Uncertainty in coupled models of cyber-physical systems

The development of cyber-physical systems typically involves the association between multiple coupled models that capture different aspects of the system and the environment where it operates. Due to the dynamic aspect of the environment, unexpected conditions and uncertainty may impact the system. In this work, we tackle this problem and propose a taxonomy for characterizing uncertainty in coupled models. Our taxonomy extends existing proposals to cope with the particularities of coupled models in cyber-physical systems. In addition, our taxonomy discusses the notion of uncertainty propagation to other parts of the system. This allows for studying and (in some cases) quantifying the effects of uncertainty on other models in a system even at design time. We show the applicability of our uncertainty taxonomy in real use cases motivated by our envisioned scenario of automotive development

New Opportunities for Integrated Formal Methods

Formal methods have provided approaches for investigating software engineering fundamentals and also have high potential to improve current practices in dependability assurance. In this article, we summarise known strengths and weaknesses of formal methods. From the perspective of the assurance of robots and autonomous systems~(RAS), we highlight new opportunities for integrated formal methods and identify threats to their adoption to be mitigated. Based on these opportunities and threats, we develop an agenda for fundamental and empirical research on integrated formal methods and for successful transfer of validated research to RAS assurance. Furthermore, we outline our expectations on useful outcomes of such an agenda

Using Abstraction in Modular Verification of Synchronous Adaptive Systems

Self-adaptive embedded systems autonomously adapt to changing environment conditions to improve their functionality and to increase their dependability by downgrading functionality in case of fail- ures. However, adaptation behaviour of embedded systems significantly complicates system design and poses new challenges for guaranteeing system correctness, in particular vital in the automotive domain. Formal verification as applied in safety-critical applications must therefore be able to address not only temporal and functional properties, but also dynamic adaptation according to external and internal stimuli. In this paper, we introduce a formal semantic-based framework to model, specify and verify the functional and the adaptation behaviour of syn- chronous adaptive systems. The modelling separates functional and adap- tive behaviour to reduce the design complexity and to enable modular reasoning about both aspects independently as well as in combination. By an example, we show how to use this framework in order to verify properties of synchronous adaptive systems. Modular reasoning in com- bination with abstraction mechanisms makes automatic model checking efficiently applicable

One evaluation of model-based testing and its automation

Model-based testing relies on behavior models for the generation of model traces: input and expected output - test cases - for an implementation. We use the case study of an automotive network controller to assess different test suites in terms of error detection, model coverage, and implementation coverage. Some of these suites were generated automatically with and without models, purely at random, and with dedicated functional test selection criteria. Other suites were derived manually, with and without the model at hand. Both automatically and manually derived model-based test suites detected significantly more requirements errors than hand-crafted test suites that were directly derived from the requirements. The number of detected programming errors did not depend on the use of models. Automatically generated model-based test suites detected as many errors as hand-crafted model-based suites with the same number of tests. A sixfold increase in the number of model-based tests led to an 11% increase in detected errors

Anytime diagnosis for reconfiguration

Many domains require scalable algorithms that help to determine diagnoses efficiently and often within predefined time limits. Anytime diagnosis is able to determine solutions in such a way and thus is especially useful in real-time scenarios such as production scheduling, robot control, and communication networks management where diagnosis and corresponding reconfiguration capabilities play a major role. Anytime diagnosis in many cases comes along with a trade-off between diagnosis quality and the efficiency of diagnostic reasoning. In this paper we introduce and analyze FLEXDIAG which is an anytime direct diagnosis approach. We evaluate the algorithm with regard to performance and diagnosis quality using a configuration benchmark from the domain of feature models and an industrial configuration knowledge base from the automotive domain. Results show that FLEXDIAG helps to significantly increase the performance of direct diagnosis search with corresponding quality tradeoffs in terms of minimality and accuracy

e-Business challenges and directions: important themes from the first ICE-B workshop

