimental case study where an autonomous robot interacts with human co-workers. In summary, this thesis proposes concepts for automatically and, thus, efficiently testing self-adaptive systems. The quality, which is fostered by this novel approach, is resilience: the ability of a system to maintain its promises while facing changing environments.:1 Introduction 1 1.1 Problem Description 1 1.2 Overview of Adopted Methods 3 1.3 Hypothesis and Main Contributions 4 1.4 Organization of This Thesis 5 I Foundations 7 2 Background 9 2.1 Self-adaptive Software and Autonomic Computing 9 2.1.1 Common Principles and Components of SAS 10 2.1.2 Concrete Implementations and Applications of SAS 12 2.2 Model-based Testing 13 2.2.1 Testing for Dependability 14 2.2.2 The Basics of Testing 15 2.2.3 Automated Test Design 18 2.3 Dynamic Variability Management 22 2.3.1 Software Product Lines 23 2.3.2 Dynamic Software Product Lines 25 3 Related Work: Existing Research on Testing Self-Adaptive Systems 29 3.1 Testing Context-Aware Applications 30 3.2 The SimSOTA Project 31 3.3 Dynamic Variability in Complex Adaptive Systems (DiVA) 33 3.4 Other Early-Stage Research 34 3.5 Taxonomy of Requirements of Model-based SAS Testing 36 II Methods 39 4 Model-driven SAS Testing 41 4.1 Problem/Solution Fit 41 4.2 Example: Surveillance Drone 43 4.3 Concepts and Models for Testing Self-Adaptive Systems 44 4.3.1 Test Case Generation vs. Simulation in the Loop 44 4.3.2 Incremental Modeling Process 45 4.3.3 Basic Representation Format: Petri Nets 46 4.3.4 Context Variation 50 4.3.5 Modeling Adaptive Behavior 53 4.3.6 Dynamic Context Change 57 4.3.7 Interfacing Context from Behavioral Representation 62 4.3.8 Adaptation Mode Variation 64 4.3.9 Context-Dependent Recon guration 67 4.4 Adequacy Criteria for SAS Test Models 71 4.5 Discussion on the Viability of the Employed Models 71 4.6 Comparison to Related Work 73 4.7 Summary and Discussion 74 5 Model-based Adaptivity Test Environment 75 5.1 Technological Foundation 76 5.2 MATE Base Components 77 5.3 Metamodel Implementation 78 5.3.1 Feature-based Variability Model 79 5.3.2 Abstract and Concrete Syntax for Textual Notations 80 5.3.3 Adaptive Petri Nets 86 5.3.4 Stimulus and Recon guration Automata 87 5.3.5 Test Suite and Report Model 87 5.4 Test Generation Framework 87 5.5 Test Automation Framework 91 5.6 MATE Tooling and the SAS Test Process 93 5.6.1 Test Modeling 94 5.6.2 Test Case Generation 95 5.6.3 Test Case Execution and Test Reporting 96 5.6.4 Interactive Simulation Frontend 96 5.7 Summary and Discussion 97 III Evaluation 99 6 Experimental Study: Self-Adaptive Co-Working Robots 101 6.1 Robot Teaching and Co-Working with WEIR 103 6.1.1 WEIR Hardware Components 104 6.1.2 WEIR Software Infrastructure 105 6.1.3 KUKA LBR iiwa as WEIR Manipulator 106 6.1.4 Self-Adaptation Capabilities of WEIR 107 6.2 Cinderella as Testable Co-Working Application 109 6.2.1 Cinderella Setup and Basic Functionality 109 6.2.2 Co-Working with Cinderella 110 6.3 Testing Cinderella with MATE 112 6.3.1 Automating Test Execution 112 6.3.2 Modeling Cinderella in MATE 113 6.3.3 Testing Cinderella in the Loop 121 6.4 Evaluation Verdict and Summary 123 7 Summary and Discussion 125 7.1 Summary of Contributions 126 7.2 Open Research Questions 127 Bibliography 129 Appendices 137 Appendix Cinderella De nitions 139 1 Cinderella Adaptation Bounds 139 2 Cinderella Self-adaptive Workflow 14

