Facebook, Inc.

Yi Zeng

Chang Huang

Scott Kenneth Yost

Juyuan Yang

In one embodiment, a method includes receiving an text query, identifying and ranking multiple content objects corresponding to character strings from the text query. A computing system determines the ranking based on an analysis of the association between the terms in the content objects and the n-grams in the query using term frequency-inverse document frequency (TF-IDF) scores of the terms. The computing system accesses a query-term index to determine the TF-IDF scores for the terms, wherein the query-term index includes multiple entries corresponding to multiple terms extracted from multiple prior queries. Each entry in the query-term index includes a query term, one or more related terms extracted from content objects of the online social network matching the prior query term, and a term frequency-inverse document frequency (TF-IDF) score for the respective related terms.

Determining related query terms through query-post associations on online social networks

Dynamically scheduled testing of a software application on one or more devices are disclosed. The system can schedule and execute of one or more test cases associated with a software application. When a device becomes available for testing, the system can determine the list of commits that have not yet been tested. If there are outstanding untested commits, the system can determine the list of test cases in a test plan that are to be scheduled and executed. For each test in the list of test cases, the system can compute an urgency value. Urgency value can be a function of the untested commits. For example, urgency value can be the number of untested commits associated with each test case. The system can then sort the test cases based on the urgency value and schedule the most 'urgent' test case for execution.

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Menlo Park, CA, United States of America

Yi Zeng