Accenture Global Solutions Limited

Ravi Kiran Velama

Vikrant Shyamkant Kaulgud

Sanjay Mittal

Vibhu Saujanya Sharma

Janardan Misra

Adam Patten Burden

Kuntal Dey

Sanjay Podder

Kapil Singi

Samarth Sikand

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, simulation-based software design and delivery attribute tradeoff identification and resolution may include receiving requirements specification, and generating, based on an analysis of the requirements specification, canonical sustainability requirements. Based on an analysis of the canonical sustainability requirements, sustainable software attribute decisions and an attribute optimization score may be generated, and used to generate a sustainable software attribute balance score and a tradeoff attributes list. Based on an analysis of the sustainable software attribute balance score and the tradeoff attributes list, a green quotient may be generated and used to generate an architecture document. Further, based on an analysis of the architecture document, software quality rules may be generated, and used to generate a software application.

Simulation-based software design and delivery attribute tradeoff identification and resolution

In some examples, collaborative learning-based cloud migration implementation may include identifying a migration agent that is to perform an application migration from a first cloud environment to a second cloud environment, and identifying a plurality of additional migration agents. A technical context and a migration flow context may be determined for the migration agent and for the plurality of additional migration agents. Executed allowed and error-response migration actions may be identified for states that are similar to a current state of the application migration, and a similarity between the migration agent and each of the migration agents that executed the allowed and error-response migration actions may be determined. A migration action that is to be performed may be identified based on a maximum relevance associated with the allowed and error-response migration actions. The identified migration action may be executed by the migration agent to perform the application migration.

Collaborative learning-based cloud migration implementation

In some examples, migration context and flow graph based migration control may include ascertaining an application that is to be migrated from a physical environment to a cloud environment, and determining a migration issue associated with the migration of the application. Migration context and flow graph based migration control may further include identifying, from a historical issue database, a plurality of historical issues, determining, for the migration issue and the plurality of historical issues, unified proximities, sorting, based on the determined unified proximities, the historical issues, selecting, from the sorted historical issues, a topmost historical issue, and determining, from the topmost historical issue, a resolution associated with the topmost historical issue. Further, migration context and flow graph based migration control may include executing the resolution to resolve the migration issue, and performing, based on the resolved migration issue, migration of the application from the physical environment to the cloud environment.

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Bengaluru, India

Ravi Kiran Velama