Sap Se

Vipul Khullar

Kirti Sinha

Prabal Mahanta

Akansha Tiwari

Soma Shekara Pavan Kumar Marla

Ayush Kumar

Shreya Sinha

Naresh Pidugu

Systems and methods include acquisition of an asynchronous message from a message producer, the asynchronous message associated with a message consumer, determination that the asynchronous message matches a stored message, identification, in response to determining that the asynchronous message matches a stored message, of a stored error message associated with the stored message, and return of a return message based on the stored error message to the message producer.

Intelligent error detection for asynchronous communication

In an implementation, a computer-implemented method, includes collecting, as collected integration flows (iFlows), published iFlows. Descriptions of the collected iFlows are extracted as extracted descriptions and the extracted descriptions are parsed. A list of one or more interchangeable operators is created. The collected iFlows are iterated through. Automated performance recommendations for a new iFlow are provided.

Static analysis for cloud integration flows using machine learning

Transaction records are obtained and respective volumes of the transaction records during a plurality of intervals are determined based on timestamps. A spread of a distribution of the respective volume is determined. Whether the respective volumes exceeds a predetermined volume threshold is determined. Then, an integration flow is selected from among a plurality of integration flows based on the respective volumes, the spread, and whether respective volumes exceeds the predetermined volume threshold. Services and application programming interfaces corresponding to the selected integration flow are configured. Then data is replicated from the database system to a cloud platform using the configured services and application programming interfaces. A portal application is provided to present the data on the cloud platform.

Intelligent cloud portal integration

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.

Automated application program interface testing

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.

Multi-layer extensibility for enterprise portal

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.

Dynamic recommendations for resolving static code issues

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.

On-line transaction processing database wait time estimation using linear regression model

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.

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New Delhi, India

Vipul Khullar