Abstract: Data flow analysis is provided. A program level data flow analysis is performed for each respective data flow path in a set of control flow chains corresponding to each respective program called by a particular job step in each respective job of a plurality of jobs in a sequence of job execution corresponding to an application. A particular field of a plurality of fields is identified in a record of each dataset of a plurality of datasets corresponding to the application that is included in a particular data flow path to form a field level data flow analysis for each particular data flow path. Results of the field level data flow analysis for each particular data flow path in the set of control flow chains corresponding to each respective program called by the particular job step in each respective job of the plurality of jobs is aggregated.

Abstract: One or more application programming interfaces to be implemented for an application are automatically identified. The automatically identifying includes determining within the application one or more artifacts to be implemented as the one or more application programming interfaces. For an artifact of the one or more artifacts, one or more requests of the artifact are determined. The one or more requests are part of an application programming interface signature for an application programming interface identified based on the artifact. For an application programming interface identified based on the artifact, one or more definitions are generated. The one or more definitions include one or more definitions for the one or more requests of the artifact.

Abstract: Data flow analysis is provided. A program level data flow analysis is performed for each respective data flow path in a set of control flow chains corresponding to each respective program called by a particular job step in each respective job of a plurality of jobs in a sequence of job execution corresponding to an application. A particular field of a plurality of fields is identified in a record of each dataset of a plurality of datasets corresponding to the application that is included in a particular data flow path to form a field level data flow analysis for each particular data flow path. Results of the field level data flow analysis for each particular data flow path in the set of control flow chains corresponding to each respective program called by the particular job step in each respective job of the plurality of jobs is aggregated.

Abstract: A computer-implemented method, according to one embodiment, includes: identifying statements in a software program. Values that satisfy path constraints of a given one of the statements are developed for ones of the identified statements that semantics information is not yet known. Outputs are produced by executing the given one of the statements using the generated values as inputs. Moreover, semantics information corresponding to the given one of the statements is determined by evaluating the generated values and corresponding outputs using a machine learning model. The semantics information is further used to generate a symbolic representation of the given one of the statements.

Abstract: One or more application programming interfaces to be implemented for an application are automatically identified. The automatically identifying includes determining within the application one or more artifacts to be implemented as the one or more application programming interfaces. For an artifact of the one or more artifacts, one or more requests of the artifact are determined. The one or more requests are part of an application programming interface signature for an application programming interface identified based on the artifact. For an application programming interface identified based on the artifact, one or more definitions are generated. The one or more definitions include one or more definitions for the one or more requests of the artifact.

Abstract: One embodiment provides a method, including: receiving information describing an application to be split into a plurality of microservices; identifying, utilizing a microservices advisor application, application elements of the application; generating, utilizing the microservices advisor application and from the application elements, a heterogenous graph, wherein each node within the heterogenous graph represents an application element and wherein each edge within the heterogenous graph represents a relationship between two nodes connected by the edge; identifying, based upon user input identifying preferences of relationships between nodes, groups of nodes within the heterogenous graph sharing a common attribute; and providing, from the microservices advisor application, a recommendation, based upon the identified groups of nodes, for splitting the application into microservices, wherein the recommendation includes a number of microservices for the application and application elements that should be included

Abstract: By analyzing transaction data of an executing application, an application graph is constructed, the application graph comprising a plurality of nodes and a plurality of edges connecting pairs of the plurality of nodes, a node in the application graph corresponding to a module of the application. The plurality of nodes is clustered into a set of clusters. Formation of a cluster in the set of clusters is analyzed, the analyzing identifying a central node of the cluster, a feature importance in placing a node into the cluster, and an edge importance in placing the node into the cluster. Responsive to a confidence value in the cluster being above a threshold confidence value, using the central node of the cluster, the application is refactored into a set of microservices, the cluster corresponding to a microservice, the central node of the cluster corresponding to a component of the microservice.

Abstract: One embodiment provides a method, including: receiving information describing an application to be split into a plurality of microservices; identifying, utilizing a microservices advisor application, application elements of the application; generating, utilizing the microservices advisor application and from the application elements, a heterogenous graph, wherein each node within the heterogenous graph represents an application element and wherein each edge within the heterogenous graph represents a relationship between two nodes connected by the edge; identifying, based upon user input identifying preferences of relationships between nodes, groups of nodes within the heterogenous graph sharing a common attribute; and providing, from the microservices advisor application, a recommendation, based upon the identified groups of nodes, for splitting the application into microservices, wherein the recommendation includes a number of microservices for the application and application elements that should be included