公开(公告)号：US11200377B2

公开(公告)日：2021-12-14

申请号：US15498779

申请日：2017-04-27

Applicant: EntIT Software LLC

Inventor： Elad Benedict ,  Einat Atedgi ,  Ohad Assulin ,  Boaz Shor

IPC: G06F8/00 ,  G06N20/00 ,  G06F40/186 ,  G06F40/284 ,  G06F7/535 ,  G06F8/71 ,  G06N5/04 ,  G06F8/65

Abstract: Techniques to create and use cluster models to predict build failures are provided. In one aspect, clusters in a set of builds may be identified. The identified clusters may be used to create a model. The model may be used to predict causes of build failures. In another aspect, a failed build may be identified. A clustering model may be retrieved. A cause of problems with the failed build may be predicted using the clustering model.

公开(公告)号：US10169223B2

公开(公告)日：2019-01-01

申请号：US15491542

申请日：2017-04-19

Applicant: EntIT Software LLC

Inventor： Ohad Assulin ,  Elad Benedict ,  Shaul Strachan ,  Raz Regev ,  Gabi Shalev

IPC: G06F9/44 ,  G06F11/36 ,  G06F8/71

Abstract: 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.

公开(公告)号：US20180307593A1

公开(公告)日：2018-10-25

申请号：US15491542

申请日：2017-04-19

Applicant: EntIT Software LLC

Inventor： Ohad Assulin ,  Elad Benedict ,  Shaul Strachan ,  Raz Regev ,  Gabi Shalev

IPC: G06F11/36 ,  G06F9/44

CPC classification number: G06F11/3692 ,  G06F8/71

Abstract: 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.

公开(公告)号：US11501175B2

公开(公告)日：2022-11-15

申请号：US16075846

申请日：2016-02-08

Applicant: ENTIT SOFTWARE LLC

Inventor： Efrat Egozi-Levi ,  Ohad Assulin ,  Boaz Shor ,  Mor Gelberg

IPC: G06F16/248 ,  G06N5/02 ,  G06Q10/04 ,  G06F16/21

Abstract: Example embodiments relate to generating sets of recommended inputs for changing predicted results of a predictive model. The examples disclosed herein access, from a database, a historical set of inputs and results of a predictive model. A function is approximated based on the historical set of inputs and results, and a gradient of the function is computed using a result of the function with respect to a local maximum value of the function. A set of recommended inputs is generated based on the gradient of the function, where a recommended input produces a positive result of the function.

公开(公告)号：US20180314953A1

公开(公告)日：2018-11-01

申请号：US15498779

申请日：2017-04-27

Applicant: EntIT Software LLC

Inventor： Elad Benedict ,  Einat Atedgi ,  Ohad Assulin ,  Boaz Shor

IPC: G06N5/04 ,  G06F17/50 ,  G06F17/11 ,  G06F7/535 ,  G06F17/27

Abstract: Techniques to create and use cluster models to predict build failures are provided. In one aspect, clusters in a set of builds may be identified. The identified clusters may be used to create a model. The model may be used to predict causes of build failures. In another aspect, a failed build may be identified. A clustering model may be retrieved. A cause of problems with the failed build may be predicted using the clustering model.