Quality Improvement in Automotive Software Engineering Using a Model-Based Approach

Premium quality and innovation are the cornerstones of the leading positions of car manufacturers and suppliers in the world market. The permanently increasing complexity of in-car electronics and the rapidly growing amount of automotive software running on embedded electronic control units, places higher demands on quality assurance for the future. Quality cannot be implemented into software on embedded control units after their development. Methods for defects detection have to be constituted to automatically stop development to fix a problem before the defect continues downstream. In addition preventive actions have to be taken in respect of front-loading quality and reliability. An automatic and tool independent check of custom development rules, quality standards and enterprise wide guidelines can support the quality assurance process in the development of automotive control software. In the domain of automotive software engineering there is a lack of automated checking for standard conformance. Especially, a formal and tool independent notation of rules to follow is missing. In this chapter, the model-based design of automotive vehicle functions is taken as an example to show how textual rules describing development standards to be met can be transformed into a formal notation using the open standards Meta Object Facility and Object Constraint Language. Thereafter these rules can be checked automatically. The feasibility of this approach is shown by a software demonstrator.

From Requirements to Java Code

When engineering complex and distributed software and hardware systems (increasingly used in many sectors, such as manufacturing, aerospace, transportation, communication, energy, and health-care), quality has become a big issue, since failures can have economic consequences and can also endanger human life. Model-based specifications of component-based systems permit to explicitly model the structure and behaviour of components and their integration. In particular Software Architectures (SA) have been advocated as an effective means to produce quality systems. In this chapter by combining different technologies and tools for analysis and development, we propose an architecture-centric model-driven approach to validate required properties and to generate the system code. Functional requirements are elicited and used for identifying expected properties the architecture shall express. The architectural compliance to the properties is formally demonstrated, and the produced architectural model is used to automatically generate Java code. Suitable transformations assure that the code is conforming to both structural and behavioural SA constraints. This chapter describes the process and discusses how some existing tools and languages can be exploited to support the approach.

Integrating Measures and Redesigns in the Definition of Domain Specific Visual Languages

The goal of this work is to facilitate the task of integrating measurement and redesign tools in modelling environments for Domain Specific Visual Languages (DSVLs), reducing or eliminating the necessity of coding. With this purpose, we have created a DSVL called SLAMMER that includes generalizations of some of the more used types of product metrics and frequent model manipulations, which can be easily customised for any other DSVL in a graphical way. The metric customisation process relies on visual patterns for the specification of the elements that should be measured in each metric type, while redesigns (as well as other actions) can be specified either personalizing generic templates or by means of graph transformation systems. The provided DSVL also allows creating new metrics, composing metrics, and executing actions guided by measurement values. The approach has been empirically validated by its implementation in a meta-modelling tool, which has been used for several DSVLs. In this way, together with the DSVL specification, a SLAMMER model can be provided containing a suite of metrics and actions that will become available in the final modelling environment. In this chapter we show a case study for a notation in the web engineering domain. As ensuring model quality is a key success factor in many computer science areas, even crucial in model-driven development, we believe that the results of this work benefit all of them by providing automatic support for the specification, generation and integration of measurement and redesign tools with modelling environments.

Measuring Models

Model-Driven Engineering (MDE) is an approach to software development that uses models as primary artifacts, from which code, documentation and tests are derived. One way of assessing quality assurance in a given domain is to define domain metrics. We show that some of these metrics are supported by models. As text documents, models can be considered from a syntactic point of view i.e., thought of as graphs. We can readily apply graph-based metrics to them, such as the number of nodes, the number of edges or the fan-in/fan-out distributions. However, these metrics cannot leverage the semantic structuring enforced by each specific metamodel to give domain specific information. Contrary to graph-based metrics, more specific metrics do exist for given domains (such as LOC for programs), but they lack genericity. Our contribution is to propose one metric, called s, that is generic over metamodels and allows the easy specification of an open-ended wide range of model metrics.

