Abstract: Example aspects include techniques for improved constraint discovery on relational data. These techniques may include constructing a first layer of a lattice using a first plurality of candidate denial constraints (DC) each having a first number of predicates. In addition, the techniques may include performing a tree-based verification process on the first layer of the lattice to determine one or more verified DCs confirmed to be DCs and a plurality of unverified DCs that are not confirmed to be DCs. Further, the techniques may include presenting, via a graphical user interface (GUI), DC information based on the one or more verified DC, and generating, for construction of a second layer of the lattice to be evaluated via the tree-based verification process, a second plurality of candidate DCs by combining the plurality of unverified DCs.

Abstract: Techniques are described herein that are capable of creating a language-agnostic computer program repair engine generator. A context-free grammar is annotated to identify token(s) that are likely to be included in or excluded from a computer program in a manner that violates the context-free grammar. A language-agnostic computer program repair engine generator is created that is configured to generate a parser. The repair engine generator is configured to create a repair engine that: converts the candidate string into repaired strings that neither violate the context-free grammar nor violate a criterion for a valid computer program; calculates differences between the candidate string and the respective repaired strings; and replaces the candidate string with a designated repaired string based at least in part on the difference between the designated repaired string and the candidate string being less than or equal to a difference threshold.