Model Based System Assurance Using the Structured Assurance Case Metamodel

Assurance cases are used to demonstrate confidence in system properties of interest (e.g. safety and/or security). A number of system assurance approaches are adopted by industries in the safety-critical domain. However, the task of constructing assurance cases remains a manual, lenghty and informal process. The Structured Assurance Case Metamodel (SACM)is a standard specified by the Object Management Group (OMG). SACM provides a richer set of features than existing system assurance languages/approaches. SACM provides a foundation for model-based system assurance, which bears great application potentials in growing technology domains such as Open Adaptive Systems. However, the intended usage of SACM has not been sufficiently explained. In addition, there has not been support to interoperate between existing assurance case (models)and SACM models. In this article, we explain the intended usage of SACM based on our involvement in the OMG specification process of SACM. In addition, to promote a model-based approach, we provide SACM compliant metamodels for existing system assurance approaches (the Goal Structuring Notation and Claims-Arguments-Evidence), and the transformations from these models to SACM. We also briefly discuss the tool support for model-based system assurance which helps practitioners make the transition from existing system assurance approaches to model-based system assurance using SACM

Utilizing Active Machine Learning for Quality Assurance: A Case Study of Virtual Car Renderings in the Automotive Industry

Computer-generated imagery of car models has become an indispensable part of car manufacturers' advertisement concepts. They are for instance used in car configurators to offer customers the possibility to configure their car online according to their personal preferences. However, human-led quality assurance faces the challenge to keep up with high-volume visual inspections due to the car models’ increasing complexity. Even though the application of machine learning to many visual inspection tasks has demonstrated great success, its need for large labeled data sets remains a central barrier to using such systems in practice. In this paper, we propose an active machine learning-based quality assurance system that requires significantly fewer labeled instances to identify defective virtual car renderings without compromising performance. By employing our system at a German automotive manufacturer, start-up difficulties can be overcome, the inspection process efficiency can be increased, and thus economic advantages can be realized

MFRL-BI: Design of a Model-free Reinforcement Learning Process Control Scheme by Using Bayesian Inference

Design of process control scheme is critical for quality assurance to reduce variations in manufacturing systems. Taking semiconductor manufacturing as an example, extensive literature focuses on control optimization based on certain process models (usually linear models), which are obtained by experiments before a manufacturing process starts. However, in real applications, pre-defined models may not be accurate, especially for a complex manufacturing system. To tackle model inaccuracy, we propose a model-free reinforcement learning (MFRL) approach to conduct experiments and optimize control simultaneously according to real-time data. Specifically, we design a novel MFRL control scheme by updating the distribution of disturbances using Bayesian inference to reduce their large variations during manufacturing processes. As a result, the proposed MFRL controller is demonstrated to perform well in a nonlinear chemical mechanical planarization (CMP) process when the process model is unknown. Theoretical properties are also guaranteed when disturbances are additive. The numerical studies also demonstrate the effectiveness and efficiency of our methodology.Comment: 31 pages, 7 figures, and 3 table

Leveraging Traceability to Integrate Safety Analysis Artifacts into the Software Development Process

Safety-critical system's failure or malfunction can cause loss of human lives or damage to the physical environment; therefore, continuous safety assessment is crucial for such systems. In many domains this includes the use of Safety assurance cases (SACs) as a structured argument that the system is safe for use. SACs can be challenging to maintain during system evolution due to the disconnect between the safety analysis and system development process. Further, safety analysts often lack domain knowledge and tool support to evaluate the SAC. We propose a solution that leverages software traceability to connect relevant system artifacts to safety analysis models, and then uses these connections to visualize the change. We elicit design rationales for system changes to help safety stakeholders analyze the impact of system changes on safety. We present new traceability techniques for closer integration of the safety analysis and system development process, and illustrate the viability of our approach using examples from a cyber-physical system that deploys Unmanned Aerial Vehicles for emergency response

