Applying Formal Methods to Elicit Specifications for Grid Data Analysis Applications Using Machine Learning Algorithms

This research focuses on the application of formal methods to elicit formal specifications for machine learning algorithms (MLAs) in the context of grid data analysis. The incorporation of machine learning into grid data applications holds significant importance for enhancing decision-making processes. However, the absence of precise specifications often poses challenges in the development and validation of these algorithms. This study focuses on the early integration of formal methods throughout the development lifecycle to specify requirements for MLAs in grid data analysis applications. The paper will first list the MLA requirements in natural languages, existing solutions, and the utilization of Descartes Agent, a formal specification language, for early-stage specifications. Descartes Agent is employed to model the functional requirements of MLAs. Through a case study example, this study demonstrates the efficacy of Descartes - Agent in precisely specifying functional needs for MLAs in grid data analysis applications that leverage machine learning.