Advances in Robotics, Automation and Control

The book presents an excellent overview of the recent developments in the different areas of Robotics, Automation and Control. Through its 24 chapters, this book presents topics related to control and robot design; it also introduces new mathematical tools and techniques devoted to improve the system modeling and control. An important point is the use of rational agents and heuristic techniques to cope with the computational complexity required for controlling complex systems. Through this book, we also find navigation and vision algorithms, automatic handwritten comprehension and speech recognition systems that will be included in the next generation of productive systems developed by man

Robust and cheating-resilient power auctioning on Resource Constrained Smart Micro-Grids

The principle of Continuous Double Auctioning (CDA) is known to provide an efficient way of matching supply and demand among distributed selfish participants with limited information. However, the literature indicates that the classic CDA algorithms developed for grid-like applications are centralised and insensitive to the processing resources capacity, which poses a hindrance for their application on resource constrained, smart micro-grids (RCSMG). A RCSMG loosely describes a micro-grid with distributed generators and demand controlled by selfish participants with limited information, power storage capacity and low literacy, communicate over an unreliable infrastructure burdened by limited bandwidth and low computational power of devices. In this thesis, we design and evaluate a CDA algorithm for power allocation in a RCSMG. Specifically, we offer the following contributions towards power auctioning on RCSMGs. First, we extend the original CDA scheme to enable decentralised auctioning. We do this by integrating a token-based, mutual-exclusion (MUTEX) distributive primitive, that ensures the CDA operates at a reasonably efficient time and message complexity of O(N) and O(logN) respectively, per critical section invocation (auction market execution). Our CDA algorithm scales better and avoids the single point of failure problem associated with centralised CDAs (which could be used to adversarially provoke a break-down of the grid marketing mechanism). In addition, the decentralised approach in our algorithm can help eliminate privacy and security concerns associated with centralised CDAs. Second, to handle CDA performance issues due to malfunctioning devices on an unreliable network (such as a lossy network), we extend our proposed CDA scheme to ensure robustness to failure. Using node redundancy, we modify the MUTEX protocol supporting our CDA algorithm to handle fail-stop and some Byzantine type faults of sites. This yields a time complexity of O(N), where N is number of cluster-head nodes; and message complexity of O((logN)+W) time, where W is the number of check-pointing messages. These results indicate that it is possible to add fault tolerance to a decentralised CDA, which guarantees continued participation in the auction while retaining reasonable performance overheads. In addition, we propose a decentralised consumption scheduling scheme that complements the auctioning scheme in guaranteeing successful power allocation within the RCSMG. Third, since grid participants are self-interested we must consider the issue of power theft that is provoked when participants cheat. We propose threat models centred on cheating attacks aimed at foiling the extended CDA scheme. More specifically, we focus on the Victim Strategy Downgrade; Collusion by Dynamic Strategy Change, Profiling with Market Prediction; and Strategy Manipulation cheating attacks, which are carried out by internal adversaries (auction participants). Internal adversaries are participants who want to get more benefits but have no interest in provoking a breakdown of the grid. However, their behaviour is dangerous because it could result in a breakdown of the grid. Fourth, to mitigate these cheating attacks, we propose an exception handling (EH) scheme, where sentinel agents use allocative efficiency and message overheads to detect and mitigate cheating forms. Sentinel agents are tasked to monitor trading agents to detect cheating and reprimand the misbehaving participant. Overall, message complexity expected in light demand is O(nLogN). The detection and resolution algorithm is expected to run in linear time complexity O(M). Overall, the main aim of our study is achieved by designing a resilient and cheating-free CDA algorithm that is scalable and performs well on resource constrained micro-grids. With the growing popularity of the CDA and its resource allocation applications, specifically to low resourced micro-grids, this thesis highlights further avenues for future research. First, we intend to extend the decentralised CDA algorithm to allow for participants’ mobile phones to connect (reconnect) at different shared smart meters. Such mobility should guarantee the desired CDA properties, the reliability and adequate security. Secondly, we seek to develop a simulation of the decentralised CDA based on the formal proofs presented in this thesis. Such a simulation platform can be used for future studies that involve decentralised CDAs. Third, we seek to find an optimal and efficient way in which the decentralised CDA and the scheduling algorithm can be integrated and deployed in a low resourced, smart micro-grid. Such an integration is important for system developers interested in exploiting the benefits of the two schemes while maintaining system efficiency. Forth, we aim to improve on the cheating detection and mitigation mechanism by developing an intrusion tolerance protocol. Such a scheme will allow continued auctioning in the presence of cheating attacks while incurring low performance overheads for applicability in a RCSMG