Salesforce Com, Inc.

J Justin Donaldson

Hormoz Tarevern

Zachary Alexander

Benjamin Busjaeger

Scott Thurston Rickard, Jr

Feifei Jiang

Siddharth Rajaram

Clifford Z Huang

Siddharth Srivastava

Berk Coker

Sadiya Hameed

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.

Accounting for positional bias in a document retrieval system using machine learning

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.

Machine learning based ranking of test cases for software development

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.

Error assignment for computer programs

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Seattle, WA, United States of America

J Justin Donaldson