An Automated Software FMEA

Abstract The concept of software failure mode and effects analysis (FMEA) has grown in attractiveness over recent years as a way of assessing the reliability of software. Like its hardware counterpart, software FMEA is immensely tedious for an engineer to perform, as well as being error-prone. This paper presents the implementation of a novel method for automating code-level software FMEA based on treating the implemented software as a model of the desired system and propagating faults through the model to identify dependencies. The method provides results at a level where they can be understood and acted on by software engineers. A tool implementing this method has been applied to a travel expenses payment program, and some of the automatically produced results are presented. Such automation extends significantly the range of software for which software FMEA becomes a realistic proposition. The analysis is tractable, and has been shown to provide useful results for software engineers. One important use of this analysis is to focus further testing. The software FMEA can be used to improve automated or source code embedded testing since tests can exonerate many potential faults allowing the FMEA analysis to present an engineer with a reduced set of potential faults

On the requirements of digital twin-driven autonomous maintenance

Autonomy has become a focal point for research and development in many industries. Whilst this was traditionally achieved by modelling self-engineering behaviours at the component-level, efforts are now being focused on the sub-system and system-level through advancements in artificial intelligence. Exploiting its benefits requires some innovative thinking to integrate overarching concepts from big data analysis, digitisation, sensing, optimisation, information technology, and systems engineering. With recent developments in Industry 4.0, machine learning and digital twin, there has been a growing interest in adapting these concepts to achieve autonomous maintenance; the automation of predictive maintenance scheduling directly from operational data and for in-built repair at the systems-level. However, there is still ambiguity whether state-of-the-art developments are truly autonomous or they simply automate a process. In light of this, it is important to present the current perspectives about where the technology stands today and indicate possible routes for the future. As a result, this effort focuses on recent trends in autonomous maintenance before moving on to discuss digital twin as a vehicle for decision making from the viewpoint of requirements, whilst the role of AI in assisting with this process is also explored. A suggested framework for integrating digital twin strategies within maintenance models is also discussed. Finally, the article looks towards future directions on the likely evolution and implications for its development as a sustainable technolog