Abstract: The subject technology receives a first query plan, the first query plan including a set of statements, the set of statements including at least one statement with at least a reference to a particular column in a first table. The subject technology identifies a first predicate from the at least one statement, the first predicate corresponding to a join operation. The subject technology determines, for the particular column, a set of expression properties. The subject technology, based on the set of expression properties, determines a first derived predicate, the first derived predicate corresponding to a first filter operation. The subject technology generates a new operation based on the first derived predicate. The subject technology generates a new query plan based at least in part on the join operation of first predicate, the first derived predicate, and the new operation of the first derived predicate.

Abstract: The subject technology provides embodiments for enabling derivation of predicates not only from other predicates but also from metadata such as expression properties. In examples, predicates are derived, avoiding unwanted impact on cardinality estimation. In other examples, predicates are derived, avoiding artificial runtime errors and providing a way to avoid security issues with secure views.

Abstract: A method, system, and computer program product for parallel regression to bypass regression process interruption are provided. The method identifies an RTL design. The RTL design is converted into a control data flow graph having a plurality of nodes and a plurality of arcs. A mapping table is generated with a machine learning database from a set of test cases based on the control data flow graph. The method selectively calls one or more test cases of the set of test cases to merge a branch to a main repository to bypass a portion of a regression process.

Abstract: A method, system, and computer program product for parallel regression to bypass regression process interruption are provided. The method identifies an RTL design. The RTL design is converted into a control data flow graph having a plurality of nodes and a plurality of arcs. A mapping table is generated with a machine learning database from a set of test cases based on the control data flow graph. The method selectively calls one or more test cases of the set of test cases to merge a branch to a main repository to bypass a portion of a regression process.

Abstract: Aspects of the invention include monitoring code coverage by executing a code sequence having a plurality of embedded markers. Aspects also include transmitting, upon encountering one of the plurality of embedded markers, a probing signal corresponding to the one of the plurality of embedded markers. Aspects further include obtaining, by a programmable data recorder, a debug level for the execution of the code sequence. Aspects also include storing the probing signal in a trace array based on a determination, by a programmable data recorder based on the debug level, that the probing signal should be recorded.

Abstract: Techniques for join elimination are described herein. Join elimination can identify and eliminate unnecessary joins in a query plan node. For example, join elimination can involve a semantic query optimization technique, which removes reference to a table whose columns are only referenced in join predicates if the joins do not filter/expand rows in the result. Such joins can cause significant performance issues on larger datasets if not optimized.

Abstract: A method, system, and computer program product for parallel regression to bypass regression process interruption are provided. The method identifies an RTL design. The RTL design is converted into a control data flow graph having a plurality of nodes and a plurality of arcs. A mapping table is generated with a machine learning database from a set of test cases based on the control data flow graph. The method selectively calls one or more test cases of the set of test cases to merge a branch to a main repository to bypass a portion of a regression process.

Abstract: Examples described herein generally relate to executing a received graph database query. The received graph database query can be converted into a recursive common table expression (CTE). Multiple alternative processes for executing the recursive CTE can be generated based on the recursive CTE. A cost associated with each of the multiple alternative processes can be determined. One of the multiple alternative processes can be converted into a multi-step sequence based on the associated cost. The multi-step sequence can be executed on a database to retrieve a set of results in response to the received graph database query.

Abstract: A method, system, and computer program product are described for use with graph-based verification of a circuit design. The method comprises performing, using a graph referencing the circuit design, a predefined set of one or more test sequences. The method further comprises determining that a first coverage point of the graph is not fully hit by the predefined set, and back-traversing the graph from a second coverage point of the graph that is partially hit or fully hit by the predefined set. The method further comprises generating, based on back-traversing the graph, a prospective test sequence to fully hit the first coverage point.

Abstract: Examples described herein generally relate to executing a received graph database query. The received graph database query can be converted into a recursive common table expression (CTE). Multiple alternative processes for executing the recursive CTE can be generated based on the recursive CTE. A cost associated with each of the multiple alternative processes can be determined. One of the multiple alternative processes can be converted into a multi-step sequence based on the associated cost. The multi-step sequence can be executed on a database to retrieve a set of results in response to the received graph database query.