Use, potential, and showstoppers of models in automotive requirements engineering

Several studies report that the use of model-centric methods in the automotive domain is widespread and offers several benefits. However, existing work indicates that few modelling frameworks explicitly include requirements engineering (RE), and that natural language descriptions are still the status quo in RE. Therefore, we aim to increase the understanding of current and potential future use of models in RE, with respect to the automotive domain. In this paper, we report our findings from a multiple-case study with two automotive companies, collecting interview data from 14 practitioners. Our results show that models are used for a variety of different purposes during RE in the automotive domain, e.g. to improve communication and to handle complexity. However, these models are often used in an unsystematic fashion and restricted to few experts. A more widespread use of models is prevented by various challenges, most of which align with existing work on model use in a general sense. Furthermore, our results indicate that there are many potential benefits associated with future use of models during RE. Interestingly, existing research does not align well with several of the proposed use cases, e.g. restricting the use of models to informal notations for communication purposes. Based on our findings, we recommend a stronger focus on informal modelling and on using models for multi-disciplinary environments. Additionally, we see the need for future work in the area of model use, i.e. information extraction from models by non-expert modellers

An Empirical Investigation of Using Models During Requirements Engineering in the Automotive Industry

Context:The automotive industry is undergoing a major transformation from a manufacturing industry towards an industry that relies heavily on software. As one of the main factors for project success, requirements engineering (RE) plays a major role in this transition. Similar to other areas of automotive engineering, the use of models during RE has been suggested to increase productivity and tackle increasing complexity by means of abstraction. Existing modelling frameworks often prescribe a variety of different, formal models for RE, trying to maximise the benefit obtained from model-based engineering (MBE). However, these frameworks are typically based on assumptions from anecdotal evidence and experience, without empirical data supporting these assumptions.Objective:The overall aim of our research is to investigate the potential benefits and drawbacks of using model-based RE in an automotive environment based on empirical evidence. To do so, we present an investigation of the current industrial practice of MBE in the automotive industry, existing challenges in automotive RE, and potential use cases for model-based RE. Furthermore, we explore two use cases for model-based RE, namely the creation of behavioural requirements models for validation and verification purposes and the use of existing trace models to support communication.Method:We address the aims of this thesis using three empirical strategies: case study, design science and survey. We collected quantitative and qualitative data using interviews as well as questionnaires.Results:Our results show that using models during automotive RE can be beneficial, if restricted to certain aspects of RE. In particular, models supporting communication and stakeholder interaction are promising. We show that the use of abstract models of behavioural requirements are considered beneficial for system testing purposes, even though they abstract from the detailed functional requirements. Furthermore, we demonstrate that existing data can be understood as a model to uncover dependencies between stakeholders. Conclusions:Our results question the feasibility to construct and maintain large amounts of formal models for RE. Instead, models during RE should be used for a few, important use cases. Additionally, MBE can be used as a means to understand existing problems in software engineering

Automatic Derivation of Dependency Chains within Systems for Automated Driving via Ontology Based Scenario Representations

In the development of automated vehicles, the complexity of the underlying, safety-critical systems poses a challenge for today’s engineers. Furthermore, mandatory standards such as ISO 26262 require traceability and assignability of elaborated requirements. In addition, due to the increasing use of agile product development processes, it is necessary to be able to deal with continuous system changes. In this work an approach is presented, which on the one hand aims at supporting design decisions of developers by representing dependencies in a system. On the other hand, an improved documentation and traceability of system requirements is intended. The proposed approach is based on the automatic derivation of so-called dependency chains. For this, ontology based representations of driving scenarios and a system’s architecture modeled by directed acyclic graphs are utilized to enable associations between scenarios and particular system components. After an introduction to the corresponding state of the art, the fundamentals of ontology engineering and the representation of system architectures are outlined. In addition a consistent terminology for driving scenarios is adopted and the situation awareness and information acquisition within driving tasks is described. Subsequently, the conceptual basis of the approach is presented. Based on the established terms a meta-model is developed, from which three key challenges concerning the intended solution are derived. These challenges are then addressed by the design of partial solutions. Not only the development methodology of the respective ontology is discussed. Also, a dedicated modeling possibility for a system’s architecture, the task-chain-pattern skill graph representation, is elaborated in this context. Moreover, the construct of a chain derivation engine, which constitutes the core concept of this work, is explained. Semantic rules contained in this engine, together with arithmetic functions attached to it, enable the eventual derivation of the intended dependency chains. In order to provide a proof of concept, the developed solution proposals are implemented first separately then collectively. Therein, driving scenarios are narrowed to two elementary scenarios in order to limit the scope of the implemented ontology. Regarding the exemplary system architecture representation, one particular skill is elaborated. Therefore, dedicated examples are provided for the different components of the implementation. In this way, the process is examined in more detail. The examples are then assembled and observed collectively. This illustrates the holistic chain derivation process and achieved analysis functionalities. The implemented software framework is charged with different quantities of variously complex input information and the resulting runtimes of the chain derivation process are interpreted. Hence, its potential and limits for a utilization in the development of fully automated vehicles is evaluated