Accenture Global Solutions Limited

Accenture Global Services Gmbh

Priya Athreyee

Rajendra Tanniru Prasad

Koustuv Jana

Bhaskar Ghosh

Koushik M Vijayaraghavan

Vijayaraghavan Koushik

Pradeep Senapati

Kamakshi Girish

Divya Sampathkumar

Avikar Banik

Aditi Kulkarni

Mohan Sekhar

Rajesh Nagarajan

Lakshmi Narasimhan

Arpan Shukla

Senthil Jeyachandran

Sandeep Rathod

Chetan Belgur

Jenice J Mukkada

Somen Roy

Arulmozhi Dharmar

Siddharth Mehta

Dibyendu Chattopadhyay

Nivedita Shah

Jenice James Mukkada

Reshma Abhijit Gurjar

Amaresh Swain

A device may receive requirement data identifying a requirement for modification of software code, and may process the requirement data, with a machine learning model, to identify entities and intents in the software code and to generate a query. The device may process the query, with a code locator model, to encode text of the query into high-dimensional vectors and to identify a semantic similarity between the high-dimensional vectors and code text. The device may process the query, the semantic similarity, and the code text, with a code developer model, to generate metadata, and may utilize the metadata to identify an identifier associated with the software code. The device may determine, utilizing the identifier, a portion of the software code, and may modify the portion of the software code based on the query to generate modified software code. The device may perform actions based on the modified software code.

Utilizing machine learning models for automated software code modification

A device may receive group information, associated with performing application maintenance, that includes information corresponding to one or more resources, associated with performing the application maintenance, and information associated with one or more shifts of the one or more resources. The device may receive ticket information, associated with performing the application maintenance, that includes priority information associated with one or more ticket types associated with performing the application maintenance. The device may simulate, based on the group information and the ticket information, performing the application maintenance to determine a simulation result. The simulation result may include information associated with one or more predicted performance metrics associated with performing the application maintenance. The device may provide the information associated with the one or more predicted performance metrics.

Simulation queuing based system for analyzing client or application related changes to an application maintenance project

A computer-implemented method and a system parse and aggregate unstructured data objects. The method includes obtaining the unstructured data objects from description fields of records in a database comprising client tickets that are created for application maintenance, transforming the obtained unstructured data objects to create transformed data objects, identifying a number of criteria attributes of the client tickets, where the number of criteria attributes are determined according to at least part of the transformed data objects, word importance, word sentiment, a user input, or client ticket priorities, or any combinations, and generating a plurality of ticket reports by aggregating the client tickets according to ranking orders of numbers of tickets for the client tickets and the identified criteria attributes.

Method and system for parsing and aggregating unstructured data objects

A device may obtain process data relating to a set of processes. The device may process the process data to generate a process analysis model. The device may classify, using the process analysis model, a particular process into a particular class of a set of classes of the process analysis model. The device may automatically assess the particular process based on the particular class, wherein a set of assessment parameters for assessing the particular process is selected based on the particular class, and wherein an assessment score is assigned to the particular process based on values for the set of assessment parameters. The device may determine, based on the assessment score and the particular class, an automation recommendation for the particular process. The device may automatically complete the particular process using a particular tool based on determining the automation recommendation.

Automated process analysis and automation implementation

A device may identify a ticket associated with an issue, a user associated with resolving the issue, and a particular business process including a particular business process step associated with the issue. The device may present information regarding a particular business process workflow, corresponding to the particular business process, associated with a business process steps. The device may identify the particular business process step from the business process steps, and may identify a particular technical solution corresponding to the particular business process step. The device may present information regarding a particular technical solution workflow, corresponding to the particular technical solution, associated with technical solution steps. The device may identify a particular technical solution step, from the technical solution steps, associated with resolving the issue. The device may update a resolution history based on identifying the particular business process, the particular business process step, and the particular technical solution step.

Ticket solver system

A device may receive ticket information associated with one or more service tickets. The ticket information may include a ticket category and ticket data associated with the one or more service tickets. The ticket data may be associated with the ticket category. The ticket data may describe information related to resolving the one or more service tickets. The device may identify, based on the ticket category or the ticket data, an association between the ticket category and an analysis category. The analysis category may be used to analyze the ticket information. The device may generate categorized ticket information based on the analysis category and the ticket information. The categorized ticket information may include the ticket data, and the ticket data may be associated with the analysis category. The device may provide the categorized ticket information.

Service ticket analysis using an analytics device

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Chennai, India

Priya Athreyee