Timing estimations for application lifecycle management work items determined through machine learning

Abstract

In some examples, a method may include accessing data records of completed work items associated with managing a lifecycle of a software application and extracting feature values from the data records of the completed work items for a selected set of features. The method may also include determining timing data of state transitions for the completed work items from the data records and generating a predictor through machine learning using the timing data and the extracted feature values as input samples. The generated predictor may provide a transition probability of a particular state transition for a work item with specific feature values. The method may further include using the predictor to determine an estimated timing of the particular state transition for the uncompleted work item.