Comparing autoencoder-based approaches for anomaly detection in highway driving scenario images

Autoencoder-based anomaly detection approaches can be used for precluding scope compliance failures of the automotive perception. However, the applicability of these approaches for the automotive domain should be thoroughly investigated. We study the capability of two autoencoder-based approaches using reconstruction errors and bottleneck-values for detecting semantic anomalies in automotive images. As a use-case, we consider a specific highway driving scenario identifying if there are any vehicles in the field of view of a front-looking camera. We conduct a series of experiments with two simulated driving scenario datasets and measure anomaly detection performance for different cases. We systematically test different autoencoders and training parameters, as well as the influence of image colors. We show that the autoencoder-based approaches demonstrate promising results for detecting semantic anomalies in highway driving scenario images in some cases. However, we also observe the variability of anomaly detection performance between different experiments. The autoencoder-based approaches are capable of detecting semantic anomalies in highway driving scenario images to some extent. However, further research with other use-cases and real datasets is needed before they can be safely applied in the automotive domain

Measuring the Evolution of Meta-models, Models and Design Requirements to Facilitate Architectural Updates in Large Software Systems

Background: In order to reduce complexity of the system and its development cost, the architecture of large software systems is often developed following the MDE (Model-Driven Engineering) approach. Developing architectures according to MDE relies on three main artifacts in the development process: domain-specific meta-models, architectural models and system design requirements. The architecture of the system is defined in the architectural models which are developed using modeling tools. The syntax of the models is defined in domain-specific meta-models, while their semantics is usually provided in a form of system design requirements in the supporting specifications. Objective: The main objective of this thesis is to develop methods and tools for managing architectural updates in the development of large software systems. Our goal is to automatically assess the impact of using new architectural features on the development projects (e.g., in terms of model complexity and required updates of the modeling tools) in order to assist system designers in planning their use in the models. The assessment is based on measuring the evolution of domain-specific meta-models, architectural models and system design requirements related to relevant architectural features. Method: We performed a series of case studies focusing on the domain-specific meta-model, architectural models and system design requirements from the automotive domain. On the one hand, the case studies helped us to understand relevant industrial contexts for our research problems and develop our methods using constructive research methodology. On the other hand, the case studies helped us to empirically validate the results of our methods. Results: We developed three new methods and software tools for automated impact assessment. The first method and the tool (QTool) show the complexity increase in the architectural models after adding a set of new features to the system. The second method (MeFIA) and the tool (ARCA) assess the impact of using these features in the system on the used modeling tools. Finally, the third method and the tool (SREA) identify a subset of design requirements that are affected by the use of the new features. Conclusion: We showed in practice that our methods and tools enable faster use of new architectural features in the development projects. More concretely, we showed that quantitative analysis of evolution of domain-specific meta-models, architectural models and system design requirements related to new architectural features can be a valuable indicator of which features shall be used in the system and what is their impact on the development projects

Measuring the Evolution of Automotive Software Models and Meta-Models to Support Faster Adoption of New Architectural Features

Background: The ever-increasing amount of software in cars today combined with high market competition demands fast adoption of new software solutions in car development projects. One challenge in enabling such a fast adoption is to develop the architecture and models of the automotive software systems in a structured and controlled way.Objective: The main objective of this thesis was to enable the fast utilization of new architectural features in automotive software models. This was achieved by developing methods and tools to analyze the evolution of the domain-specific meta-models that are used to define the language of software models and their features. In particular, we wanted to identify the underlying changes caused by meta-model evolution related to a specific set of architectural features and assess their impact on both the architectural models and modeling tools used by different roles (e.g., the Original Equipment Manufacturers, OEMs, and their suppliers) in the development process.Method: We achieved our objective by conducting an action research project in close collaboration with the Volvo Car Group (Volvo Cars) and the consortium of the AUTOSAR standard, which aims to standardize the architecture of automotive software systems. This collaboration facilitates fast feedback from experts in the field on the problems, ideas and methods we developed in the course of this research, thereby enabling the validation of the research results and proposed methods in on-going development projects, i.e., their direct application in the industry.Results: We identified the most suitable software measures for measuring the evolution of both the automotive software models and meta-models. The calculation and presentation of the measurement results were done with the support of two, newly-developed tools. We also developed a method for the automated identification of an optimal set of new architectural features that should be adopted in development projects to facilitate the decision-making process concerning the selection of which of these new features would be adopted.Conclusion: We applied the developed methods and tools to the architectural models and meta-models used at Volvo Cars and concluded that they provide valuable input for the decision-making process concerning which new versions of the standardized meta-model should be used in different projects. We also concluded that these methods and tools can facilitate the assessment of the impact of adopting new architectural features on the different roles involved in the development process

Measuring the Evolution of Automotive Software Models and Meta-Models to Support Faster Adoption of New Architectural Features

