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As software is rapidly being embedded into major parts of our society, ranging from medical devices and self-driving vehicles to critical infrastructures, potential risks of software failures are also growing at an alarming pace. Existing certification processes, however, suffer from a lack of rigor and automation, and often incur a significant amount of manual effort on both system developers and certifiers. To address this issue, we propose a substantially automated, cost-effective certification method, backed with a novel analysis synthesis technique to automatically generate application-specific analysis tools that are custom-tailored to producing the necessary evidence. The outcome of this research promises to not only assist software developers in producing safer and more reliable software, but also benefit industrial certification agencies by significantly reducing the manual effort of certifiers. Early validation flows from experience applying this approach in constructing an assurance case for a surgical robot system in collaboration with the Center for the Advanced Surgical Technology. 

Abstract Motivation As the cost of sequencing decreases, the amount of data being deposited into public repositories is increasing rapidly. Public databases rely on the user to provide metadata for each submission that is prone to user error. Unfortunately, most public databases, such as non-redundant (NR), rely on user input and do not have methods for identifying errors in the provided metadata, leading to the potential for error propagation. Previous research on a small subset of the non-redundant (NR) database analyzed misclassification based on sequence similarity. To the best of our knowledge, the amount of misclassification in the entire database has not been quantified. We propose a heuristic method to detect potentially misclassified taxonomic assignments in the NR database. We applied a curation technique and quality control to find the most probable taxonomic assignment. Our method incorporates provenance and frequency of each annotation from manually and computationally created databases and clustering information at 95% similarity. Results We found more than 2 million potentially taxonomically misclassified proteins in the NR database. Using simulated data, we show a high precision of 97% and a recall of 87% for detecting taxonomically misclassified proteins. The proposed approach and findings could also be applied to other databases. Availability Source code, dataset, documentation, Jupyter notebooks, and Docker container are available at https://github.com/boalang/nr. Supplementary information Supplementary data are available at Bioinformatics online.