Mapping AADL models to a repository of multiple schedulability analysis techniques

To fill the gap between the modeling of real-time systems and the scheduling analysis, we propose a framework that supports seamlessly the two aspects: 1) modeling a system using a methodology, in our case study, the Architecture Analysis and Design Language (AADL), and 2) helping to easily check temporal requirements (schedulability analysis, worst-case response time, sensitivity analysis, etc.). We introduce an intermediate framework called MoSaRT, which supports a rich semantic concerning temporal analysis. We show with a case study how the input model is transformed into a MoSaRT model, and how our framework is able to generate the proper models as inputs to several classic temporal analysis tools

An evaluation framework for assessing the dependability of Dynamic Binding in Service-Oriented Computing

Service-Oriented Computing (SOC) provides a flexible framework in which applications may be built up from services, often distributed across a network. One of the promises of SOC is that of Dynamic Binding where abstract consumer requests are bound to concrete service instances at runtime, thereby offering a high level of flexibility and adaptability. Existing research has so far focused mostly on the design and implementation of dynamic binding operations and there is little research into a comprehensive evaluation of dynamic binding systems, especially in terms of system failure and dependability. In this paper, we present a novel, extensible evaluation framework that allows for the testing and assessment of a Dynamic Binding System (DBS). Based on a fault model specially built for DBS's, we are able to insert selectively the types of fault that would affect a DBS and observe its behavior. By treating the DBS as a black box and distributing the components of the evaluation framework we are not restricted to the implementing technologies of the DBS, nor do we need to be co-located in the same environment as the DBS under test. We present the results of a series of experiments, with a focus on the interactions between a real-life DBS and the services it employs. The results on the NECTISE Software Demonstrator (NSD) system show that our proposed method and testing framework is able to trigger abnormal behavior of the NSD due to interaction faults and generate important information for improving both dependability and performance of the system under test

Automated Machine Learning for Multi-Label Classification

Automated machine learning (AutoML) aims to select and configure machine learning algorithms and combine them into machine learning pipelines tailored to a dataset at hand. For supervised learning tasks, most notably binary and multinomial classification, aka single-label classification (SLC), such AutoML approaches have shown promising results. However, the task of multi-label classification (MLC), where data points are associated with a set of class labels instead of a single class label, has received much less attention so far. In the context of multi-label classification, the data-specific selection and configuration of multi-label classifiers are challenging even for experts in the field, as it is a high-dimensional optimization problem with multi-level hierarchical dependencies. While for SLC, the space of machine learning pipelines is already huge, the size of the MLC search space outnumbers the one of SLC by several orders. In the first part of this thesis, we devise a novel AutoML approach for single-label classification tasks optimizing pipelines of machine learning algorithms, consisting of two algorithms at most. This approach is then extended first to optimize pipelines of unlimited length and eventually configure the complex hierarchical structures of multi-label classification methods. Furthermore, we investigate how well AutoML approaches that form the state of the art for single-label classification tasks scale with the increased problem complexity of AutoML for multi-label classification. In the second part, we explore how methods for SLC and MLC could be configured more flexibly to achieve better generalization performance and how to increase the efficiency of execution-based AutoML systems

Simulating Operational Concepts for Autonomous Robotic Space Exploration Systems: A Framework for Early Design Validation

During mission design, the concept of operations (ConOps) describes how the system operates during various life cycle phases to meet stakeholder expectations. ConOps is sometimes declined in a simple evaluation of the power consumption or data generation per mode. Different operational timelines are typically developed based on expert knowledge. This approach is robust when designing an automated system or a system with a low level of autonomy. However, when studying highly autonomous systems, designers may be interested in understanding how the system would react in an operational scenario when provided with knowledge about its actions and operational environment. These considerations can help verify and validate the proposed ConOps architecture, highlight shortcomings in both physical and functional design, and help better formulate detailed requirements. Hence, this study aims to provide a framework for the simulation and validation of operational scenarios for autonomous robotic space exploration systems during the preliminary design phases. This study extends current efforts in autonomy technology for planetary systems by focusing on testing their operability and assessing their performances in different scenarios early in the design process. The framework uses Model-Based Systems Engineering (MBSE) as the knowledge base for the studied system and its operations. It then leverages a Markov Decision Process (MDP) to simulate a set of system operations in a relevant scenario. It then outputs a feasible plan with the associated variation of a set of considered resources as step functions. This method was applied to simulate the operations of a small rover exploring an unknown environment to observe and sample a set of targets

UML consistency rules: a systematic mapping study

Context: The Unified Modeling Language (UML), with its 14 different diagram types, is the de-facto standard tool for objectoriented modeling and documentation. Since the various UML diagrams describe different aspects of one, and only one, software under development, they are not independent but strongly depend on each other in many ways. In other words, the UML diagrams describing a software must be consistent. Inconsistencies between these diagrams may be a source of the considerable increase of faults in software systems. It is therefore paramount that these inconsistencies be detected, ana

Ernst Denert Award for Software Engineering 2019

This open access book provides an overview of the dissertations of the five nominees for the Ernst Denert Award for Software Engineering in 2019. The prize, kindly sponsored by the Gerlind & Ernst Denert Stiftung, is awarded for excellent work within the discipline of Software Engineering, which includes methods, tools and procedures for better and efficient development of high quality software. An essential requirement for the nominated work is its applicability and usability in industrial practice. The book contains five papers describing the works by Sebastian Baltes (U Trier) on Software Developers’Work Habits and Expertise, Timo Greifenberg’s thesis on Artefaktbasierte Analyse modellgetriebener Softwareentwicklungsprojekte, Marco Konersmann’s (U Duisburg-Essen) work on Explicitly Integrated Architecture, Marija Selakovic’s (TU Darmstadt) research about Actionable Program Analyses for Improving Software Performance, and Johannes Späth’s (Paderborn U) thesis on Synchronized Pushdown Systems for Pointer and Data-Flow Analysis – which actually won the award. The chapters describe key findings of the respective works, show their relevance and applicability to practice and industrial software engineering projects, and provide additional information and findings that have only been discovered afterwards, e.g. when applying the results in industry. This way, the book is not only interesting to other researchers, but also to industrial software professionals who would like to learn about the application of state-of-the-art methods in their daily work