System and method for performing early delayed issue detection and providing alert notification

A system for determining and mitigating a potential delay in task completion, the system comprising:

• at least one memory;
• and
• at least one processor,
• wherein the system is configured to perform:
• extracting a plurality of previously resolved tasks and related information;
• analyzing the extracted plurality of previously resolved tasks and related information;
• extracting changes occurred to each of the plurality of previously resolved tasks;
• calculating features for training a machine learning model based on the extracted changes;
• dividing the plurality of previously resolved tasks into different groups, the different groups include a first group and a second group;
• training, the machine learning model, using the calculated features associated with the different groups of the plurality of previously resolved tasks;
• starting a sprint including a target task;
• determining a cutoff time for determining whether the target task will be completed by an end date of the sprint;
• processing the target task until reaching the cutoff time;
• upon reaching the cutoff time, processing change history of the target task up to the cutoff time and before the sprint is completed, and extracting a sequence of changes included in the change history;
• and
• executing the machine learning model using the extracted sequence of changes for determining whether a delay is expected or not for the target task.

A non-transitory computer readable storage medium that stores a computer program for determining and mitigating a potential delay in task completion, the computer program, when executed by a processor, causing a system to perform a plurality of processes comprising:

• extracting a plurality of previously resolved tasks and related information;
• analyzing the extracted plurality of previously resolved tasks and related information;
• extracting changes occurred to each of the plurality of previously resolved tasks;
• calculating features for training a machine learning model based on the extracted changes;
• dividing the plurality of previously resolved tasks into different groups, the different groups include a first group and a second group;
• training, the machine learning model, using the calculated features associated with the different groups of the plurality of previously resolved tasks;
• starting a sprint including a target task;
• determining a cutoff time for determining whether the target task will be completed by an end date of the sprint;
• processing the target task until reaching the cutoff time;
• upon reaching the cutoff time, processing change history of the target task up to the cutoff time and before the sprint is completed, and extracting a sequence of changes included in the change history;
• and
• executing the machine learning model using the extracted sequence of changes for determining whether a delay is expected or not for the target task.