Quality-Aware Model-Driven Service Engineering

Service engineering and service-oriented architecture as an integration and platform technology is a recent approach to software systems integration. Quality aspects ranging from interoperability to maintainability to performance are of central importance for the integration of heterogeneous, distributed service-based systems. Architecture models can substantially influence quality attributes of the implemented software systems. Besides the benefits of explicit architectures on maintainability and reuse, architectural constraints such as styles, reference architectures and architectural patterns can influence observable software properties such as performance. Empirical performance evaluation is a process of measuring and evaluating the performance of implemented software. We present an approach for addressing the quality of services and service-based systems at the model-level in the context of modeldriven service engineering. The focus on architecture-level models is a consequence of the black-box character of services.

A Framework for Understanding and Addressing the Semiotic Quality of Use Case Models

As software systems become ever more interactive, there is a need to model the services they provide to users, and use cases are one abstract way of doing that. As use cases models become pervasive, the question of their communicability to stakeholders arises. In this chapter, we propose a semiotic framework for understanding and systematically addressing the quality of use case models. The quality concerns at each semiotic level are discussed and process- and product-oriented means to address them in a feasible manner are presented. The scope and limitations of the framework, including that of the means, are given. The need for more emphasis on prevention over cure in improving the quality of use case models is emphasized. The ideas explored are illustrated by examples.

Quality-Driven Model Transformations

Model-Driven Architecture (MDA) is a software engineering approach that promotes the use of models and model transformations as primary development artifacts. Usually; there are several ways to transform a source model into a target model. Alternative target models may have the same functionality but may differ in their quality attributes (e.g.; understandability; modifiability). This chapter presents an approach to deal with quality-driven model transformations. Specifically; it focuses on a specific set of transformations to obtain UML class diagrams from a Requirements Model. A set of alternative transformations are identified; and the selection of the best alternative is done through a controlled experiment. The goal of the experiment is to empirically validate which alternative transformation produces the UML class diagram that is the easiest to understand. This evidence can be further used to define high-quality transformation processes; as it will be based on empirical knowledge rather than on common wisdom and the intuition of the researchers and developers.

Transitioning from Code-Centric to Model-Driven Industrial Projects

Introducing Model Driven Software Development (MDSD) into industrial projects is rarely done as a “green field” development. The usual path is to make a transition from code-centric (CC) development in existing projects into MDSD in a step-wise manner. Similarly to all other software development activities; software quality assurance needs to be adjusted to meet the new challenges arising when using models instead of the code for the mainstream development. In this chapter we present a set of empirical data on the issues related to transitioning from CC to MDSD projects in industry. First; we present results from a set of experiments evaluating how a domain specific notation affects the effectiveness and efficiency of reading techniques used for inspecting models. Second; we present a comparison of productivity increase when changing to MDSD projects from one of the large Swedish companies. Finally we present a short survey on the prioritization of products; projects; and resource metrics in MDSD projects.

Model-Driven Integration in Complex Information Systems

This chapter introduces model-driven integration in complex information systems by giving two practical examples. It relies on the experiences the authors have made in two different research projects at the public utilities domain. The chapter starts with a short introduction of the general problem domain and it gives detailed background information about the current state of the art in model-driven integration. Afterwards, the two research projects are introduced. The purpose of the first project (MINT) was to provide an integration approach allowing interoperability among several different legacy systems. Hence, the project itself was only acting as a “bridge” between the systems. The second project (DER) was built from scratch and got the challenge of integrating several existing third party systems into the newly designed system. In this project, the main system is a core element and only needed to integrate existing legacy systems for specific tasks.

Assuring Maintainability in Model-Driven Development of Embedded Systems

In model-driven software development as much as in classical code-driven development maintenance costs make up the bulk of the total life cycle costs of a software system. However, as development methods in MDSD differ from classical methods, assuring the maintainability of systems built with MDSD requires companies to adjust their quality assurance to work with the new paradigm and the novel type of development artefacts. As the automotive industry has already applied model-driven approaches for some time (usually in the form of Matlab/Simulink) it proves to be a fertile ground to advance assurance methods for the maintainability of model-based systems. In this chapter we describe a two-dimensional quality metamodel and present an instance that defines maintainability for MDSD with Matlab/Simulink and TargetLink. We exemplify how such a model serves as the basis of all quality assurance activities and report on experiences made in an industrial case study with one of the leading international providers of commercial vehicles and transport solutions.