Advancements in data-driven machine learning have emerged as a pivotal element in supporting automotive software systems (ASSs) engineering across various levels of the V-development process. During system verification and validation, the integration of an intelligent fault detection and diagnosis (FDD) model with test recordings analysis process serves as a powerful tool for efficiency, ensuring functional safety. However, the lack of interpretability of the black-box FDD models developed not only hinders understanding of the cause underlying the prediction but also prevents the model from being adapted based on the prediction result. This, in turn, increases the computational cost required for developing a complex FDD model and limits confidence in real-time safety-critical applications. To address this challenge, a novel explainable method for fault detection, identification, and localization is proposed in this article with the aim of providing a clear understanding of the logic behind the prediction outcome. To this end, a hybrid 1dCNN-GRU-based intelligent model was developed to analyze the recordings from the real-time validation process of ASSs. The employment of explainable AI techniques, i.e., IGs, DeepLIFT, Gradient SHAP, and DeepLIFT SHAP, was instrumental in enabling model adaptation and facilitating the root cause analysis (RCA). The proposed approach is applied to the real-time dataset collected during a virtual test drive performed by the user on hardware-in-the-loop (HIL) system. A thorough evaluation of the model’s performance revealed its superiority in diagnosing the fault type and location compared to state-of-the-art models. By integrating XAI techniques, the proposed approach offers a high-performance, low-computational-cost DL model with improved interpretability, which not only supports safety engineers but also contributes to the optimisation of the real-time validation process of ASSs.

In large-scale projects operated in regulated environments, standard development processes are employed to meet strict compliance demands. Since such processes are usually complex, providing process users with access to their required process, which should be tailored to a project’s needs is a challenging task that requires proper tool support. In this paper, we present a process ecosystem in which software processes are provided as web-based services. We outline the general idea, describe the modeling approach, and we illustrate the concept’s realization using a proof-of-concept case based on a large software process line that is mandatory to use for IT projects in the German public sector. The suitability is evaluated with three experts that valued the improved accessibly and usability of the process and the end-user support tool.

Glass beads were among the most common grave goods in the Early Middle Ages, with an estimated number in the millions. The color, size, shape and decoration of the beads are diverse leading to many different archaeological classification systems that depend on the subjective decisions of individual experts. The lack of an agreed upon expert categorization leads to a pressing problem in archaeology, as the categorization of archaeological artifacts, like glass beads, is important to learn about cultural trends, manufacturing processes or economic relationships (e.g., trade routes) of historical times. An automated, objective and reproducible classification system is therefore highly desirable. We present a high-quality data set of images of Early Medieval beads and propose a clustering pipeline to learn a classification system in a data-driven way. The pipeline consists of a novel extension of deep embedded non-redundant clustering to identify multiple, meaningful clusterings of glass bead images. During the cluster analysis we address several challenges associated with the data and as a result identify high-quality clusterings that overlap with archaeological domain expertise. To the best of our knowledge this is the first application of non-redundant image clustering for archaeological data.

The often-occurring short-term orders of manufactured products require a high machine availability. This requirement increases the importance of predictive maintenance solutions for bearings used in machines. There are, among others, hybrid solutions that rely on a physical model. For their usage, knowing the different degradation stages of bearings is essential. This research analyzes the underlying failure mechanisms of these stages theoretically and in a practical example of the well-known FEMTO dataset used for the IEEE PHM 2012 Data Challenge to provide this knowledge. In addition, it shows for which use cases the usage of low-frequency accelerometers is sufficient. The analysis provides that the degradation stages toward the end of the bearing life can also be detected with low-frequency accelerometers. Further, the importance of high-frequency accelerometers to detect bearing faults in early degradation stages is pointed out. These aspects have not been paid attention to by industry and research until now, despite providing a considerable cost-saving potential.

In this work, we propose a computational robot trust model based on the predictability of the human partner as the main factor that impacts robot trust. Using this model and based on the robot’s knowledge about the task, the robot switches its behavior between conservative and normal, and adjusts the implemented safety mechanism as a function of the current robot trust level. We illustrate the proposed model impact on the outcome of the collaboration using a simple scenario. The results show a significant improvement in safety conditions of the human in collaboration with a robot at the cost of justifiable team performance reduction.

