Abstract: Recommending database access patterns for transactions in microservices is provided. A recommendation of a particular database access pattern to improve access by transactions to data in fields of tables in a database corresponding to a microservice is generated based on analysis of field access graphs for the tables accessed by the transactions. The recommendation of the particular database access pattern is output. The particular database access pattern is implemented to improve access by the transactions to the data in the fields of the tables in the database corresponding to the microservice to improve performance of the microservice based on user input and read view implementation guidance.

Abstract: Systems/techniques that facilitate monolith-to-microservice refactoring via source-code-to-domain-model graph comparison are provided. In various embodiments, a system can access source code of a monolithic application and a target domain model corresponding to the monolithic application. In various aspects, the system can refactor the monolithic application into microservices, by aligning a first graph representing the source code with a second graph representing the target domain model.

Abstract: Techniques for recommending microservices to perform the different functions of a legacy architecture are disclosed. In one example, a computer implemented method comprises receiving a plurality of recommendations comprising a plurality of program components as candidates for assignment to a plurality of microservices, and determining roles of respective ones of the plurality of program components. A user interface is provided which is configured to allow a user to modify one or more of the plurality of recommendations based at least in part on the roles. Modifications to the one or more of the plurality of recommendations are analyzed, and one or more metrics are computed based at least in part on the analysis.

Abstract: Described are techniques for exposing an endpoint for a software function used by microservices. The techniques include obtaining software classes of a plurality of microservices and analyzing the software classes to identify a software function that is eligible for conversion to an application programming interface (API) format. The techniques further include determining a server microservice, which hosts a software class that includes the software function, and one or more client microservices that use the software function. The techniques further include generating deployable source code implementing an API that exposes the software function hosted by the server microservice to the one or more client microservices. The API uses a software wrapper to encapsulate software function semantics used by a monolith application refactored into the plurality of microservices.

Abstract: Methods, systems, and computer program products for constraints-based refactoring of monolith applications through attributed graph embeddings are provided herein.

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: Recommending database access patterns for transactions in microservices is provided. A recommendation of a particular database access pattern to improve access by transactions to data in fields of tables in a database corresponding to a microservice is generated based on analysis of field access graphs for the tables accessed by the transactions. The recommendation of the particular database access pattern is output. The particular database access pattern is implemented to improve access by the transactions to the data in the fields of the tables in the database corresponding to the microservice to improve performance of the microservice based on user input and read view implementation guidance.

Abstract: Methods, systems, and computer program products for identifying microservices from a monolith application through static code analysis are provided herein. A method includes performing a static code analysis to extract multiple features of a monolith application; partitioning code elements of the monolith application into multiple groups using an agglomerative clustering process, wherein the agglomerative clustering process is based on the extracted multiple features and a set of clustering metrics; obtaining at least one weight corresponding to one or more of: at least one of the multiple features and at least one of the multiple groups; adjusting the groups based on the at least one weight; generating a list of candidate microservices for the monolith application, wherein each candidate microservice in the list corresponds to a different one of the adjusted multiple groups; and outputting the list of candidate microservices to at least one of a system and a user.

Abstract: According to one embodiment of the present invention, a system transforms an application for a distributed computing environment. The system comprises one or more memories, and at least one processor coupled to the one or more memories. The system analyzes a description of user intent to extract information for transforming the application. The extracted information indicates functionalities for the distributed computing environment. A plurality of software artifacts of the application are mapped to the functionalities. The plurality of software artifacts form different groups of software artifacts. Remaining software artifacts of the application are assigned into the different groups based on a remaining software artifact corresponding to a mapped software artifact of a group. The different groups correspond to microservices for the distributed computing environment. The microservices for the distributed computing environment are presented based on the different groups.

Abstract: Methods, systems, and computer program products for translating clusters of a monolith application to microservices are provided herein.

Abstract: A method, computer system, and a computer program product for debugging a deep neural network is provided. The present invention may include identifying, automatically, one or more debug layers associated with a deep learning (DL) model design/code, wherein the identified one or more debug layers include one or more errors, wherein a reverse operation is introduced for the identified one or more debug layers. The present invention may then include presenting, to a user, a debug output based on at least one break condition, wherein in response to determining the at least one break condition is satisfied, triggering the debug output to be presented to the user, wherein the presented debug output includes a fix for the identified one or more debug layers in the DL model design/code and at least one actionable insight.

Abstract: Techniques for recommending microservices to perform the different functions of a legacy architecture are disclosed. In one example, a computer implemented method comprises receiving a plurality of recommendations comprising a plurality of program components as candidates for assignment to a plurality of microservices, and determining roles of respective ones of the plurality of program components. A user interface is provided which is configured to allow a user to modify one or more of the plurality of recommendations based at least in part on the roles. Modifications to the one or more of the plurality of recommendations are analyzed, and one or more metrics are computed based at least in part on the analysis.

Abstract: Methods, systems, and computer program products for multi-objective driven refactoring of a monolith application using reinforcement learning are provided herein. A computer-implemented method includes obtaining multiple code modules of a monolith application and a plurality of conflicting metrics for determining a set of microservices for the monolith application; performing a reinforcement learning-based clustering process that iteratively generates a plurality of clusters comprising the code modules based at least in part on feedback provided for the plurality of conflicting metrics at each iteration; generating candidate microservices for the monolith application, wherein each candidate microservice corresponds to a different one of the plurality of clusters; and outputting the generated candidate microservices to at least one of a system and a user.

Abstract: Methods, systems, and computer program products for identifying microservices from a monolith application through static code analysis are provided herein. A method includes performing a static code analysis to extract multiple features of a monolith application; partitioning code elements of the monolith application into multiple groups using an agglomerative clustering process, wherein the agglomerative clustering process is based on the extracted multiple features and a set of clustering metrics; obtaining at least one weight corresponding to one or more of: at least one of the multiple features and at least one of the multiple groups; adjusting the groups based on the at least one weight; generating a list of candidate microservices for the monolith application, wherein each candidate microservice in the list corresponds to a different one of the adjusted multiple groups; and outputting the list of candidate microservices to at least one of a system and a user.

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: Methods, systems, and computer program products for translating clusters of a monolith application to microservices are provided herein.

Abstract: Methods, systems, and computer program products for constraints-based refactoring of monolith applications through attributed graph embeddings are provided herein.

Abstract: One embodiment provides a method, including: mining a plurality of deep-learning models from a plurality of input sources; extracting information from each of the deep-learning models, by parsing at least one of (i) code corresponding to the deep-learning model and (ii) text corresponding to the deep-learning model; identifying, for each of the deep-learning models, operators that perform operations within the deep-learning model; producing, for each of the deep-learning models and from (i) the extracted information and (ii) the identified operators, an ontology comprising terms and features of the deep-learning model, wherein the producing comprises populating a pre-defined ontology format with features of each deep-learning model; and generating a deep-learning model catalog comprising the plurality of deep-learning models, wherein the catalog comprises, for each of the deep-learning models, the ontology corresponding to the deep-learning model.

Abstract: Methods, systems, and computer program products for factchecking artificial intelligence models using blockchain are provided herein. A computer-implemented method includes obtaining at least one artificial intelligence model and at least one set of data related to the at least one artificial intelligence model; determining a set of characteristics based at least in part on the at least one artificial intelligence model and the at least one set of data; selecting one of a plurality of networks based at least in part on a target deployment of the at least one artificial intelligence model to verify the set of characteristics; generating a report based at least in part on verifying the set of characteristics using the selected network, wherein the report establishes a threshold level of trust for the at least one artificial intelligence model; and storing the report on a blockchain.