Abstract: Embodiments relate to analyzing developmental progress and productivity of users based upon monitored activity features, and inferring expected levels of productivity for future time intervals. By analyzing code check-ins submitted by a user over a time interval, as other features associated with user activities during the time interval, a metric indicating a level of quality or productivity for the time interval can be determined. Based upon the determined metrics for the time intervals, expected levels of productivity for the user can be inferred for future time intervals. In addition, tasks can be automatically performed to reduce an amount of impact on the productivity of the user during time intervals of expected high productivity, such as scheduling of tasks outside of the time intervals, adjusting certain settings of a user device, and/or the like.

Abstract: Embodiments relate to analyzing developmental progress and productivity of users based upon monitored activity features, and inferring expected levels of productivity for future time intervals. By analyzing code check-ins submitted by a user over a time interval, as other features associated with user activities during the time interval, a metric indicating a level of quality or productivity for the time interval can be determined. Based upon the determined metrics for the time intervals, expected levels of productivity for the user can be inferred for future time intervals. In addition, tasks can be automatically performed to reduce an amount of impact on the productivity of the user during time intervals of expected high productivity, such as scheduling of tasks outside of the time intervals, adjusting certain settings of a user device, and/or the like.

Abstract: Embodiments relate to analyzing developmental progress and productivity of users based upon monitored activity features, and inferring expected levels of productivity for future time intervals. By analyzing code check-ins submitted by a user over a time interval, as other features associated with user activities during the time interval, a metric indicating a level of quality or productivity for the time interval can be determined. Based upon the determined metrics for the time intervals, expected levels of productivity for the user can be inferred for future time intervals. In addition, tasks can be automatically performed to reduce an amount of impact on the productivity of the user during time intervals of expected high productivity, such as scheduling of tasks outside of the time intervals, adjusting certain settings of a user device, and/or the like.

Abstract: Embodiments relate to analyzing developmental progress and productivity of users based upon monitored activity features, and inferring expected levels of productivity for future time intervals. By analyzing code check-ins submitted by a user over a time interval, as other features associated with user activities during the time interval, a metric indicating a level of quality or productivity for the time interval can be determined. Based upon the determined metrics for the time intervals, expected levels of productivity for the user can be inferred for future time intervals. In addition, tasks can be automatically performed to reduce an amount of impact on the productivity of the user during time intervals of expected high productivity, such as scheduling of tasks outside of the time intervals, adjusting certain settings of a user device, and/or the like.