Accenture Global Solutions Limited

Mohan Sekhar

Bhaskar Ghosh

Rajendra Tanniru Prasad

Vijayaraghavan Koushik

Krupa Srivastava

Mark Lazarus

Arpan Shukla

Koushik M Vijayaraghavan

Rajesh Nagarajan

Chandrashekhar Arun Deshpande

Mahesh Venkataraman

Purnima Jagannathan

Kishore P Durg

Jothi Gouthaman

Radhika Golden

Roopalaxmi Manjunath

Koustuv Jana

Pradeep Senapati

Balaji Venkateswaran

Prashant Sawant

Senthil Jeyachandran

Rohan Sharma

Le G Dang

Madhusudhana Desai

Sumit Kute

Tiarenla Jamir

Jayant Swamy

Sunder Ranganathan Nochilur

Sriram Lakshminarasimhan

Lakshmi Narasimhan

Raghavan Tinniyam Iyer

Luke Higgins

Priya Athreyee

Srikanth Prasad

Prabir GhoushalKumar

Shankaranand Mallapur

Raghavendra Meharwade

Jeffson Felix Dsouza

Anubhav F Gupta

Bhavin Prafulchandra Mehta

Sarvesh Madhusudan Damle

Suja Jain

Aruna Sivakumar

Kamakshi Girish

Girish Dattatreya Kulkarni

Pratap Venkata Naga Poorna Bontha

Robin Nastri

John Hopkins

Muthalanghat Nisha

Janagi Madhankumar

Sadanand Padmawar

Achal Srivastava

Abigail Hart

Juhi A Gupta

Arulmozhi Dharmar

Somen Roy

Amaresh Swain

Anandakrishnan Rajaram

Prashant Bhadre

Srikanth Nr

Nevis Ravi Kumar Rodriguez

Syed Mohammed Yusuf

Niyaz Shaffi

Muthukumar Rengaraju

Mahesh Rajappan

A device may detect a trigger to perform a benchmarking task. The benchmarking task may include a first benchmarking of a first resource utilization associated with one or more tasks completed via an automated procedure. The benchmarking task may include a second benchmarking of a second resource utilization associated with the one or more tasks completed via a manual procedure. The device may determine project data relating to a project platform based on detecting the trigger to perform the benchmarking task. The device may process the project data relating to the project platform to benchmark the project. The device may generate a recommendation relating to completion of the one or more tasks using the automated procedure or the manual procedure. The device may communicate with one or more other devices to perform a response action based on the recommendation.

Benchmarking for automated task management

This document describes modeling and simulation techniques to select a cloud architecture profile based on correlations between application workloads and resource utilization. In some aspects, a method includes obtaining infrastructure data specifying utilization of computing resources of an existing computing system. Application workload data specifying tasks performed by one or more applications running on the existing computing system is obtained. One or more models are generated based on the infrastructure data and the application workload data. The model(s) define an impact on utilization of each computing resource in response to changes in workloads of the application(s). A workload is simulated, using the model(s), on a candidate cloud architecture profile that specifies a set of computing resources. A simulated utilization of each computing resource of the candidate cloud architecture profile is determined based on the simulation. An updated cloud architecture profile is generated based on the simulated utilization.

Enhanced selection of cloud architecture profiles

The disclosed system and method for software testing provide a strategy for testing new software functionalities in a sequence that detects defects according to a testing strategy designed to accomplish a predetermined objective. Providing a pre-testing stage generates test execution results for test cases designed to find defects related to new functionalities. Using machine learning clustering techniques to identify which test cases are similar to one another facilitates organizing test cases in a sequence appropriate for accomplishing a predetermined objective.

Software test case sequencing

In some examples, artificial intelligence and machine learning based product development may include ascertaining an inquiry, by a user, related to a product that is to be developed or that is under development, and ascertaining an attribute associated with the user. The inquiry may be analyzed to determine at least one virtual assistant from a set of virtual assistants to respond to the inquiry. The determined at least one virtual assistant may be invoked based on an authorization by the user. Further, development of the product may be controlled based on the invocation of the determined at least one virtual assistant.

