Micro Focus Lcc

Entit Software LLC

Shaul Strachan

Raz Regev

George Kour

Ohad Assulin

Elad Benedict

Gabi Shalev

Sigal Ishay

A technique includes extracting data from a historical data store representing completed work items and associated features of the work items. The work items are associated with a lifecycle stage of an application. The technique includes training a regression model to estimate a time for completing a given work item based at least in part on the features.

Using machine learning regression to estimate time for completing application lifecycle management work item

Examples disclosed herein involve selecting tests for an application commit. In examples herein, a commit of an application code is detected based on a commit identifier in a request, the commit to update the application code, characteristics of the commit are determined from a repository of commit records; and tests for the commit are selected based on the characteristics of the application code to be executed to validate the updated application code.

Test selection for application commit

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.

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

Techniques for identifying a build commit that caused a test failure are provided. A build which includes a failed test may be identified. For each commit in the build a weighting factor may be calculated for files that have been previously associated with the failed test. The weighting factor may be based on the number of times the file has been associated with the failed test and the total number of tests. A weighting factor may also be calculated for files that have not been previously associated with the failed test based on the number of times the file appears with other files that are associated with the failed test. The weighting factors may be added to create a score for the commit. The scores for the commits in the build may be ordered. The higher the score, the more likely the commit was the cause of the failed test.

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Yehud, Israel

Shaul Strachan