Method for the functional requirements transformation into tasks of the project management system

As global computerization and business process automation continue to advance, the volume of software development and, consequently, the number of technical and functional requirements are increasing correspondingly. This growth creates an urgent need to automate the conversion of project documentation into tasks within project management systems (PMS). Manual decomposition of large technical specifications and their subsequent transfer to issue trackers is not only time-consuming but also highly dependent on human factors. As a result, this approach extends planning timelines and creates a mismatch between initial business objectives and final development artifacts, thereby imposing a systemic constraint in the early stages of the IT project lifecycle. This paper presents a method to automate the generation of structured JSON data for import into issue trackers. A key feature of the developed method is the integration of a JSON Schema validation mechanism and an iterative error-correction loop to handle both syntactic and logical errors. Experimental validation demonstrated the high efficiency of the proposed approach: the share of valid JSON documents reached 95 %, significantly exceeding the 60 % baseline achieved by directly prompting large language models (LLMs). The replication accuracy of the hierarchical task structure (Epic → Story → Subtask) reached 88 %. Furthermore, the method ensures end-to-end requirements traceability, enabling the tracking of links from specific sections of the technical specification to the final development tasks. The research results confirm that the method reduces labor costs associated with routine data migration and minimizes error risks in IT project planning.

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