Accenture Global Solutions Limited

Accenture Global Services Gmbh

Kapil Singi

Vikrant Shyamkant Kaulgud

Sanjay Podder

Vibhu Saujanya Sharma

Adam Patten Burden

Swapnajeet Gon Choudhury

Kuntal Dey

Amitabh Saxena

Dipin Era

Janardan Misra

Kishore P Durg

Alpana A Dubey

Pradeepkumar Duraisamy Soundrapandian

Shubhashis Sengupta

Anurag Dwarakanath

Swati Sharma

Divya Rawat

Anitha Chandran

Samarth Sikand

Sanjay Mittal

Sankar Narayan Das

Ravi Kiran Velama

Chethana Dinakar

Andrew Francis Hickl

Rambhau Eknath Rote

David E Ingram

Jagadeesh Chandra Bose Rantham Prabhakara

Kanchanjot Kaur Phokela

Afsal Marattil

Brian Ahern

Jagadeesh Chandra Bose R P

Raghunandan Hemmige Keshava

A retraining monitoring system maintains the sustainability of a production machine learning (ML) model system that includes a production ML model retraining platform. The retraining monitoring system collects contextual data from the production ML model system and determines if one or more of a currently-selected architectural options has to be changed for sustainability. An architectural option of the production ML model retraining platform, such as, a processing location is selected from a cloud retraining platform or an on-premises retraining platform by a selection process based on a multi-armed bandit problem. An evaluation of the retraining architecture is dealt with as a reinforcement learning problem to implement one of a periodic retraining architecture or a reactive retraining architecture.

Sustainable retraining for production machine learning

A method and system for assisting program code development are disclosed. The method may include obtaining multimodal data of a meeting discussing a program code development, extracting a plurality of topics and a plurality of concepts from the multimodal data, identifying a plurality of meeting segments for the plurality of concepts. The method may further include determining a coding intent from program codes for the program code development, aligning the coding intent to a set of topics, identifying the set of concepts associated with the topic aligned with the coding intent, identifying a set of meeting segments associated with the concept. The method may further include determining an alignment metric of the meeting segment based on an alignment metric between the coding intent and the topic, and outputting one or more meeting segments for the coding intent based on alignment metrics of the one or more meeting segments.

Assisting program code development by processing multimodal data of a meeting

Disclosed herein is a system and method for generating computer code for a plurality of components of a software development project. An artificial intelligence code generator can generate computer code in response to a natural language text input describing a component of the software development project. A first database can store natural language text describing components of the software development project. A second database can store computer code generated at least partially by the artificial intelligence code generator and defining components of the software development project and corresponding to the natural language text stored in the first database. Using a pre-trained language model, the system can generate a natural language summary text based on the code intent of the first component, the identified characteristics of the objects, and the natural language text retrieved from the first database.

Computer code generator

In some examples, collective application portfolio migration control may include determining, for a plurality of applications that are to be clustered for migration to a cloud environment, a coupling coefficient that represents a type of coupling between pairs of applications from the plurality of applications, a proximity coefficient that represents application proximities, and a connectedness coefficient that represents application connections. A combined application relatedness coefficient may be determined based on the coupling coefficient, the proximity coefficient, and the connectedness coefficient. A portfolio graph may be generated based on the combined application relatedness coefficient to generate migration application clusters to duster the plurality of applications. Migration of the plurality of applications to the cloud environment may be controlled based on the migration application clusters.

Collective application portfolio migration control

In some examples, policy-based application architecture generation may include generating, based on a knowledge model schema, data residency policies, and a data classification ontology, a knowledge model, and determining, based on the knowledge model, whether an application includes regulated data. Based on an analysis of application data and user data, user location and regulated data insights may be generated to determine location specific data residency policies. Location specific regulated data in-flow and data source hosted location insights may be analyzed to determine a location compliance assessment that includes an indication of whether a location associated with the application is compliant or not compliant with the location specific data residency policies. Based on an indication of non-compliance, a strategy may be generated for compliance of application architecture for the application with the location specific data residency policies, and re-architecting of the application architecture may be implemented.

Policy-based application architecture generation

A device may receive application data identifying an application to be executed by a hybrid cloud computing environment and constraint data identifying constraints associated with the hybrid cloud computing environment. The device may select objectives based on the application data and the constraint data, and may identify factors associated with the objectives. The device may correlate the factors with potential data centers, and may apply weights to the correlated factors to generate correlated factor scores. The device may identify a list of data centers based on the correlated factor scores, and may determine a reduced list of data centers. The device may apply a preference technique to the reduced list of data centers to generate a ranked list of data centers, and may determine the hybrid cloud computing environment based on the ranked list of data centers. The device may perform actions based on the hybrid cloud computing environment.

