Automation Leveraging Artificial Intelligence

Vinod Sundararaju Antony, Director – Projects

Ankur Joshi, Manager – Business DevelopmentCognizant Technology Solutions

AbstractThe mandate of the digital era is quality at speed. Enterprises are now mandated to delivery enhanced quality and customer experience at faster time to market. This this is further challenged by the exploding digital platforms. Automation has grained significant traction and is enabling the QA teams in reducing cycle time. Automation has matured and expanded from just UI based automation to end to end automation across the SDLC. However one of the significant drawbacks with current automation approaches is the need for extensive coding and maintenance. This white paper describes an approach to automation leveraging artificial intelligence eliminating the need for extensive coding for test automation for regression testing.

1. The digital era

Digital has far evolved from being a thought leadership initiative to the core of an organisation’s business strategy. A leading analyst research agency, IDC predicts that in 2019, worldwide spending on Digital Transformation will reach $2.2 trillion, almost 60% larger than in 2016.In particular new platforms and services for IoT, AI and conversational systems will be a key focus through 2020.

Organizations are increasingly looking for avenues which can help them achieve the right competitive advantage. With plethora of competitive products available in abundant channels/devices and significant democratization of technology, organizations have to ensure that their digital strategy hits all the right notes with the target audience.

Adoption of highly mature methodologies including CI and DevOps, have resulted in ever shrinking release timelines, which in turn demands a reorganization of IT to align with the Business strategy and ensure they deliver the right product in the right time with the right user experience. The expectation of such shorter release timelines while still maintaining the quality of the products, is the key driver to push quality upstream by enabling early defect detection through leverage of Shift-Left initiatives.

2. What are the challenges with the current automation approaches?

The pressure on the QA organization to test faster is ever increasing. The current challenges which the QA organizations face are:

Drawbacks with current test automation and regression approaches because of the need for extensive coding and maintenance. Increasing Maintenance & Failure effort - In general 60% - 70% of automation script failures are due to environmental issues, test data issues, issues related to configuration and 3rd party integration system failures. A significant effort is spent in triage of such script failures due to extraneous reasons

Different tools and frameworks across testing types make it cumbersome for testers to maintain and update the respective scripts. In some cases the effort is so high that there is a need to maintain dedicated build (testers who design test scripts) and run (testers who maintain scripts) teams

3. The rise of Artificial intelligence

The surge of Artificial IntelligenceThe concept of artificial intelligence has been in existence for the last 20 – 30 years. Artificial intelligence was once a discarded technology but has how resurged vigorously, thanks to powerful techniques such as big data and deep learning.

What is artificial intelligence?Artificial intelligence (AI) is the simulation of human intelligence processes by machines / computer systems. The processes include

learning (the acquisition of information and rules for using the information),

reasoning (using the rules to reach approximate or definite conclusions),

and self-correction.

Particular applications of AI include expert systems, speech recognition and machine vision.

AI is gaining traction in the fields of healthcare, business operations, education and finance.

4. Automation leveraging artificial intelligence

The automation platform elaborated further in this paper leverages artificial intelligence techniques to enable regression automation without scripting. Testing is performed automatically through intelligent object recognition and application crawl. This enables increased quality and efficiency where teams can jointly build, test, release, and maintain new digital applications more frequently and more efficiently.

The key elements of the automation platform are:

1. Intelligent Object Recognition

Advanced algorithms to identify UI objects directly at runtime during test execution similar to humans

Object identification capability combined with intelligent locator algorithm Ability to execute tests across multiple browsers and platforms

2. Application Crawl

Systematic crawl / application traversal Generation of application graph

Fig 1. Sample Application Graph

3. Tester, Analysis and Reporting Bot Tester Bots for performing automated user actions Analysis and Reporting bots comparing version of apps to identify pass and fail

4. Insights driven intelligence Intelligence based on production insights

The below diagram provides the detailed architecture:

Key BenefitsThe key benefits of this approach are:

Faster time to market – Significant reduction in efforts with complete E2E test coverage

Improved quality through increased and extensive coverage using self-learning algorithms

Scalability – Adaptable to client technology landscape, built on open source

5. Does this save the world? (Exclusions)

The above mentioned approach to automation is primarily suitable for regression testing. The specific scenarios in which the application would be suitable vs not-suitable are:

Suitable for Regression Testing Suitable for Digital applications (primarily B2C applications) Does not cover enterprise applications Not suitable for Content based applications (heavy content, video based

applications) Heavy business / financial processing logic involved in validations

Conclusion

In the current situation where release cycles have shrunk exponentially, continuous testing has become a mandate to aid continuous integration and deployment. One of the challenges that testing teams face today is the absence of a non-scripting based automation platform

which can perform thorough regression. This paper presents the unique and innovative approach to regression test automation eliminating the need for scripting and thereby ensuring high quality at the reduced cost while minimizing test cycle times.

References & Appendix http://www.gartner.com/smarterwithgartner/gartners-top-10-technology-trends-2017/ https://www.forbes.com/sites/gilpress/2015/12/06/6-predictions-about-the-future-of-

digital-transformation/#45903bc31102 http://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/beyond-

agile-reorganizing-it-for-faster-software-delivery The Forrester Wave™: Modern Application Functional Test Automation Tools, Q4

2016 2017 Predictions: Dynamics That Will Shape The Future In The Age Of The Customer IDC FutureScape: Worldwide IT Industry 2017 Predictions

Author Biography

Vinod Sundararaju Antony - Vinod has 16 years of experience in Software Testing and Quality Assurance. Over the years he has taken up a variety of roles such as Global Test Delivery Manager, Quality Consultant and Transformation Consultant. Vinod has extensive experience in Test Delivery, Test Process Maturity consulting, establishing Test Centers of Excellence, defining and delivering Quality Transformation programs and enabling enterprises to improve quality, optimize cost and time to market. Vinod has developed Quality strategy for large Business Transformation programs and has set up Enterprise wide Quality Strategy and processes. Vinod holds a Bachelor of Engineering degree and is PMP & ACP certified.

Ankur Joshi – Ankur has 8 years of experience in Software Quality Assurance and has played roles such as Business Development Manager and SDET. Ankur has experience in testing delivery for agile projects including testing areas such as functionality, regression, compatibility and analytics. Ankur has also been involved in developing QA solutions for large strategic programs for clients in the Information, Media & Entertainment domain and projecting value and benefits from transformational QA initiatives aligned to key business asks. Ankur holds Bachelor of Engineering & Master of Business Administration (Marketing & Operations) degrees and is ISTQB certified.

THANK YOU!