Abstract: Some embodiments may be associated with facilitating extensibility for an enterprise portal in a cloud computing environment. A computer processor of a multi-level extensibility framework server may provide to a user a graphical view of existing services of the enterprise portal using information from the business enterprise portal data store and a sample data model. The processor may also receive from the user extension information for at least one of the technical layers and, based on the received extension information, automatically generate and provide an intelligent extension proposal to the user. The processor may also display simulated results to the user based on the intelligent extension proposal and the sample data model. The processor may then receive from the user a confirmation of the intelligent extension proposal and automatically transfer extension fields, entities, and mapping to multiple technical layers of the enterprise portal.

Abstract: According to some embodiments, systems and methods are provided, comprising receiving a code fragment exhibiting a static code issue; determining, via a trained exemption neural network, whether the received code fragment is exempt or not exempt from resolution; in a case it is not exempt, inputting the code fragment to a trained classification neural network; determining whether the static code issue is a syntactical static code issue or a non-syntactical static code issue; in a case it is a syntactical static code issue, inputting the code fragment to a first trained network to generate a first resolution; and in a case the static code issue is a non-syntactical static code issue, inputting the code fragment to a second trained network to generate a second resolution of the non-syntactical static code issue. Numerous other aspects are provided.

Abstract: Various examples are directed to systems and methods for evaluating an Application Program Interface (API) for interfacing an application to a database through a data model. A computing system may access a first view data structure associated with a first API call. The computing system may use a first view data structure to select a first table from the one or more tables, the first table being associated with the first API call, where the data model is described by a plurality of view data structures based on one or more tables at a database, including the first view data structure. The computing system may measure the API against a standard of performance for the API using a first performance double view data structure and a first performance double table.

Abstract: Embodiments may be associated with database access. In some embodiments, a machine learning linear regression training platform determines a set of database properties (e.g., direct and/or indirect properties such as a document type, a type of process, a number of items, etc.) associated with Online Transaction Processing (“OLTP”) database table access. The machine learning linear regression training platform may then train a linear regression model based on the set of database properties and prior locking information (e.g., locking and unlocking timestamps) that represent access to the OLTP database table. Information about the linear regression model may be output to a wait time estimation platform that adapted to use the linear regression model to generate, in substantially real-time, an estimated wait time for an OLTP database table access based on the database properties of the OLTP database table access.

Abstract: Technologies are provided for assigning developers to source code issues using machine learning. A machine learning model can be generated based on multiple versions of source code objects (such as source code files, classes, modules, packages, etc.), such as those that are managed by a version control system. The versions of the source code objects can reflect changes that are made to the source code objects over time. Associations between developers and source code object versions can be analyzed and used to train the machine learning model. Patterns of similar changes to various source code objects can be detected and can also be used to train the machine learning model. When an issue is detected in a version of a source code object, the model can be used to identify a developer to assign to the issue. Feedback data regarding the developer assignment can be used to re-train the model.

Abstract: A method, a system, and a computer program product for generating name recommendations in a core data services computing environment. A dataset for training a name data model is received. The name data model is configured for determination of a recommendation for one or more names in a plurality of names associated with one or more artifacts in a plurality of artifacts of a database management system. The name data model is trained using the received dataset and applied to generate one or more names associated with the one or more artifacts.

Abstract: According to some embodiments, systems and methods are provided, comprising receiving a code fragment exhibiting a static code issue; determining, via a trained exemption neural network, whether the received code fragment is exempt or not exempt from resolution; in a case it is not exempt, inputting the code fragment to a trained classification neural network; determining whether the static code issue is a syntactical static code issue or a non-syntactical static code issue; in a case it is a syntactical static code issue, inputting the code fragment to a first trained network to generate a first resolution; and in a case the static code issue is a non-syntactical static code issue, inputting the code fragment to a second trained network to generate a second resolution of the non-syntactical static code issue. Numerous other aspects are provided.

Abstract: Technologies are provided for assigning developers to source code issues using machine learning. A machine learning model can be generated based on multiple versions of source code objects (such as source code files, classes, modules, packages, etc.), such as those that are managed by a version control system. The versions of the source code objects can reflect changes that are made to the source code objects over time. Associations between developers and source code object versions can be analyzed and used to train the machine learning model. Patterns of similar changes to various source code objects can be detected and can also be used to train the machine learning model. When an issue is detected in a version of a source code object, the model can be used to identify a developer to assign to the issue. Feedback data regarding the developer assignment can be used to re-train the model.

Abstract: Embodiments may be associated with database access. In some embodiments, a machine learning linear regression training platform determines a set of database properties (e.g., direct and/or indirect properties such as a document type, a type of process, a number of items, etc.) associated with Online Transaction Processing (“OLTP”) database table access. The machine learning linear regression training platform may then train a linear regression model based on the set of database properties and prior locking information (e.g., locking and unlocking timestamps) that represent access to the OLTP database table. Information about the linear regression model may be output to a wait time estimation platform that adapted to use the linear regression model to generate, in substantially real-time, an estimated wait time for an OLTP database table access based on the database properties of the OLTP database table access.

Abstract: Data is received at a source system and characterizing a modified first software object for transport to a destination system via a request and to update a first software object deployed on the destination system. A first dependency of the modified first software object on a second software object is determined by the source system. An inconsistency between the modified first software object and the second software object is identified by the source system and using the first dependency. Data indicative of the inconsistency is provided. Related apparatus, systems, techniques and articles are also described.

Abstract: Data is received at a source system and characterizing a modified first software object for transport to a destination system via a request and to update a first software object deployed on the destination system. A first dependency of the modified first software object on a second software object is determined by the source system. An inconsistency between the modified first software object and the second software object is identified by the source system and using the first dependency. Data indicative of the inconsistency is provided. Related apparatus, systems, techniques and articles are also described.

Abstract: Techniques and solutions are described for facilitating the conversion of tables, having primary data and an append specifying a format of append data, from a first format to a second format. A runtime object and a data dictionary entry can be created for a primary table in the second format, and at least a portion of primary data transferred to the primary table in the second format. A database layer object corresponding to the first table in the first format can be associated with a temporary runtime object and a temporary data dictionary entry so that append data can be transferred to an append table in the second format. Operations directed to the primary table in the second format can be modified to also be directed to the append table, such as using a JOIN view.

Abstract: Techniques and solutions are described for facilitating the conversion of tables, having primary data and an append specifying a format of append data, from a first format to a second format. A runtime object and a data dictionary entry can be created for a primary table in the second format, and at least a portion of primary data transferred to the primary table in the second format. A database layer object corresponding to the first table in the first format can be associated with a temporary runtime object and a temporary data dictionary entry so that append data can be transferred to an append table in the second format. Operations directed to the primary table in the second format can be modified to also be directed to the append table, such as using a JOIN view.