公开(公告)号：US20240272874A1

公开(公告)日：2024-08-15

申请号：US18136930

申请日：2023-04-20

Applicant: Cognizant Technology Solutions India Pvt. Ltd.

Inventor： Dattaprasad Kulkarni ,  Hemant Patil ,  Rajeswari Kumaran ,  Neya Renganathan ,  Radha Ponram ,  Komal Sinha

IPC: G06F8/10 ,  G06F40/295

CPC classification number: G06F8/10 ,  G06F40/295

Abstract: A system and method for optimized processing of requirements data in a software development life cycle is provided. The present invention provides for determining a first pre-defined parameter, a second pre-defined parameter, a third pre-defined parameter, a fourth pre-defined parameter, a fifth pre-defined parameter, a sixth pre-defined parameter, and a seventh pre-defined parameter associated with user story by applying pre-defined rules respectively. Further, an output is rendered as Requirement Completeness Index (RCI) for user story data, and corrective actions are automatically carried out on user story data based on the generated RCI.

公开(公告)号：US20240346447A1

公开(公告)日：2024-10-17

申请号：US18212385

申请日：2023-06-21

Applicant: Cognizant Technology Solutions India Pvt. Ltd.

Inventor： Dattaprasad Kulkarni ,  Hemant Patil ,  Rajeswari Kumaran ,  Shikha Grover ,  Lucky Singh

IPC: G06Q10/10 ,  G06Q10/0631

CPC classification number: G06Q10/103 ,  G06Q10/06311

Abstract: The present invention provides for a system for optimizing sprint-based tasks implemented in an agile methodology. The system comprises a memory and a processor configured to execute a task optimization engine to receive input data comprising story point data associated with historical user stories captured during previous sprints, where the input data is analyzed to determine a first feature dataset. A timeseries dataset is determined for forecasting unplanned task for upcoming sprint based on analysis of the input data associated with unplanned task of the previous sprints and a dataset associated with attributes is determined based on the input data associated with the previous sprints for story point data of an upcoming sprint. The datasets are combined to generate a persistent identifier for sprint capacity buffer data values to optimize sprint-based tasks in agile methodology.