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AssigneeXilinx, Inc.
InventorsAmbujavalli Kesavan, Kumaraswamy Namburu, Priyadarshini Vijjigiri, Aman Rustagi, Desmond Lobo
Estimating the term…
Systems and methods for dynamically selecting source code development tests using a trained machine learning (ML) model are disclosed. In certain embodiments, a plurality of data features is derived from information indicating a plurality of modifications to a source code repository. Based at least in part on the derived data features, an ML model is trained to identify correlations between the modifications and a plurality of historical source code development test results. Upon receiving an indication of one or more additional modifications to the source code repository, the trained ML model dynamically selects, from a plurality of source code development tests, a subset of source code development tests relevant to the additional modifications.
A method comprising:
A system, comprising:
A non-transitory computer-readable medium storing a set of executable instructions that, when executed by one or more processors, causes at least one of the one or more processors to:
Dependencies are inferred from the claim text.