Inspirations and Aspirations for the New Tester

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Next Generation Cognitive Robotic Test Framework ( NGCRTF)

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Responsible Creative Design: Future of Software Testing

Creating a Sustainable SW Testing Organization

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Age of Digital Assurance

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CI/CD for Digital Apps - Streamlining the Dev, Test & Release Cycle in Enterprises

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Future of Testing in DevOps World

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Magic of Testing

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Disruptive technologies and evolving discipline of digital quality

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Approach for Skill Assessment of Quality Engineers - A Case Study

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Continuous Integration: Leverage CI to Guarantee API Quality

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DevOps Next - Keeping It Simple the Softenger way (K.I.S.S.)

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• Training the system with a data model that considers factors influencing likelihood and impact of failure


• Calculating probability of failure using machine learning algorithms - Logistic Regression and Artificial Neural Network (ANN)


• Applying each algorithm on prior release information to determine the best technique for optimization


• Deploying AI driven intelligent test solution in CI/CD pipeline for greater efficiency

• Unavailability of complete & correct of data from source systems.


• Inability to use available data for deriving Business insights.


• Delayed / Missing Batch SLA’s.


• Obsolete data in the Database /Systems


• Takes too long for accessing / measure the Business Performance A further analysis uncovers following as the root causes in data quality and testing context:
  
  
  
  • No Exhaustive coverage at all check points
  
  
  • Missing defined Test strategies – esp.: Migration / Infrastructure upgrade
  
  
  • Unavailability of QA Framework
  
  
  • No exposure on Database objects validation
  
  
  • Lack of Automation tools or accelerators It is a call–to-action for organizations to address the above Data Quality gaps and plan towards digital transformation. Thus, this document outlines - Challenges, exhaustive test approach, Defined Testing strategies, practices, QA framework and automation possibilities for adapting and achieving Greater Data Quality.
  
  
  
  

• Implementation of Behavioral Driven Development (BDD) with Open Source Tool stack of Ruby, Watir and Cucumber to embrace agile and improve Speed to Market achieved $514K savings with reduction in defect leakage in UAT/Production from 10% to less than 1%.


• Implementation of Continuous Integration(CI) with Jenkins for Unattended Execution – Achieved unattended execution of 11000+ scenarios and 190 Web Services on weekly basis for early defect detection

• White-box Testing – Testing the core of the framework (e.g. automated unit testing of smart contracts using frameworks like Embark/Truffle/Dapple/Populus)


• Grey-box Testing - Testing integrations between Blockchain framework & applications (e.g. API Testing for validating access control, wallet balances etc)


• Black-box Testing - Testing of Blockchain application functionalities / business rules (e.g. issuance of crypto-currencies)


• Non-Functional Testing – Performance and security testing (e.g. validation of performance on pumping large transaction volume into the Blockchain network, secured Blockchain access, wallet signature etc.) Additionally, this paper will also explore areas such as Blockchain-as-a-Service, Blockchain Cloud, Blockchain IoT and their implications on Quality Assurance.

1. Effectiveness of the Algorithm/Logic


2. Correctness of Implementation


3. End to End Solution Accuracy


4. Quality Bench Mark Cognitive solutions are often about optimization or better insights over the traditional models. There are no fixed boundaries or pass fail criteria. Taking an example of forecast projections, we use what we call the “Time Travel Technique” – where validations would be conducted against the actuals by moving back in time. Other techniques like cross validations with key performance indicators can also help test the accuracy. The closer you are to reality the better the predictions. There are no one-fits-all solutions available while addressing the “Non-testable programs”. Every such solution needs a unique test plan and methods to ensure that the primary objective of the Quality function is met – The solution provided meets the customer need.

1. Identifying the expected data set pattern for training the algorithm


2. Tuning the algorithm to 100% accuracy on learning the expected data set Real time scenarios have been handpicked for illustrating how the above 2 challenges can be addressed. Once we address this challenges, rest becomes child play. Machine Learning Algorithms are rendered through languages like Java, Python & R. Illustrations in this paper are developed through Java. By extrapolating the ides to respective real time scenarios, the idea of leveraging Machine Learning for Test Data Management would become a reality. Intended audience for this paper would be Test Engineers, Test Leads, Test Managers, Test Architects & Test Consultants.

• Transforms organization culture away from an FTE based to Service Output based model / consumption based model, with clear ownership on both the requestor of the service & provider of services. Payment linked to successful outcome


• Predictable quality, Service price & Outcome through standardized Input, Output & tasks


• Engagement model standardization between Business & IT significantly reduces lead times associated with testing request to estimation approvals


• Service standardization to achieve common understanding and measure of success in delivering testing services • Complete predictability of service cost and service quality standards


• Continuous optimization of services committed


• Huge reduction in time, effort & cost to scope new service engagements


• Accurate estimate through use of historical data and statistical analysis tool


• Delivery risks transferred to service provider thereby driving the right behaviors from the partner organization

1. What is Artificial Intelligence


2. Some applications of AI


3. How AI based systems work


4. Languages used for AI programming


5. Heuristic algorithms used in AI


6. Models Used by AI Systems


7. Prerequisites on using AI based system


8. Data Cleansing & Standardization


9. Integrating AI Based System with your Existing Systems


10. Applications of AI in Software Testing


11. Benefits of using AI in Software Testing


12. Example of an actual AI enabled System for QA


13. Conclusion and Recommendation


14. References & Appendix

• Availability of a mobile device performance testing framework for the testing community


• Can be controlled remotely as well via Push Notifications.


• Provides crash logs for troubleshooting


• Plug & Play SDK


• Centralized reporting dashboard


• Notifications & Alarms for production monitoring.

• Reduction inmanual effort


• Reduction inanalysis time


• On-demand development, deployment and availability of Virtual Service


• Service-Virtualization-as-a-service using CI tools