Background: The ever-increasing amount of software in cars today combined with high market competition demands fast adoption of new software solutions in car development projects. One challenge in enabling such a fast adoption is to develop the architecture and models of the automotive software systems in a structured and controlled way.Objective: The main objective of this thesis was to enable the fast utilization of new architectural features in automotive software models. This was achieved by developing methods and tools to analyze the evolution of the domain-specific meta-models that are used to define the language of software models and their features. In particular, we wanted to identify the underlying changes caused by meta-model evolution related to a specific set of architectural features and assess their impact on both the architectural models and modeling tools used by different roles (e.g., the Original Equipment Manufacturers, OEMs, and their suppliers) in the development process.Method: We achieved our objective by conducting an action research project in close collaboration with the Volvo Car Group (Volvo Cars) and the consortium of the AUTOSAR standard, which aims to standardize the architecture of automotive software systems. This collaboration facilitates fast feedback from experts in the field on the problems, ideas and methods we developed in the course of this research, thereby enabling the validation of the research results and proposed methods in on-going development projects, i.e., their direct application in the industry.Results: We identified the most suitable software measures for measuring the evolution of both the automotive software models and meta-models. The calculation and presentation of the measurement results were done with the support of two, newly-developed tools. We also developed a method for the automated identification of an optimal set of new architectural features that should be adopted in development projects to facilitate the decision-making process concerning the selection of which of these new features would be adopted.Conclusion: We applied the developed methods and tools to the architectural models and meta-models used at Volvo Cars and concluded that they provide valuable input for the decision-making process concerning which new versions of the standardized meta-model should be used in different projects. We also concluded that these methods and tools can facilitate the assessment of the impact of adopting new architectural features on the different roles involved in the development process

AUTOSAR Standard

In this chapter, we describe the role of AUTOSAR (AUTomotive Open System ARchitecture) standard in the development of automotive system architectures. AUTOSAR defines the reference architecture and methodology for the development of automotive software systems, and provides the language (meta-model) for their architectural models. It also specifies the architectural modules and functionality of the middleware layer known as the basic software. We start by describing the layers of the AUTOSAR reference architecture. We then describe the proposed development methodology by identifying major roles in the automotive development process and the artifacts they produce with examples of each artifact. We follow up by explaining the role of AUTOSAR meta-model in the development process and show examples of the architectural models that instantiate this meta-model. We also explain the use of the AUTOSAR meta-model for configuring basic software modules. We conclude the chapter by showing trends in the evolution of the AUTOSAR standard and reflect on its future role in the automotive domain

Improving Measurement Certainty by Using Calibration to Find Systematic Measurement Error - A Case of Lines-of-Code Measure

Base measures such as the number of lines-of-code are oftenused to make predictions about such phenomena as project effort,product quality or maintenance effort. However, quite often we rely onthe measurement instruments where the exact algorithm for calculatingthe value of the measure is not known. The objective of our research isto explore how we can increase the certainty of base measures in softwareengineering. We conduct a benchmarking study where we use fourmeasurement instruments for lines-of-code measurement with unknowncertainty to measure five code bases. Our results show that we can adjustthe measurement values by as much as 20% knowing the systematicerror of the tool. We conclude that calibrating the measurement instrumentscan significantly contribute to increased accuracy in measurementprocesses in software engineering. This will impact the accuracy of predictions(e.g. of effort in software projects) and therefore increase thecost-effciency of software engineering processes

Measuring the Evolution of Meta-Models - A Case Study of Modelica and UML Meta-Models

The evolution of both general purpose and domain-specific meta-models and its impact on the existing models and modeling tools has been discussed extensively in the modeling research community. To assess the impact of domain-specific meta-model evolution on the modeling tools, a number of measures have been proposed by Durisic et al., NoC (Number of Changes) being the most prominent one. The proposed measures are evaluated on a case of AUTOSAR meta-model that specifies the language for designing automotive system architectures. In this paper, we assess the applicability of these measure and the underlying data-model for their calculation in a case study of Modelica and UML meta-models. Our preliminary results show that the proposed data-model and the measures can be applied to both analyzed meta-models as we were able to capture 68/77 changes on average per Modelica/UML release. However, only a subset of the data-model elements is applicable for analyzing the evolution of Modelica and also certain transformation of the data-model is required in case of UML. Despite these encouraging results, further studies are needed to assess the usefulness of the actual measures, e.g., NoC, in assessing the impact of Modelica/UML meta-model evolution on the modeling tools

Identifying Optimal Sets of Standardized Architectural Features - A Method and its Automotive Application

Industrial standards are used to formalize procedures, rulesand guidelines for the industry to follow. Following a stan-dard requires continuous adoption of the new standardizedfeatures where only their subset is required by individualcompanies. Therefore the prioritization of the features andthe assessment of their impact on the development projectsis crucial for the success of the project. In software engineer-ing, industrial standards are used increasingly often to stan-dardize a language for designing architectural componentsof the system by defining domain-specific meta-models. Thepurpose is to assure the interoperability between a numberof software tools exchanging the architectural models. Inthis paper, we present a method for identifying optimal setsof new standardized architectural features to be adopted inthe development projects. The optimization is done basedon the assessment of their benefit for the projects and theestimated cost of re-work in the modeling tools according tothe changes in the standardized meta-model. We evaluatethe method by applying it on 14 new architectural featuresof a new release of the AUTOSAR standard which is followedin the development of the automotive software systems

Identifying Optimal Sets of Standardized Architectural Features - A Method and its Automotive Application

Industrial standards are used to formalize procedures, rules and guidelines for the industry to follow. Following a standard requires continuous adoption of the new standardized features where only their subset is required by individual companies. Therefore the prioritization of the features and the assessment of their impact on the development projects is crucial for the success of the project. In software engineering, industrial standards are used increasingly often to standardize a language for designing architectural components of the system by defining domain-specific meta-models. The purpose is to assure the interoperability between a number of software tools exchanging the architectural models. In this paper, we present a method for identifying optimal sets of new standardized architectural features to be adopted in the development projects. The optimization is done based on the assessment of their benefit for the projects and the estimated cost of re-work in the modeling tools according to the changes in the standardized meta-model. We evaluate the method by applying it on 14 new architectural features of a new release of the AUTOSAR standard which is followed in the development of the automotive software systems