Review of Software Effort Estimation Methods for Projects on the 1C:Enterprise Platform

This paper reviews contemporary methods for software effort estimation in the development and maintenance of solutions based on the 1C:Enterprise platform. Published studies are analyzed to compare regression models, decision tree-based methods, ensemble learning algorithms, and language models applied to software effort estimation. Particular attention is paid to the types of data used for prediction, including structured task attributes, textual issue descriptions, expert estimates, and historical data.

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