A three-day asynchronous, interactive workshop was held at ICE-B’10 in Piraeus, Greece in July of 2010. This event captured conference themes for e-Business challenges and directions across four subject areas: a) e-Business applications and models, b) enterprise engineering, c) mobility, d) business collaboration and e-Services, and e) technology platforms. Quality Function Deployment (QFD) methods were used to gather, organize and evaluate themes and their ratings. This paper summarizes the most important themes rated by participants: a) Since technology is becoming more economic and social in nature, more agile and context-based application develop methods are needed. b) Enterprise engineering approaches are needed to support the design of systems that can evolve with changing stakeholder needs. c) The digital native groundswell requires changes to business models, operations, and systems to support Prosumers. d) Intelligence and interoperability are needed to address Prosumer activity and their highly customized product purchases. e) Technology platforms must rapidly and correctly adapt, provide widespread offerings and scale appropriately, in the context of changing situational contexts

A Study on the Influence of Software and Hardware Features on Program Energy

International audienceSoftware energy consumption has emerged as a growing concern in recent years. Managing the energy consumed by a software is, however, a difficult challenge due to the large number of factors affecting it – namely, features of the processor , memory, cache, and other hardware components, characteristics of the program and the workload running, OS routines, compiler optimisations, among others. In this paper we study the relevance of numerous architectural and program features (static and dynamic) to the energy consumed by software. The motivation behind the study is to gain an understanding of the features affecting software energy and to provide recommendations on features to op-timise for energy efficiency. In our study we used 58 subject desktop programs, each with their own workload, and from different application domains. We collected over 100 hardware and software met-rics, statically and dynamically, using existing tools for program analysis, instrumentation and run time monitoring. We then performed statistical feature selection to extract the features relevant to energy consumption. We discuss potential optimisations for the selected features. We also examine whether the energy-relevant features are different from those known to affect software performance. The features commonly selected in our experiments were execution time, cache accesses, memory instructions, context switches, CPU migrations, and program length (Halstead metric). All of these features are known to affect software performance, in terms of running time, power consumed and latency

Machine Learning-based Test Selection for Simulation-based Testing of Self-driving Cars Software

Simulation platforms facilitate the development of emerging Cyber-Physical Systems (CPS) like self-driving cars (SDC) because they are more efficient and less dangerous than field operational test cases. Despite this, thoroughly testing SDCs in simulated environments remains challenging because SDCs must be tested in a sheer amount of long-running test cases. Past results on software testing optimization have shown that not all the test cases contribute equally to establishing confidence in test subjects' quality and reliability, and the execution of "safe and uninformative" test cases can be skipped to reduce testing effort. However, this problem is only partially addressed in the context of SDC simulation platforms. In this paper, we investigate test selection strategies to increase the cost-effectiveness of simulation-based testing in the context of SDCs. We propose an approach called SDC-Scissor (SDC coSt-effeCtIve teSt SelectOR) that leverages Machine Learning (ML) strategies to identify and skip test cases that are unlikely to detect faults in SDCs before executing them. Our evaluation shows that SDC-Scissor outperforms the baselines. With the Logistic model, we achieve an accuracy of 70%, a precision of 65%, and a recall of 80% in selecting tests leading to a fault and improved testing cost-effectiveness. Specifically, SDC-Scissor avoided the execution of 50% of unnecessary tests as well as outperformed two baseline strategies. Complementary to existing work, we also integrated SDC-Scissor into the context of an industrial organization in the automotive domain to demonstrate how it can be used in industrial settings.Comment: arXiv admin note: substantial text overlap with arXiv:2111.0466

Fundamental Approaches to Software Engineering

This open access book constitutes the proceedings of the 25th International Conference on Fundamental Approaches to Software Engineering, FASE 2022, which was held during April 4-5, 2022, in Munich, Germany, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2022. The 17 regular papers presented in this volume were carefully reviewed and selected from 64 submissions. The proceedings also contain 3 contributions from the Test-Comp Competition. The papers deal with the foundations on which software engineering is built, including topics like software engineering as an engineering discipline, requirements engineering, software architectures, software quality, model-driven development, software processes, software evolution, AI-based software engineering, and the specification, design, and implementation of particular classes of systems, such as (self-)adaptive, collaborative, AI, embedded, distributed, mobile, pervasive, cyber-physical, or service-oriented applications