Artificial intelligence and machine learning based product development

A device may obtain ticket data relating to a set of tickets, and process the ticket data to generate a ticket analysis model that is a clustering based natural language analysis model of natural language text associated with tickets of the set of tickets. The device may classify the set of tickets using the ticket analysis model, may determine an automation plan for at least one class of ticket determined based on classifying the set of tickets, and may implement the automation plan to configure an automatic ticket resolution or ticket generation mitigation procedure for the at least one class of ticket. The device may receive a ticket after configuring the automatic ticket resolution or ticket generation mitigation procedure, may classify, using the ticket analysis model, the ticket into the at least one class of ticket, and may automatically implement a response action for the ticket based on classifying the ticket and using the automatic ticket resolution or ticket generation mitigation procedure.

Automation plan generation and ticket classification for automated ticket resolution

A device may determine a plurality of components for a development project. The device may determine a blueprint template based on the plurality of components of the development project. The device may generate a blueprint for the development project based on the blueprint template. The device may generate a package for the development project based on the blueprint for the development project. The device may provide output associated with the blueprint and the package based on generating the blueprint and generating the package.

Development project blueprint and package generation

A device receives a command to identify an automation evaluation for a role, determines tasks of the role based on data relating to the role, and determines activities for the tasks based on the data relating to the role. The device determines one or more automation scores, which correspond to a suitability for automation of the activities, based on a set of characteristics of the activities and based on the data relating to the role. The automation scores are determined using a machine learning model to parse natural language descriptions of the activities and score parsed portions of the natural language descriptions. The device generates, for the role, an aggregate automation score based on the automation scores, determines the automation evaluation for the role based on the aggregate automation score and characteristics of an entity associated with the role, and performs an action relating to the automation evaluation.

Data processing for role assessment and course recommendation

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 component analysis platform may communicate with one or more devices to obtain prediction data relating to a type of component. The component analysis platform may process the prediction data to determine a set of predictors for failure of an instance of the component, and may generate a model for failure of the instance of the component based on the set of predictors. The component analysis platform may monitor the instance of the component to obtain component data relating to the instance of the component. The component analysis platform may determine, using the model and based on the component data relating to the instance of the component, a predicted failure for the instance of the component. The component analysis platform may perform a response action related to the predicted failure.

Data processing for component failure determination

A device may receive a feature submission identifying a candidate feature for a software application. The device may pre-process the feature submission to reformat data associated with the candidate feature to match a data format associated with stored information regarding past feature submissions. The device may perform natural language processing on the data associated with the candidate feature to determine a semantic meaning of the candidate feature. The device may compare the semantic meaning of the candidate feature with an index of past feature submissions. The device may provide a response indicating whether the candidate feature matches the past feature submissions wherein the response comprises an indication that the candidate feature does not match any past feature submission or information identifying one or more past feature submissions associated with a threshold semantic similarity to the candidate feature.

Feature submission de-duplication engine

A device may identify a tool operating on a first device for integration into a lifecycle management platform operating on a second device. The tool may be associated with providing a functionality not included in the lifecycle management platform. The first device may be external to the second device. The device may determine a set of tool attributes for data events associated with the tool. The data events may include a data input, a data output, a new message, an updated message, a deleted message, or the like. The device may select a message format based on the set of tool attributes. The device may configure adaptation for a tool application programming interface (API) of the tool and a platform API of the lifecycle management platform based on the message format. The device may provide information associated with configuring adaptation for the tool API and the platform API.

Device based automated tool integration for lifecycle management platform

A device receive data regarding a project. The device may automatically evaluate the data using a set of analytics tools. The device may communicate with a client device to provide a virtual agent user interface component based on evaluating the data using the set of analytics tools. The virtual agent user interface component may include information identifying a set of alerts relating to the project and a set of recommendations relating to the set of alerts. The device may receive information indicating that a particular recommendation, of the set of recommendations is to be implemented. The device may communicate with one or more other devices to implement the particular recommendation.

Data processor for projects

One or more computing resources, of a project management platform, may implement a delivery tool layer to provide one or more tools associated with managing portions of a project. The one or more computing resources may implement an integration layer to integrate the one or more tools into the project management platform for the project. The one or more computing resources may implement a data acquisition layer to obtain data regarding the project. The one or more computing resources may implement an automation layer to automate tasks associated with the project. The automation layer may be associated with automating a set of development operations, a set of maintenance operations, a set of management operations, a set of testing operations, or the like. The one or more computing resources may implement an artificial intelligence (AI) layer to process data relating to the project using one or more AI integrations.

End-to end project management platform with artificial intelligence integration

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Bengaluru, India

Mohan Sekhar