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：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.