Abstract: Users of a database management engine may generate fillable digital documents by mapping interface elements onto form documents. When a user maps interface elements onto a form document, the user may accidentally overlap two or more interface elements. To rectify this, the database management engine may modify the position of one of interface elements based on a set of positioning rules. In addition, the database management engine may identify and suggest mappings to users based on similar documents that have been previously mapped. The database management engine identifies similar documents using information about the document, the user, and the mapping itself. The mapping associated with the most similar document may be provided to the user as a suggested mapping. The database management engine converts the form document and finalized mapping into a fillable digital document. The fillable digital document is sent to recipients, who complete the fillable digital document.

Abstract: A codebase system that maintains and utilizes a dynamic mapping between a collection of tests and files within a codebase to ensure error detection and code validity. The codebase system accesses a collection of tests configured to process files within the codebase to identify errors. It then generates a mapping by executing these tests to determine the specific files each test operates on. Upon detecting updates to the test collection, the codebase system updates this mapping by re-executing the updated tests, identifying the new set of files for each test, and aggregating these updates into the existing mapping. When changes to the files in the codebase are detected, the codebase system accesses the updated mapping to identify which tests relate to the altered files. The codebase system then executes this relevant subset of tests to validate the modifications made to the files.

Abstract: A central database system trains and applies machine-learned models based on characteristics of one or more entities associated with the central database system. For instance, the central database system trains a machine-learned model configured to identify issues a target entity is likely to encounter based on training data identifying characteristics of historical entities and issues faced by the historical entities. Likewise, the central database system trains machine-learned models configured to predict actions that entities are likely to take in the future, and resources required to take those actions. The central database system can then perform one or more proactive actions or make one or more recommendations based on the predicted issues, the predicted future actions, and the predicted required resources.