Abstract Software is an integrated part of new features within the automotive sector, car manufacturers, the Hersteller Initiative Software (HIS) consortium defined metrics to determine software quality. Yet, problems with assigning metrics to quality attributes often occur in practice. The specified boundary values lead to discussions between contractors and clients as different standards and metric sets are used. This paper studies metrics used in the automotive sector and the quality attributes they address. The HIS, ISO/IEC 25010:2011, and ISO/IEC 26262:2018 are utilized to draw a big picture illustrating (i) which metrics and boundary values are reported in literature, (ii) how the metrics match the standards, (iii) which quality attributes are addressed, and (iv) how the metrics are supported by tools. Our findings from analyzing 38 papers include a catalog of 112 metrics of which 17 define boundary values and 48 are supported by tools. Most of the metrics are concerned with source code, are generic, and not specifically designed for automotive software development. We conclude that many metrics exist, but a clear definition of the metrics' context, notably regarding the construction of flexible and efficient measurement suites, is missing.

Most people have trouble assessing artificial intelligence. They cannot decide without a sound education what are the applicabilities and limitations of this technology and how to use it. In this publication we present a workshop program, which allows even novices to understand and implement the most fundamental concepts of supervised learning within a short time. The participants of the workshop are motivated by a combination of lecture, configuration work on a computer, and rapid testing on real model vehicles. The workshop presented here has already been held four times and was rated as very positive and instructive by all 48 participants.

Artificial intelligence is a promising element in the development of autonomous vehicles. We have designed an end-to-end learning-based autonomous driving system solely with a neural network through supervised learning, which has been deployed on a model vehicle equipped with a lidar. Input of the convolutional neural network are the point clouds from the lidar and outputs are the requested drive torques and steering angles. For the training of the neural network, the required sensor and actuator data were recorded by remotely controlling the model vehicle. With supervised learning, the end-to-end neural network learns the safety-relevant rules only implicitly through examples in the training data. Because it is not guaranteed that the neural network has learned all necessary rules and can apply them correctly in all situations, safe operation cannot be assured. To address this safety issue, we developed a software architecture including a runtime monitoring component. If the runtime monitoring component detects a violation of any predefined safety rule, it will select an appropriate strategy in order to transfer the vehicle into a safe state.

Hybrid approaches for software and system development have become reality. Recent research shows the use of hybrid development approaches mainly grounded in experience and driven by pragmatism. At the same time, a vast number of success factors is known that influences process development and process use alike. However, even though industrial practice shows a need for hybrid development approaches and knowledge regarding the success factors is in place, a systematic approach to develop, deploy and tailor hybrid development approaches is missing. This paper reports on ongoing research that aims at developing a method to support the evidence-driven construction of hybrid development approaches. We provide an overview of the required method components and outline how hybrid development approaches can be deployed at the organizational level and tailored at the project level. We further give an overview of ongoing and completed studies supporting the method's construction and evaluation.

The engineering of cyber physical systems requires holistic simulation perspectives. To cope with the complexity of these systems, we aim to provide a simulation methodology that is efficient regarding model complexity. The required holistic perspective is reached on a coarse level, which is co-simulated with multiple detailed models of some areas of the system that are of particular interest to the investigated phenomena. Which areas are thus “zoomed in” is dynamic during a simulation run. To reflect this, the resulting Multi-Level-Simulation is deployed in a dynamic cloud environment, using the provided hardware resources in a cost-efficient manner.

Reference architecture can help in enterprise architecture management to develop and operate standardized and maintainable software landscapes. Similar to the software architectures of single systems, however, they are threatened by architecture erosion, i.e. the continuous divergence between intended architectures and their actual realizations. Architecture erosion has negative effects on the maintainability of software systems and on other quality attributes. In this paper, we report on the application of a rule-based architecture conformance checking approach in an industrial case study in which we investigate an industrial reference architecture for the German public administration. The reference architecture and its constraints for implementations are formalized as architecture rules enabling automatic conformance checking tool support. The results from the case study show that the approach is capable of checking reference architecture conformance in realistic settings and helps to avoid software architecture erosion.

Detecting software architecture erosion is an important task during the development and maintenance of software systems. Even in model-driven approaches in which consistency between artifacts can partially be established by construction and consistency issues have been intensively investigated, the intended architecture and its realization may diverge with negative effects on software quality. In this article, we describe an approach to flexible architecture erosion detection for model-driven development approaches. Consistency constraints expressed by architectural aspects called architectural rules are specified as formulas on a common ontology, and models are mapped to instances of that ontology. A knowledge representation and reasoning system is then utilized to check whether these architectural rules are satisfied for a given set of models. We describe three case studies in which this approach has been used to detect architecture erosion flexibly and argue that the negative effects of architecture erosion can be minimized effectively.

