公开(公告)号：US20180052853A1

公开(公告)日：2018-02-22

申请号：US15243598

申请日：2016-08-22

Applicant: salesforce.com, inc.

Inventor： Scott Thurston Rickard, JR. ,  Clifford Z. Huang ,  J. Justin Donaldson

IPC: G06F17/30

CPC classification number: G06F16/24578 ,  G06F16/22 ,  G06F16/9535

Abstract: A system stores objects of different types and allows search over the objects. The system receives search requests and processes them to determine search results matching the search criteria. The system ranks the search results based on weighted aggregates of features describing objects represented by each search result. The system monitors search results that were accessed by user for further information and marks them as accessed results. The system adjusts the feature weights used for ranking search results to optimize the ranking of the search results. The system analyzes the result of using the adjusted feature weights on past searches that are stored in the system. The system determines an aggregate accessed results rank for each adjusted set of weights. The system selects a set of feature weights that optimizes the aggregate accessed results rank for past searches.

公开(公告)号：US20180101534A1

公开(公告)日：2018-04-12

申请号：US15292033

申请日：2016-10-12

Applicant: salesforce.com, inc.

Inventor： Zachary Alexander, JR. ,  Scott Thurston Rickard, JR. ,  Clifford Z. Huang ,  J. Justin Donaldson

IPC: G06F17/30

CPC classification number: G06F16/93

Abstract: A document retrieval system tracks user selections of documents from query search results and uses the selections as proxies for manual user labeling of document relevance. The system trains a model representing the significance of different document features when calculating true document relevance for users. To factor in positional biases inherent in user selections in search results, the system learns positional bias values for different search result positions, such that the positional bias values are accounted for when computing document feature features that are used to compute true document relevance.

公开(公告)号：US10565265B2

公开(公告)日：2020-02-18

申请号：US15292033

申请日：2016-10-12

Applicant: salesforce.com, inc.

Inventor： Zachary Alexander ,  Scott Thurston Rickard, Jr. ,  Clifford Z. Huang ,  J. Justin Donaldson

IPC: G06F17/30 ,  G06F16/93

Abstract: A document retrieval system tracks user selections of documents from query search results and uses the selections as proxies for manual user labeling of document relevance. The system trains a model representing the significance of different document features when calculating true document relevance for users. To factor in positional biases inherent in user selections in search results, the system learns positional bias values for different search result positions, such that the positional bias values are accounted for when computing document feature features that are used to compute true document relevance.

公开(公告)号：US10409667B2

公开(公告)日：2019-09-10

申请号：US15624000

申请日：2017-06-15

Applicant: salesforce.com, inc.

Inventor： J. Justin Donaldson ,  Hormoz Tarevern ,  Sadiya Hameed ,  Siddharth Srivastava ,  Feifei Jiang

IPC: G06F11/00 ,  G06F11/07 ,  G06N3/08 ,  G06N3/04

Abstract: An online system identifies an assignment for a computer program error indicated in an error message by applying an assignment model to token sequences identified in the error message. The error message includes a sequence of execution paths of the computer program. Each execution path indicates a function call active in computer memory when the error was generated. In other words, the error message allows tracking of the sequence of nested paths up to the point where the error was generated. In one example, the error message is a stack trace message that reports active stack frames in computer memory during the execution of the program.

公开(公告)号：US10474562B2

公开(公告)日：2019-11-12

申请号：US15710127

申请日：2017-09-20

Applicant: salesforce.com, inc.

Inventor： J. Justin Donaldson ,  Benjamin Busjaeger ,  Siddharth Rajaram ,  Berk Coker ,  Hormoz Tarevern

IPC: G06F11/36 ,  G06N3/08 ,  G06F8/71 ,  G06F16/13

Abstract: An online system ranks test cases run in connection with check-in of sets of software files in a software repository. The online system ranks the test cases higher if they are more likely to fail as a result of defects in the set of files being checked in. Accordingly, the online system informs software developers of potential defects in the files being checked in early without having to run the complete suite of test cases. The online system determines a vector representation of the files and test cases based on a neural network. The online system determines an aggregate vector representation of the set of files. The online system determines a measure of similarity between the test cases and the aggregate vector representation of the set of files. The online system ranks the test cases based on the measures of similarity of the test cases.

公开(公告)号：US20180365091A1

公开(公告)日：2018-12-20

申请号：US15624000

申请日：2017-06-15

Applicant: salesforce.com, inc.

Inventor： J. Justin Donaldson ,  Hormoz Tarevern ,  Sadiya Hameed ,  Siddharth Srivastava ,  Feifei Jiang

IPC: G06F11/07 ,  G06N3/08 ,  G06N3/04

CPC classification number: G06F11/079 ,  G06F11/0706 ,  G06F11/0751 ,  G06F11/0772 ,  G06N3/02 ,  G06N3/04 ,  G06N3/08

Abstract: An online system identifies an assignment for a computer program error indicated in an error message by applying an assignment model to token sequences identified in the error message. The error message includes a sequence of execution paths of the computer program. Each execution path indicates a function call active in computer memory when the error was generated. In other words, the error message allows tracking of the sequence of nested paths up to the point where the error was generated. In one example, the error message is a stack trace message that reports active stack frames in computer memory during the execution of the program.

公开(公告)号：US20190087311A1

公开(公告)日：2019-03-21

申请号：US15710127

申请日：2017-09-20

Applicant: salesforce.com, inc.

Inventor： J. Justin Donaldson ,  Benjamin Busjaeger, JR. ,  Siddharth Rajaram ,  Berk Coker, JR. ,  Hormoz Tarevern

IPC: G06F11/36 ,  G06F17/30 ,  G06N3/08

Abstract: An online system ranks test cases run in connection with check-in of sets of software files in a software repository. The online system ranks the test cases higher if they are more likely to fail as a result of defects in the set of files being checked in. Accordingly, the online system informs software developers of potential defects in the files being checked in early without having to run the complete suite of test cases. The online system determines a vector representation of the files and test cases based on a neural network. The online system determines an aggregate vector representation of the set of files. The online system determines a measure of similarity between the test cases and the aggregate vector representation of the set of files. The online system ranks the test cases based on the measures of similarity of the test cases.