International Business Machines Corporation

Srikanth Govindaraj Tamilselvam

Amrita Saha

Pankaj Satyanarayan Dayama

Priyanka Agrawal

Senthil Kumar Kumarasamy Mani

Biplav Srivastava

Kuntal Dey

Seema Nagar

Amith Singhee

Enara C Vijil

Anush Sankaran

Utkarsh Milind Desai

Prithu Banerjee

William Graham O'Keeffe

Shivali Agarwal

Naveen Panwar

Shreya Khare

Sameep Mehta

David Deidou Taieb

Anshu Narendra Jain

Rahul Aralikatte

Sai Koti Reddy Danda

Ghulam Ahmed Ansari

Vijay Ekambaram

Ramasuri Narayanam

Ashok Pon Kumar Sree Prakash

James Randal Moulic

Anuradha Bhamidipaty

Daniela Rosu

Mithkal M Smadi

Manikandan Padmanaban

Giriprasad Sridhara

Aliza Rivka Heching

Terrence E White

Vikas Raykar

Sambaran Bandyopadhyay

Ankur Jain

Tarun Tater

Jia Liu

Karthikeyan Dakshinamurthy

Winnie Wing-Yee Cheng

Alex Mathai

Sumana S Bhat

Karen L B Brown

Bipin K Joshi

Sarbajit Kumar Rakshit

Yu Deng

Amit Anil Nanavati

Scott N Gerard

Krishna Kummamuru

Kalapriya Kannan

John Michael Boyer

Himanshu Gupta

Akshar Kaul

John P Bufe, Iii

Sampath Dechu

Gyana Ranjan Parija

Arvind Agarwal

Dinesh Garg

Vikas V Joshi

Arun Kumar

Deepak S Padmanabhan

Parag Jain

Neelamadhav Gantayat

Dan O'Connor

Ayushi Dalmia

Vaibhav S Dantale

Vini Kanvar

Abhirut Gupta

Radha Bellamkonda

Divakar R Mysore

Raunak Sinha

Tanmayee Narendra

Ridhi Jain

Dasari Surya Sai Venkatesh

Gopal Bhageria

Radhika Vaddarse

Ravi Vishnu Israni

Gopalakrishnan Sankararamasubramanian

Rahul D Sharnagat

An approach to real-time multi-agent visual question answering in a decentralized environment. The approach can include initializing a plurality of agents based on data associated with an image and a question about the image. The approach can include identifying a first portion of the plurality of agents to construct a grid of agents based on similarity between the first portion of agents. The approach can include determining a second portion of agents of the first portion of agents with edges between agents allowing sharing of agent concepts and agent parameters. The approach can include generate training trajectories of the grid of agents based on executing a collaborative rollout. Additionally, the approach can include optimizing agent policies of the grid of agents.

Decentralized online multi-agent visual question answering

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.

Automated generation of application programming interfaces for microservices

A computer implemented method creates microservices for an application. A number of processor units clusters programs and data structures for the application using runtime metadata to form groups of the programs and data structures. The runtime metadata is obtained from running the application. The number of processor units creates a design for the microservices for the application using the groups of the programs and the data structures.

Microservice creation using runtime metadata

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.

Factchecking artificial intelligence models using blockchain

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.

Monolith-to-microservice refactoring via source-code-to-domain-model graph comparison

Methods, apparatus, and processor-readable storage media for facilitating a secure platform for point-to-point brain sensing are provided herein. An exemplary method includes presenting a user of a brain-computer interface with learning content; monitoring brain signals of the user using a brain-computer interface while the learning content is being presented; determining, based at least in part on said monitoring, that a confusion state of the user exceeds a personalized confusion threshold in regard to at least a part of the learning content; in response to said determining, outputting information to assist the user in understanding the part of the learning content until the confusion state of the user is below the personalized confusion threshold.

Secure platform for point-to-point brain sensing

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.

Automatically recommending database access patterns for transactions in microservices

A method, computer program product, and a system where a processor(s) generates a digital wardrobe for each user of a set of users. The processor(s) obtains a prospective theme(s) for a given event and a list of participants comprising a portion of the set of users. The processor(s) identify preferences, in the digital wardrobes of the portion of the set of users, relevant to each of the one or more prospective themes for the given event. The processor(s) determine, based on analyzing the relevant preferences in the digital wardrobes of the portion of the set of users, if a consensus exists in the relevant preferences of the portion of the set of users, where the consensus represents a given prospective theme, where the respective relevant preferences for the given prospective theme are aligned across the portion of the set of users. Based on determining that the consensus exists, the processor(s) generates an event theme for the given event, wherein the event theme is the consensus.

Optimized automatic consensus determination for events

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.

Microservices recommendation framework

Methods, systems, and computer program products for constraints-based refactoring of monolith applications through attributed graph embeddings are provided herein. A computer-implemented method includes performing an analysis of source code of a monolith application to identify structural features and behavioral features; generating a graph structure representing the monolith application based on the identified structural features and the identified behavioral features, wherein the graph structure comprises: a plurality of nodes, each node corresponding to one of a plurality of software modules of the monolith application, and a plurality of edges between the plurality nodes, each edge representing a relationship of the corresponding nodes; automatically identifying constraints associated with the plurality of nodes in the graph structure based on the analysis of the source code; clustering the nodes using a convolutional neural network based the constraints; and outputting candidate microservices corresponding to respective ones of the clusters for refactoring the monolith application.

Constraints-based refactoring of monolith applications through attributed graph embeddings

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 within each of the microservices.

Heterogeneous graph generation for application microservices

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.

Application refactoring with explainability

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.

Identifying microservices for a monolith application through static code analysis

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.

Transforming an application into a microservice architecture

Methods, systems, and computer program products for translating clusters of a monolith application to microservices are provided herein. A computer-implemented method includes obtaining a plurality of candidate microservices for refactoring a monolith application, each candidate microservice including one or more of a plurality of software classes of the monolith application; analyzing the monolith application to identify one or more software functions in the plurality of software classes to expose as one or more application programming interfaces; automatically isolating non-overlapping software functions of the monolith application between the plurality of candidate microservices; converting source code corresponding to the identified one or more software functions into a standardized application programming interface format based at least in part on the isolating; and using the converted source code to generate a plurality of deployable microservices corresponding to the plurality of candidate microservices.

Translating clusters of a monolith application to microservices

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Chennai, India

Srikanth Govindaraj Tamilselvam