Autocoding

Abstract: In a context of heightened safety requirements for safety-critical embedded systems and ever-increasing costs of verification and validation, we describe a fully automated, credible autocoding chain for control systems. This chain generates code, along with guarantees of high level functional properties, which cans be independently verified. The platform relies on domain specific knowledge and formal analysis methods to bridge the semantic gap between domain experts and code verification experts. First, a graphical dataflow language is extended with annotation symbols, enabling the control engineer to express high level properties of its control law within the framework of a familiar block-diagram language. A public-domain autocoder is enhanced not only to generate the code implementing the initial design, but also to carry high level properties down to annotations at the level of the code. Finally, using customized code analysis tools, certificates are generated, which guarantee the correctness of the annotations with respect to the code, and can be verified using existing static analysis tools. For now, we limit the conclusions to the bounded-input bounded-output characteristic of linear controllers, however the approach appears readily extendable to a broader array of properties and systems.

Abstract: Background Existing datasets often lack job exposure data. Standard Occupational Classification (SOC) codes can link work exposure data to health outcomes via a Job Exposure Matrix, but manually assigning SOC codes is laborious. We explored the utility of two SOC autocoding programs. Methods We entered industry and occupation descriptions from two existing cohorts into two publicly available SOC autocoding programs. SOC codes were also assigned manually by experienced coders. These SOC codes were then linked to exposures from the Occupational Information Network (O*NET). Results Agreement between the SOC codes produced by autocoding programs and those produced manually was modest at the 6-digit level, and strong at the 2-digit level. Importantly, O*NET exposure values based on SOC code assignment showed strong agreement between manual and autocoded methods. Conclusion Both available autocoding programs can be useful tools for assigning SOC codes, allowing linkage of occupational exposures to data containing free-text occupation descriptors.

Abstract: Background Autocoding (or automatic concept indexing) occurs when a software program extracts terms contained within text and maps them to a standard list of concepts contained in a nomenclature. The purpose of autocoding is to provide a way of organizing large documents by the concepts represented in the text. Because textual data accumulates rapidly in biomedical institutions, the computational methods used to autocode text must be very fast. The purpose of this paper is to describe the doublet method, a new algorithm for very fast autocoding.