Abstract: A computer-implemented method can receive a software update for a software application deployable on a target system and perform an automatic performance test of the software application incorporating the software update on the target system. The automatic performance test can be integrated in a continuous integration and continuous delivery (CI/CD) pipeline. The method can collect a plurality of performance metrics measured when performing the automatic performance test and determine a performance score based on the plurality of performance metrics. Responsive to detecting the performance score is below a predefined performance threshold, the method can cause the CI/CD pipeline to reject the software update to the software application.

Abstract: Requests are received for handling by a cloud computing environment which are then executed by the cloud computing environment. While each request is executing, performance metrics associated with the request are monitored. A vector is subsequently generated that encapsulates information associated with the request including the text within the request and the corresponding monitored performance metrics. Each request is then assigned (after it has been executed) to either a normal request cluster or an abnormal request cluster based on which cluster has a nearest mean relative to the corresponding vector. In addition, data can be provided that characterizes requests assigned to the abnormal request cluster. Related apparatus, systems, techniques and articles are also described.

Abstract: A query is received that requests issues relevant to a user. Thereafter, a plurality of issues responsive to the query are retrieved. The retrieved issues are ranked using a first machine learning model to result in a first subset of the retrieved issues. The first subset of the retrieved issues are then ranked using a second, different machine learning model to result in a second subset of the retrieved issues which are a subset of the first subset of the retrieved issues. Data can then be provided which is responsive the query and includes at least a portion of the second subset of the retrieved issues. Related apparatus, systems, techniques and articles are also described.

Abstract: The present disclosure provides systems and methods for generating a graph of microservices of a software application. Source code for the application may be parsed using one or more method identifiers to identify a plurality of methods of the software application. Names and path values for the plurality of methods may be stored in a graph database as nodes and edges. The graph database may be queried to determine dependencies of a method, including the microservice that the method belongs to any remote methods called by the method. In addition, properties of the nodes may be transformed into a set of vectors to provide searching of the graph and recommendations.

Abstract: The present disclosure provides systems and methods for generating a graph of microservices of a software application. Source code for the application may be parsed using one or more method identifiers to identify a plurality of methods of the software application. Names and path values for the plurality of methods may be stored in a graph database as nodes and edges. The graph database may be queried to determine dependencies of a method, including the microservice that the method belongs to any remote methods called by the method. In addition, properties of the nodes may be transformed into a set of vectors to provide searching of the graph and recommendations.

Abstract: Requests are received for handling by a cloud computing environment which are then executed by the cloud computing environment. While each request is executing, performance metrics associated with the request are monitored. A vector is subsequently generated that encapsulates information associated with the request including the text within the request and the corresponding monitored performance metrics. Each request is then assigned (after it has been executed) to either a normal request cluster or an abnormal request cluster based on which cluster has a nearest mean relative to the corresponding vector. In addition, data can be provided that characterizes requests assigned to the abnormal request cluster. Related apparatus, systems, techniques and articles are also described.

Abstract: Supervised learning creates and trains a model to predict resource consumption by a remote system. Historical time-series data (e.g., monitor logs of CPU consumption, memory consumption) are collected from systems called upon to perform a task. This raw data is transformed into a labeled data set ready for supervised learning. Using the labeled data set, a model is constructed to correlate the input data with a resulting load. The constructed model may be a Sequence to Sequence (Seq2Seq) model based upon Gated Recurrent Units of a Recurrent Neural Network. After training, the model is saved for re-use to predict future load based upon an existing input. For example, the existing input may be data from a most recent 24 hour period (hour0-hour23), and the output of the model may be the load predicted for the next 24 hour period (hour24-hour47). This prediction promotes efficient reservation remote server resources.

Abstract: A query is received that requests issues relevant to a user. Thereafter, a plurality of issues responsive to the query are retrieved. The retrieved issues are ranked using a first machine learning model to result in a first subset of the retrieved issues. The first subset of the retrieved issues are then ranked using a second, different machine learning model to result in a second subset of the retrieved issues which are a subset of the first subset of the retrieved issues. Data can then be provided which is responsive the query and includes at least a portion of the second subset of the retrieved issues. Related apparatus, systems, techniques and articles are also described.