Software-as-a-Service (SaaS) is a delivery model whose basic idea is to provide applications to the customer on demand over the Internet. In contrast to similar but older approaches, SaaS promotes multi-tenancy as a tool to exploit economies of scale. This means that a single application instance serves multiple customers. On the one hand, however, a major throwback of SaaS is the customers' hesitation of sharing infrastructure, application code, or data with other tenants. On the other hand, tenants might specifically demand to share the same application code or data in order to be able to collaborate, e.g. joint product development or joint sales activities for their complementing products. In this paper an approach is presented which strives for enabling the customers to choose if or even with whom they want to share the application. The approach enables the customer to make that choice not just for the entire application but specifically for individual application components (AC). Thus, it is possible that a customer chooses to have their application deployed publicly (sharing the application with all other customers choosing this model) except for one or more ACs which handle very sensitive data. For these ACs, the customer may demand not to share at all, not to share with competitors, or even specifically name with which other customers they wish to share infrastructure.

In practice, more and more software development projects are distributed, ranging from partly distributed teams to global projects with each stakeholder located differently. Teaching actual practice in software engineering at university needs a proper mixture of theory and practice. But setting up practical exercises for global software engineering is hard, because students have to cooperate across different locations and situations reflecting the teaching intentions have to be provoked explicitly. This paper presents the concepts behind our common teaching environment for global software engineering - the GloSELab. It describes the experiences on setting up a distributed course and reports our teaching intentions based on each universities main focus: project management, requirements engineering & quality assurance, architecture, and implementation. Furthermore, we discuss our setup - a stage-gate process, where each location takes care of a different phase - and report occurred problems and how they supported or interfered with our teaching intentions.

In the agile community, there is a strong commitment to the few and simple principles and values that are collected in the agile manifesto. Those are a common guideline and a value system for agile methods. Rich process models suffer in having such principles and values. In this special panel, we aim at initiating a discussion on commonly agreed principles and values in rich process models, and at starting the work on a "Manifesto for Rich Process Models".

The V-Modell XT is the standard software development process for IT-projects in the German government. For federal agencies, this process is mandatory to manage internal IT-projects, as well as to coordinate projects of third-party suppliers. The non-profit organization "WEIT e.V." maintains the further development of the process model and provides official and industrial process owners with services. The process model is designed as a process line. Therefore the further development needs to be handled with care. In this position paper, we present the values that were chosen mandatory for the process's further development. We also motivate values that we consider relevant for rich process models in general.

Modern enterprise application systems are parts of complex IT landscapes. The architecture of such a landscape may impose constraints upon the design of single applications, for example by the mandatory use of enterprise-wide reference architectures. It is of great importance for the sake of smooth operation and easy maintaining that single applications are com-pliant to the reference architectures. Checking this compliance is highly important for the architecture management to assure the quality of application systems. Unfortunately, current tool support is not flexible enough to easily check different aspects of architectural compliance.This paper proposes a rule-based approach based upon logic programming concepts towards a formalism for architectural compliance checking. In this approach, the architecture and design are represented as logical knowledge base that can be queried for architectural compliance. Furthermore, the paper presents a case study, in which the approach was prototypically implemented and applied in an industrial context.

Global software engineering has become a fact in many companies due to real necessity in practice. In contrast to co-located projects global projects face a number of additional software engineering challenges. Among them quality management has become much more difficult and schedule and budget overruns can be observed more often. Compared to co-located projects global software engineering is even more challenging due to the need for integration of different cultures, different languages, and different time zones-across companies, and across countries. The diversity of development locations on several levels seriously endangers an effective and goal-oriented progress of projects. In this position paper we discuss reasons for global development, sketch settings for distribution and views of orchestration of dislocated companies in a global project that can be seen as a ldquovirtual project environmentrdquo. We also present a collection of questions, which we consider relevant for global software engineering. The questions motivate further discussion to derive a research agenda in global software engineering.

Globalization is one of the big trends in software development. Development projects need a variety of different resources with appropriate expert knowledge to be successful. More and more of these resources are nowadays obtained from specialized organizations and countries all over the world, varying in development approaches, processes, and culture. As seen with early outsourcing attempts, collaboration may fail due to these differences. Hence, the major challenge in global software engineering is to streamline collaborating organizations towards a successful conjoint development. Based on typical collaboration scenarios, this paper presents a structured approach to integrate processes in a comprehensible way.

In practice, pure top-down and refinement-based development processes are not sufficient. Usually, an iterative and incremental approach is applied instead. Existing methodologies, however, do not support such evolutionary development processes very well. In this paper, we present the basic concepts of an overall methodology based on component ware and software evolution. The foundation of our methodology is a novel, well-founded model for component-based systems. This model is sufficiently powerful to handle the fundamental structural and behavioral aspects of component ware and object-orientation. Based on the model, we are able to provide a clear definition of a software evolution step.