Automation Across SDLC

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Automation across SDLC

Automation strategy across value stream

• Build Metrics & Dashboard

• Automation Framework Setup
• Build Verification Test

• API Automation

• Automation using SV

•Critical Functional Test
•API & UI Automation

•Regression Automation with UI & API

•Regression Automation with UI & API
•Journey Automation (Across apps)

Planning

Build

Design

Integration

SIT

UAT

Pre Pod

Uniform Framework Across Mobile, Web & API

Designing the right framework for greater reusability

Real Time Reporting

Life Cycle Automation

Test Environment Automation

Virtualize services across ecosystem to ensure speed is maintained across QE phases

Test Data Automation

Use of Synthetic data automation through API or database insert queries or Data sub setting using golden records or DB snapshot.

Test Execution Automation

Sanity, Regression, End-to-end journey test automation. API test automation including call chains.

Test Environment

Test Data

Test Execution

Automation Delivery Approach

Proposal/SOW

Assessment

Automation Strategy

Test Data Environment

Sprint Wise Adoption

Scope Defination

Solution/ Test Design

Pre-Requisites
(Env,Data,Infra)

Scripting

Adoption Strategy

Training to customer team

Implementation

Test Sign Off

Automation Execution

Overview

AUTOMATION REGRESSION DASHBOARD

86 Applications

25,309

Test Cases Automated

795 Defects

CLASS A APPLICATIONS

NON CLASS A APPLICATIONS

76 Class A Application Base

Class A Application list taken from APM (Application Portfolio Management)

Business & Enterprise Layer Applications consists of 70 applications including security, infra & external are filtered out

38 Application Executed

Application that will be decommissioned
(over period of next 18 months)

Low changed frequency (no major changes in the app in 6 months)

Covered Through other applications Y2

Security Utility
(Internal root CA)

-

=

22 Application In Transformation

Automated regression will be covered as a part of transformation & changed program.
(eg Intuition, BPM,MW Upgrade etc.)

16 Application In Scope

Applications to be covered for automated regression as a part of the project.

Class B Application list taken from APM (Application Portfolio Management)

Business & Enterprise Layer Applications consists of 70 applications including security, infra & external are filtered out

Application that will be decommissioned
(over period of next 18 months)

Low changed frequency (no major changes in the app in 6 months)

Covered Through other applications Y2

Security Utility
(Internal root CA)

Automated regression will be covered as a part of transformation & changed program.
(eg Intuition, BPM,MW Upgrade etc.)

Applications to be covered for automated regression as a part of the project.

+

10 Application In Scope

0 Application Executed

10 Application In Transformation

0 Application In Scope

-

=

+

Execution Reports Sample

Power BI Real Time Dashboard (Project Progress)

Power BI Real Time Dashboard (Overall Defect Details)

Azure Automation Summary – (UI Execution)

UI Automation Summary

• 116 UI Automation scenarios identified and Automated for including Positive and Negative conditions

• UI Automation scripts included in CI/CD pipeline to be executed post deployment in SIT

• Automated executions using multiple browser (Chrome & Edge) is performed for the end to end user journeys

• UI Static content (Labels and Error) validations using UI automated scripts

• Test artifact is generated post every run to detail the status of UI executions

Azure Automation Summary – (API Execution)

API Automation Summary

• 135 API In Scope for CIB and all have automated and integrated in CI/CD Pipeline

• Scripts executed prior to every SIT deployment to ensure functionality is working. Artefact generated in an automated manner

• Failures are reviewed on a daily basis and defects are reported in Azure

Automation ROI – Benefits to a Client

Automation ROI Summary

• Incremental progress has given ROI in 6 months

• Effort saving of 878 PDs realized against 660 PDs spent

• Increased frequency of execution

Automation Identified Defects - Benefits to a Client

Defect Identification

• Enhanced coverage of automation resulted in increased defect identification

• Exponential increase in defect identification month on month