Sap Se

Dinesh Bhandarkar

Krishnan Harihara Subramanian

Jerin James

Shuva Ranjan Sen Sharma

Madhu Tirumalasetti

Catherine Sneha P

Biju V Kalleppilli

Nagashree S

Arpitha Shirdish

Sandeep Joy

Technologies are described for automated payload testing of open data protocol (OData) application programming interfaces (APIs). For example, metadata describing an OData service can be retrieved from the OData service. The OData service can then be tested for one or more entities of the OData service. Testing can be performed by dynamically creating various payloads for testing the entities of the OData service and the OData operations supported by the entities. Results of executing the payloads can be received and evaluated to determine whether a given payload was executed successfully by the OData service. Results of the testing (e.g., indications of success or failure) can be output.

Automated dynamic payload testing of OData APIs

A testing scenario (forming part of a computing environment executing a plurality of applications) is initiated to characterize performance of the applications. During the execution of the testing scenario, various performance metrics associated with the applications are monitored. Thereafter, data characterizing the performance metrics is provided (e.g., displayed, loaded into memory, stored on disk, transmitted to a remote computing system, etc.). The testing scenario is generated by monitoring service calls being executed by each of a plurality of automates across the applications, generating a service request tree based on the monitored service calls for all of the applications, and removing cyclic dependencies in the service request tree such that reusable testing components are only used once. Related apparatus, systems, techniques and articles are also described.

Open data protocol performance test automation intelligence (OPT-AI)

According to some embodiments, systems and methods are provided, comprising providing, via a request module, a user interface, wherein the user interface displays at least one request and a status for each request; receiving selection of one of the displayed requests at a unification layer, via the user interface, wherein the unification layer is provided by the request module; accessing, by the request module, at least one of a first application and a second application based on the received selection; and generating a visualization displayed on the user interface, wherein the visualization includes the at least one request and status for each request, the selected at least one request and a request indicator. Numerous other aspects are provided.

Harmonized multi-column flexi user interface

Techniques and solutions are described for training and using a classifier based on a machine learning model to analyze performance information to assist in correcting a software bug or performance issue. The performance information can be processed prior to submission to a trained classifier, such as to remove, modify, or format data. A classification result provided by the classifier can be compared with a database to determine whether a solution or target is associated with the classification result. User feedback can be used to provide more accurate suggestions of solutions or targets, as well as to improve the accuracy of the classifier.

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Bengaluru, India

Dinesh Bhandarkar