A Framework for Model-based Testing of Integrated Modular Avionics

In modern aircraft, electronics and control systems are designed based on the Integrated Modular Avionics (IMA) system architecture. While this has numerous advantages (reduction of weight, reduced power and fuel consumption, reduction of development cost and certification effort), the IMA platform also adds an additional layer of complexity. Due to the safety-critical nature of many avionics functions careful and accurate verification and testing are imperative. This thesis describes results achieved from research on model-based testing of IMA systems, in part obtained during the European research project SCARLETT. It presents a complete framework which enables IMA domain experts to design and run model-based tests on bare module, configured module, and application level in a standardised test environment. The first part of this thesis provides background information on the relevant topics: the IMA concept, domain-specific languages, model-based testing, and the TTCN-3 standard. The second part introduces the IMA Test Modelling Language (ITML) framework and its components. It describes a tailored TTCN-3 test environment with appropriate adapters and codecs. Based on MetaEdit and its meta-metamodel GOPPRR, it defines the three variants of the domain-specific language ITML, each with its abstract and concrete syntax as well as static and dynamic semantics. The process of test procedure generation from ITML models is explained in detail. Furthermore, the design and implementation of a universal Test Agent is shown. A dedicated communication protocol for controlling the agent is defined as well. The third part provides an evaluation of the framework. It shows usage scenarios in the SCARLETT project, gives a comparison to related tools and approaches, and explains the advantages of using the ITML framework for an IMA domain expert. The final part presents several example ITML models. It also provides reference material like XML schemata, framework source code, and model validators

Acquire Driving Scenarios Efficiently: A Framework for Prospective Assessment of Cost-Optimal Scenario Acquisition

Scenario-based testing is becoming increasingly important in safety assurance for automated driving. However, comprehensive and sufficiently complete coverage of the scenario space requires significant effort and resources if using only real-world data. To address this issue, driving scenario generation methods are developed and used more frequently, but the benefit of substituting generated data for real-world data has not yet been quantified. Additionally, the coverage of a set of concrete scenarios within a given logical scenario space has not been predicted yet. This paper proposes a methodology to quantify the cost-optimal usage of scenario generation approaches to reach a certainly complete scenario space coverage under given quality constraints and parametrization. Therefore, individual process steps for scenario generation and usage are investigated and evaluated using a meta model for the abstraction of knowledge-based and data-driven methods. Furthermore, a methodology is proposed to fit the meta model including the prediction of reachable complete coverage, quality criteria, and costs. Finally, the paper exemplary examines the suitability of a hybrid generation model under technical, economical, and quality constraints in comparison to different real-world scenario mining methods.Comment: Accepted to be published as part of the 26th IEEE International Conference on Intelligent Transportation Systems (ITSC) 2023, Bilbao, Spain, September 24-28, 202

Scenario-Based Development and Verification of Domain-Specific Languages

The use of domain-specific languages (DSLs) has increased manifold for problem solving in specific domain areas as they allow for a wider variety of expressions within their domain. Modeling using DSLs has shown high increases in productivity after accounting for the time and cost expended in developing them, making them a suitable target for improvement in order to reap higher rewards. The currently used approach for domain modeling involves the creation of an ontology which is then used to describe the domain model. This ontology encapsulates all domain knowledge and can be cumbersome to create, requiring external sources of information and assistance from a domain expert. This dissertation first discusses the use and importance of DSLs for scenario generation for a domain and presents an extension to the Aviation Scenario Definition Language (ASDL). The main contribution of this dissertation is a novel framework for scenario based development of DSLs, called the Domain-Specific Scenario (DoSS) framework. This framework proposes the use of scenarios in natural language, which are currently used in requirements engineering and testing, as the basis for developing the domain model iteratively. An example of the use of this approach is provided by developing a domain model for ASDL and comparing the published model with one obtained using DoSS. This approach is supplemented with a case study to validate the claim that DoSS is easier to use by non-experts in the domain by having a user create a model and comparing it to one obtained by the author. These models were found to be almost identical, showing a promising return for this approach. The time taken and effort required to create this model by the user were recorded and found to be quite low, although no similar results have been published so no comparison could be made. State charts are then used for verification of scenarios to ensure the conformity between scenarios and models. The dissertation also discusses applications of the ideas presented here, specifically, the use of ASDL for Air Traffic Control training scenarios and the use of DoSS for ontology generation

The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems

Scenario-based testing for the safety validation of highly automated vehicles is a promising approach that is being examined in research and industry. This approach heavily relies on data from real-world scenarios to derive the necessary scenario information for testing. Measurement data should be collected at a reasonable effort, contain naturalistic behavior of road users and include all data relevant for a description of the identified scenarios in sufficient quality. However, the current measurement methods fail to meet at least one of the requirements. Thus, we propose a novel method to measure data from an aerial perspective for scenario-based validation fulfilling the mentioned requirements. Furthermore, we provide a large-scale naturalistic vehicle trajectory dataset from German highways called highD. We evaluate the data in terms of quantity, variety and contained scenarios. Our dataset consists of 16.5 hours of measurements from six locations with 110 000 vehicles, a total driven distance of 45 000 km and 5600 recorded complete lane changes. The highD dataset is available online at: http://www.highD-dataset.comComment: IEEE International Conference on Intelligent Transportation Systems (ITSC) 201

Evaluating the Impact of Critical Factors in Agile Continuous Delivery Process: A System Dynamics Approach

Continuous Delivery is aimed at the frequent delivery of good quality software in a speedy, reliable and efficient fashion – with strong emphasis on automation and team collaboration. However, even with this new paradigm, repeatability of project outcome is still not guaranteed: project performance varies due to the various interacting and inter-related factors in the Continuous Delivery 'system'. This paper presents results from the investigation of various factors, in particular agile practices, on the quality of the developed software in the Continuous Delivery process. Results show that customer involvement and the cognitive ability of the QA have the most significant individual effects on the quality of software in continuous delivery

An evaluation framework for stereo-based driver assistance

This is the post-print version of the Article - Copyright @ 2012 Springer VerlagThe accuracy of stereo algorithms or optical flow methods is commonly assessed by comparing the results against the Middlebury database. However, equivalent data for automotive or robotics applications rarely exist as they are difficult to obtain. As our main contribution, we introduce an evaluation framework tailored for stereo-based driver assistance able to deliver excellent performance measures while circumventing manual label effort. Within this framework one can combine several ways of ground-truthing, different comparison metrics, and use large image databases. Using our framework we show examples on several types of ground truthing techniques: implicit ground truthing (e.g. sequence recorded without a crash occurred), robotic vehicles with high precision sensors, and to a small extent, manual labeling. To show the effectiveness of our evaluation framework we compare three different stereo algorithms on pixel and object level. In more detail we evaluate an intermediate representation called the Stixel World. Besides evaluating the accuracy of the Stixels, we investigate the completeness (equivalent to the detection rate) of the StixelWorld vs. the number of phantom Stixels. Among many findings, using this framework enables us to reduce the number of phantom Stixels by a factor of three compared to the base parametrization. This base parametrization has already been optimized by test driving vehicles for distances exceeding 10000 km