Intelligent Digital Mesh Testing

Intelligent Digital Mesh Testing

• 1.
  By Manisha Mittal– Quality Assurance Consultant Intelligent Digital Mesh QA & Testing Copyright © 2019, Nagarro. All rights reserved.
• 2.
  2 Agenda 1. Understanding intelligenthyper connected environment 2. Intelligent digital mesh across domains 3. Challenges and opportunities in Quality Assurance 4. Evolution in testing approaches 5. Benefits of AI/ML and analytics in digital mesh testing 6. Focus areas & tools for testing Intelligent apps 7. Intelligent testing landscape: Optimize test cycles 8. Intelligent testing approach 9. Challenges & Solutions 10. Business benefits of intelligent digital testing
• 3.
  Understanding intelligent hyperconnected environment An Intelligent system that can do intelligent things with: • Technologies like Artificial Intelligence, Machine Learning, Deep Learning, Natural Language Processing • Techniques that make smart machine understand, learn, predict, adapt, operate autonomously. Digital consists of: • Virtual & Augmented Reality • Conversational platforms • eGaming, movies & music, e Ticketing, automation & Control, healthcare and e communication Mesh connects people, devices, content & services together through: • Mobile devices • Wearables • Electronic devices • Mobile testing • Device testing: Wearables, Electronic devices • Scalability and performance testing • Regulatory compliance testing • Analytics (AI/ML) validation • Intelligent test case generation Digital Assurance focus areas: 3
• 4.
  4 Intelligent digital meshacross domains • Robot assisted surgeries • Personalized health monitoring using smart watchesHealthcare • Risk analytics & regulation • Managing client satisfaction • Portfolio managementFinancial • Dynamic pricing • Social media feedback analysis • Customer feedback analysisHospitality • Demand forecasting • Process optimisation • Condition MonitoringManufacturing • Recommendation engines & campaign analytics • Inventory planning • Better customer service with reduced response time Retail • Loyalty program and campaign analytics • Route planning and optimization • Better customer service by reducing response timeTravel • Smart supply and demand optimization • Power usage analytics • Customer specific pricing Energy
• 5.
  5 Challenges and opportunitiesin quality assurance • Spread of intelligent technologies leading to significant change in our lives • With intelligent technologies increasingly disrupting the business models, small companies pose a challenge to large corporate houses • Significant increase of internet traffic with spread of mobile telephony and interconnected devices • Plethora of hardware & software devices/dependency on technology for day to day needs • Requires anyplace, anywhere, and anytime connectivity on any device and with any service
• 6.
  6 Evolution in testingapproaches
• 7.
  7 • AI canwrite scripts and analyze large amounts of data sets faster • AI can handle sorting through log files, saving time and enhancing correctness in the program tremendously • AI can help in eliminating more bugs • ML approach, which will offer more reliable outcomes than traditional testing • ML analyzes customer data for a more proper understanding of the most recent products and features that customers need Benefits of AI/ML and analytics in digital mesh testing
• 8.
  8 Focus areas &tools for testing Intelligent apps
• 9.
  9 Intelligent testing landscape:Optimize test cycles Skills Required Test Parameters Test Goals Best Practices • Bot identifies the assets and helps generate test cases from requirements • Good grasp of statistical concepts • Capabilities in data analytics • Knowledge of computer- hardware architectures • Know-how of languages such as R, Python, Java, etc • A/B testing • Metamorphic testing • Predictive Analysis • Cognitive QA testing • Predictive QA dashboards • Intelligent QA automation • Continuous testing • Smart QA analytics • Defect Analytics • Log Analytics • Continuous Improvement • Design specific test approach • Perform usability testing • Ensure security & customer exp. • Assure range & frequency • Ensure performance • Simulate usage scenarios • Use real devices • Ensure uninterrupted data transmission & collection • Deploy network simulation tools • Increase levels of automation • Implement non-silo approach for test environment and data provisioning • Re-skill QA engineers • Improve tracking to optimize processes • Data security as per complaints
• 10.
  Intelligent testing approach •Bot identifies the assets and helps generate test cases from requirements. 01Identify • Bot identifies the duplicate test cases & optimizes test scenarios using AI & ML. 02Optimize • Bot automates environment setup • Bot accepts & validates coding standards, execution status • Bot integrates data from all cycles 03Automate • Bot analyses results and test performance. • Bot predicts future defects based on historical data 04Analyze 10
• 11.
  11 Identify Optimize AutomateAnalyze • Bot identifies the assets • Helps in generating the test cases from requirements • Analysis of existing test cases, data • Combines value from different attributes along with business rules • Achieves 100% coverage of business variations of application
• 12.
  12 Identify Optimize AutomateAnalyze • Optimize number & types of tests to achieve 100% coverage • Search, Tagging and Modeling: This model finds duplicate test cases & groups them together into what we call a cluster • Attributes & values found in these clusters are used in test optimization • Optimal set of test cases are generated which represent 100% coverage of business variations of application • Build a repository of optimized test suites that could be used repeatedly
• 13.
  13 Identify Optimize AutomateAnalyze • Actual automation in cucumber format is generated from optimal test-set, which is integrated into automation framework • Analysis between sprints is done to get 100% coverage. It includes modifying the original set of test cases and eliminating redundant test cases • Search test data catalogue and match the data which is needed for automation • Map objects with xpath and store objects
• 14.
  14 Identify Optimize AutomateAnalyze • AI analyzes defects and identifies the patterns of defects & helps prevent them in future • Cognitively identifies the origin of the defect in real-time & classify it as code or no code defect • Works together to reduce test cases & defects with lower cost, increase speed and quality with 100% coverage
• 15.
  15 Challenges & proposedsolutions
• 16.
  16 Business benefits ofintelligent digital testing • Predict, prevent, and automate the process using self-learning algorithms • Test suite optimization & identify of high risk areas for risk-based prioritization • Identify hotspots & automatically executing test cases Improved quality • Quicker time to market: Speed-up release of new products and services • Identify and remediate compliance gaps transparently More efficient, secure & resilient • Reduced cost of operations • Bots perform complex tasks Increased revenues and profits • Deliver services when requested • Identify and remediate compliance gaps transparently Availability & reliability • Enables organizations to quickly turn data into actionable insights for better decision making • Mitigate business and technical risks • Monitor for rapid and cost-effective scalability of operations Improve end-user satisfaction and customer loyalty • Bots are dynamic as they automatically discover and evaluate every new feature in the product • Bots analyzes the application from end user prospective and record performance • Bots builds, execute phenomenal amount of test cases within minutes Optimal test coverage
• 17.
  17 THANK YOU