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Register a new userTowards a Cross-Domain Software Safety Assurance Process for Embedded Systems
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Added by
Marc Zeller
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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In this work, we outline a cross-domain assurance process for safety-relevant software in embedded systems. This process aims to be applied in various different application domains and in conjunction with any development methodology. With this approach we plan to reduce the growing effort for safety assessment in embedded systems by reusing safety analysis techniques and tools for the product development in different domains.
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Computer systems are so complex, so they are usually designed and analyzed in terms of layers of abstraction. Complexity is still a challenge facing logical reasoning tools that are used to find software design flaws and implementation bugs. Abstraction is also a common technique for scaling those tools to more complex systems. However, the abstractions used in the design phase of systems are in many cases different from those used for assurance. In this paper we argue that different software quality assurance techniques operate on different aspects of software systems. To facilitate assurance, and for a smooth integration of assurance tools into the Software Development Lifecycle (SDLC), we present a 4-dimensional meta-architecture that separates computational, coordination, and stateful software artifacts early on in the design stage. We enumerate some of the design and assurance challenges that can be addressed by this meta-architecture, and demonstrate it on the high-level design of a simple file system.
Safety-critical software systems are in many cases designed and implemented as families of products, usually referred to as Software Product Lines (SPLs). Products within an SPL vary from each other in terms of which features they include. Applying existing analysis techniques to SPLs and their safety cases is usually challenging because of the potentially exponential number of products with respect to the number of supported features. In this paper, we present a methodology and infrastructure for certified emph{lifting} of existing single-product safety analyses to product lines. To ensure certified safety of our infrastructure, we implement it in an interactive theorem prover, including formal definitions, lemmas, correctness criteria theorems, and proofs. We apply this infrastructure to formalize and lift a Change Impact Assessment (CIA) algorithm. We present a formal definition of the lifted algorithm, outline its correctness proof (with the full machine-checked proof available online), and discuss its implementation within a model management framework.
This paper describes a comprehensive prototype of large-scale fault adaptive embedded software developed for the proposed Fermilab BTeV high energy physics experiment. Lightweight self-optimizing agents embedded within Level 1 of the prototype are responsible for proactive and reactive monitoring and mitigation based on specified layers of competence. The agents are self-protecting, detecting cascading failures using a distributed approach. Adaptive, reconfigurable, and mobile objects for reliablility are designed to be self-configuring to adapt automatically to dynamically changing environments. These objects provide a self-healing layer with the ability to discover, diagnose, and react to discontinuities in real-time processing. A generic modeling environment was developed to facilitate design and implementation of hardware resource specifications, application data flow, and failure mitigation strategies. Level 1 of the planned BTeV trigger system alone will consist of 2500 DSPs, so the number of components and intractable fault scenarios involved make it impossible to design an `expert system that applies traditional centralized mitigative strategies based on rules capturing every possible system state. Instead, a distributed reactive approach is implemented using the tools and methodologies developed by the Real-Time Embedded Systems group.
Software testing is sometimes plagued with intermittently failing tests and finding the root causes of such failing tests is often difficult. This problem has been widely studied at the unit testing level for open source software, but there has been far less investigation at the system test level, particularly the testing of industrial embedded systems. This paper describes our investigation of the root causes of intermittently failing tests in the embedded systems domain, with the goal of better understanding, explaining and categorizing the underlying faults. The subject of our investigation is a currently-running industrial embedded system, along with the system level testing that was performed. We devised and used a novel metric for classifying test cases as intermittent. From more than a half million test verdicts, we identified intermittently and consistently failing tests, and identified their root causes using multiple sources. We found that about 1-3% of all test cases were intermittently failing. From analysis of the case study results and related work, we identified nine factors associated with test case intermittence. We found that a fix for a consistently failing test typically removed a larger number of failures detected by other tests than a fix for an intermittent test. We also found that more effort was usually needed to identify fixes for intermittent tests than for consistent tests. An overlap between root causes leading to intermittent and consistent tests was identified. Many root causes of intermittence are the same in industrial embedded systems and open source software. However, when comparing unit testing to system level testing, especially for embedded systems, we observed that the test environment itself is often the cause of intermittence.
Building on concepts drawn from control theory, self-adaptive software handles environmental and internal uncertainties by dynamically adjusting its architecture and parameters in response to events such as workload changes and component failures. Self-adaptive software is increasingly expected to meet strict functional and non-functional requirements in applications from areas as diverse as manufacturing, healthcare and finance. To address this need, we introduce a methodology for the systematic ENgineering of TRUstworthy Self-adaptive sofTware (ENTRUST). ENTRUST uses a combination of (1) design-time and runtime modelling and verification, and (2) industry-adopted assurance processes to develop trustworthy self-adaptive software and assurance cases arguing the suitability of the software for its intended application. To evaluate the effectiveness of our methodology, we present a tool-supported instance of ENTRUST and its use to develop proof-of-concept self-adaptive software for embedded and service-based systems from the oceanic monitoring and e-finance domains, respectively. The experimental results show that ENTRUST can be used to engineer self-adaptive software systems in different application domains and to generate dynamic assurance cases for these systems.
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