Supporting the Quality Assurance of a Scientific Framework

The quality assurance of scientific software has to deal with special challenges of this type of software, including missing test oracles, the need for high performance computing, and the high priority of non-functional requirements. A scientific framework consists of common code, which provides solutions for several similar mathematical problems. The various possible uses of a scientific framework lead to a large variability in the framework. In addition to the challenges of scientific software, the quality assurance of a scientific framework needs to find a way of dealing with the large variability. In software product line engineering (SPLE), the idea is to develop a software platform and then use mass customization for the creation of a group of similar applications. In this thesis, we show how SPLE, in particular variability modeling, can be applied to support the quality assurance of scientific frameworks. One of the main contributions of this thesis is a process for the creation of reengineering variability models for a scientific framework based on its mathematical requirements. Reengineering means the adjustment of a software system to improve the software quality, mostly without changing the software’s functionality. In our research, the variability models are created for existing software and therefore we call them reengineering variability models. The created variability models are used for a systematic development of system test applications for the framework. Additionally, we developed a model-based method for test case derivation for the system test applications based on the variability models. Furthermore, we contribute a software product line test strategy for scientific frameworks. A test strategy strongly influences the test activities performed. Another main contribution of this thesis is the design of a quality assurance process for scientific frameworks, which combines the test activities of the test strategy with other quality assurance activities. We introduce a list of special characteristics for scientific software, which we use as rationale for the design of this process. We report on a case study, analyzing the feasibility and acceptance by developers for two parts of the design of the quality assurance process: variability model creation and desk-checking, a kind of lightweight review. Using FeatureIDE, an environment for feature-oriented software development as well as an automated test environment, we prototypically demonstrate the applicability of our approach

Automatic mode tracking for flight dynamic analysis using a spanning algorithm

Identifying and tracking dynamic modes in a multi-dimensional parameter space is a problem that presents itself in many engineering disciplines. In a flight dynamics context, the dynamic modes refer to the modes of motion obtained from a linearisation of the aircraft system about a known operating point. Typically dynamic results derived from these linear models are unsorted, where mode indices are unrelated from one operating point to the next. When varying the parameters, or in this case operating point, difficulties in automating the process of relating modes from a linear system derived at one parameter set to the next exists. This paper builds on the work in tracking modes in a structural context, using the Modal Assurance Criterion (MAC) to numerically relate modes from two comparable linear systems. The (MAC) is deployed within a spanning algorithm to discover and identify all modes within all conditions, with their relationship to adjacent/neighbouring conditions. This is tested on a 1-, 2- and 3-dimensional parameter space, twelve state system

An Assurance Framework for Independent Co-assurance of Safety and Security

Integrated safety and security assurance for complex systems is difficult for many technical and socio-technical reasons such as mismatched processes, inadequate information, differing use of language and philosophies, etc.. Many co-assurance techniques rely on disregarding some of these challenges in order to present a unified methodology. Even with this simplification, no methodology has been widely adopted primarily because this approach is unrealistic when met with the complexity of real-world system development. This paper presents an alternate approach by providing a Safety-Security Assurance Framework (SSAF) based on a core set of assurance principles. This is done so that safety and security can be co-assured independently, as opposed to unified co-assurance which has been shown to have significant drawbacks. This also allows for separate processes and expertise from practitioners in each domain. With this structure, the focus is shifted from simplified unification to integration through exchanging the correct information at the right time using synchronisation activities

Methodology to Determine Melt Pool Anomalies in Powder Bed Fusion of Metals Using a Laser Beam by Means of Process Monitoring and Sensor Data Fusion

Additive manufacturing, in particular the powder bed fusion of metals using a laser beam, has a wide range of possible technical applications. Especially for safety-critical applications, a quality assurance of the components is indispensable. However, time-consuming and costly quality assurance measures, such as computer tomography, represent a barrier for further industrial spreading. For this reason, alternative methods for process anomaly detection using process monitoring systems have been developed. However, the defect detection quality of current methods is limited, as single monitoring systems only detect specific process anomalies. Therefore, a new methodology to evaluate the data of multiple monitoring systems is derived using sensor data fusion. Focus was placed on the causes and the appearance of defects in different monitoring systems (photodiodes, on- and off-axis high-speed cameras, and thermography). Based on this, indicators representing characteristics of the process were developed to reduce the data. Finally, deterministic models for the data fusion within a monitoring system and between the monitoring systems were developed. The result was a defect detection of up to 92% of the melt track defects. The methodology was thus able to determine process anomalies and to evaluate the suitability of a specific process monitoring system for the defect detection