Determining a recommended hybrid cloud computing environment for an application on demand

In some examples, data classification and modelling based application compliance analysis may include generating, for application data for an application, and based on data laws, a knowledge graph schema that is used to generate an instantiated knowledge graph. Based on a plurality of data sources, domain knowledge and patterns, and the instantiated knowledge graph, classified data may be generated to generate an annotated knowledge graph. Based on the annotated knowledge graph, an indication of applicable sovereign laws and compliance related to the application data for the application may be generated. Based on the annotated knowledge graph and the applicable sovereign laws and compliance related to the application data for the application, a sovereign cloud strategy related to the application data for the application may be generated. Further, the sovereign cloud strategy may be applied to the application data for the application.

Data classification and modelling based application compliance analysis

In some possible implementations, a device may include one or more memories and one or more processors, communicatively coupled to the one or more memories, to: receive a document that includes a plurality of clauses relating to two or more entities; identify a selected clause, of the plurality of clauses, based on a characteristic of the selected clause; determine a domain-specific language (DSL) unit based on the selected clause, the DSL unit identifying the two or more entities and values associated with the selected clause, identified by the selected clause, associated with the two or more entities; automatically generate a smart contract based on the DSL unit, the smart contract being configured to enforce a condition or action, of the values, between the two or more entities; and perform an action based on the smart contract.

Rapid design, development, and reuse of blockchain environment and smart contracts

In some examples, temporal variation identification of regulatory compliance based robotic agent control may include ascertaining a temporal sequence of compliance specification text, where the temporal sequence may include time points and versions of the compliance specification text at the time points. For each time point of the temporal sequence of the compliance specification text, a compliance specification graph may be generated. Based on an analysis of each of the generated compliance specification graphs, changes in the temporal sequence of the compliance specification text may be determined. Further, an operation associated with a robotic agent may be controlled by the robotic agent and based on the determined changes in the temporal sequence of the compliance specification text.

Temporal variation identification of regulatory compliance based robotic agent control

System and methods for cryptologic digital twin segregation with blockchain for development operations are provided. By way of introductory example, a system may receive a first development object from a first devops tool and a second development object from a second devops tool. The system may identify, in a segregation model graph, a first node and a second node, the first node corresponding to the first development object and the second node corresponding to the second development object. The system may determine the first node is mapped to an off-chain storage classification and the second node is mapped to an on-chain storage classification. The system may store the first development object in an off-chain storage, generate a hash value of the first development object, and store the hash value of the first development object on a blockchain. The system may store the second development object on the blockchain.

Cryptographic digital twin segregation system for development operations

A device may obtain information identifying a base application. The device may extract a set of sub-application artifacts associated with the base application based on structural information associated with the base application. The device may define a set of metadata attributes associated with the set of sub-application artifacts associated with the base application. The device may generate a set of hash tuples for the set of metadata attributes associated with the set of sub-application artifacts associated with the base application. The device may generate a base composite identity of the base application based on the set of hash tuples. The device may store the base composite identity in a blockchain and in connection with storage of the base application in the blockchain to enable subsequent identification and verification of the base application.

Blockchain based digital identity generation and verification

A device may receive information regarding a software environment and/or one or more modules associated with the software environment; receive information regarding one or more compliance requirements; determine one or more smart contracts based on the information regarding the one or more compliance requirements and the information regarding the software environment and/or the one or more modules associated with the software environment; obtain data regarding the software environment and/or the one or more modules; determine, using the one or more smart contracts, whether the data satisfies the one or more compliance requirements; and add at least part of a block, to a blockchain, to indicate whether the data satisfies the one or more compliance requirements, for realization of a reward or penalty associated with the one or more compliance requirements.

Software assurance and trust in a distributed delivery environment

According to an example, partitioning based migration of systems to container and micro-service based-platforms may include determining, based on an analysis of source code for an application that is to be partitioned, an entity model corresponding to the application, identifying resources associated with the application, and determining a mapping of the identified resources to entities of the entity model. Further, partitioning based migration of systems to container and micro-service based-platforms may include identifying dependencies for each of the mapped resources, generating dependency and control flow metrics for the application, generating affinity values between the mapped resources, generating a resource affinity graph, determining an affinity score between each of the mapped resources, and generating resource clusters that correspond to partitions of the application.

Partitioning based migration of systems to container and microservice based platforms

A method and system generate test cases for applications that include User Graphical Interfaces (GUIs). The system may extract GUI information from prototype requirements (e.g., interactive wireframes), generate a screen model for each screen of the GUI by using a subset of extracted GUI information, generate an application model representing the prototype requirements by combining the screen model for each screen, and generate test cases by using the sequence of the GUICs and the event types included in the test path and a semantic knowledge base. The semantic knowledge base is generated by using the relationships between GUICs and the GUIC events.

Test case generation system

An application testing developer system provides a platform for generating real-time suggestions for allocating test cases to testers in a distributed environment based on monitored characteristics from previous testing of a test application. The application testing developer system includes a smart advisory tool that optimizes test case allocation in real-time, adaptively assigns incentives in real-time to test cases for prioritizing testing of certain test cases over others, and monitors and validates testing activities.

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Bengaluru, India

Kapil Singi