We would like to Welcome Senthilkumar Thirumalaisamy as our esteemed speaker for Global Testing Retreat #ATAGTR2019
Senthilkumar Thirumalaisamy is having 11 years of experience in the IT Industry, predominantly in Quality Engineering & Assurance. He has played roles as SDET, Functional Test Automation Lead, and currently working as Manager & Automation Architect for various Automation projects.
He has extensive work experience on Test automation, providing cost-effective Innovative automation tools, defining and delivering automation maturity roadmaps & Transformation programs.
Senthilkumar is our esteemed speaker for #ATAGTR2019.
Senthilkumar will be taking an Interactive Session on “Top 10 quality engineering best practices to achieve quality-at-speed“ – Track 2 on Day 1
Senthilkumar will be taking an Interactive Session on “AI-led Intelligent Testing” – Track 1 on Day 2
Senthilkumar will be taking an Interactive Session on “Assuring Quality for AI-based applications” – Track 1 on Day 2
Abstract: Top 10 quality engineering best practices to achieve quality-at-speed
The Digital era will see a proliferation of intelligent products, transaction channels and influencers available at the fingertips of a digital customer. This would require consumer–facing companies to deliver highest levels of customer experience, at speed and scale with higher efficiency. Quality, Customer Experience & Trust (Data privacy and Security) are therefore becoming more important than ever. For this reason, it has become paramount for enterprises to assure quality across the application lifecycle with better test processes, tools, people, governance and more importantly in maximized speed.
This paper provides a guideline to transform traditional testing approaches into best in class Quality Engineering (QE) for the Digital ICE (intelligent, continuous and early) age. This paper describes the Top 10 best practices in the areas of functional, automation, DevOps, production support and non-functional testing to deliver quality at speed. Some of the key best practices detailed in this paper include Automation First Mind-set, Change Based Testing, Leveraging the power of AI, Shift left with Service virtualization, Containerization, Continuous Everything and Metrics that Matter.
Abstract: AI-led Intelligent Testing
In today’s Digital era, quality at speed is the key to business success. Enterprises are continuously experimenting with newer software delivery models and investing in technology innovations to accelerate time-to-market. The success of Agile and DevOps relies on not just the automation coverage but in the ability to eliminate manual hand-offs and interventions. Automation has become the norm and businesses are heavily investing in orchestrating regression and progression automation for enabling Continuous Integration and Continuous Delivery. Test Automation moved beyond the UI to regression automation and end-to-end automation across the life cycle.
Artificial Intelligence represents a range of technologies including machine learning, natural language processing, semantic technology, and deep learning that learn over time as they are trained on more data.
This white paper describes an approach to intelligent automation in QA, leveraging Artificial Intelligence (AI) that eliminates the need for complex and extensive coding for automated tests. AI led intelligent testing can create, execute and repair regression tests, automatically and replaces the need to code Selenium or other test scripts. Modern AI embeds machine learning to learn about application flows and interdependencies. Such an AI model can learn from real-user data to remove the effort and inefficiencies in test scripting.
Abstract: Assuring Quality for AI-based applications
AI has already picked up pace across various industry sectors with the aim of deriving business value in terms of enhanced customer experience, new sources of revenue and cost reduction. AI will swiftly mature from ‘Siloed Pilots’ to ‘Mainstream Adoption’. Testing a software application and AI based application are two ends of a spectrum. While software applications have defined set of inputs/outputs, their behaviour is defined/documented and in most cases the inputs are linear, AI based applications have the following challenges: 1. Non-deterministic and probabilistic – No defined input and output, 2. Ever-changing Behaviour – AI systems are always learning, 3. Non-Linear inputs – e.g. Voice, conversational/free-flowing text.
To ensure that the AI applications function as expected, there is a need for an optimal test strategy which focusses on validating the AI algorithms with the right test data (fresh inputs/outputs post training of algorithm) and against a defined acceptance criterion (amount of error which business is willing to accept).
Assuring Quality for AI based applications pose several challenges such as:
• Getting the right data sets for testing
• Identifying suitable algorithms
• Predicting the expected outcome and behaviour of AI systems as they learn over time
This paper details out challenges involved in assuring quality for AI-based application as compared to traditional applications and provides unique approaches for testing for AI including:
• Testing machine learning algorithms through metamorphic relations
• Testing of unstructured data
• Adversarial scenarios testing
• Dual coding for Machine Learning (ML) model
• Testing of Voice User Interface (VUI)
• Testing of Chat-bots
We had posted some questions to Senthilkumar as a part of his #ATAMyStory
1. Why should someone choose testing as a career? Or What does testing really mean to you?
There were times Testing was considered routine, repetitive manual task and there was a myth from testing fraternity that any automation will replace individual’s job. But, as such testing is a challenging job as individual needs to certify any product from across the technology land scape, business and end users as well. A decade ago, when it comes to testing its manual but with digital disruption in place testing has emerged a new avatar from Automation to Quality assurance to Quality Engineering.
2. While practice makes all of us perfect, share an everyday practice that has made things better for you at work
We all aware that there is NO age limit for any individual to learn. Self-learning is something each individual does. In addition, anything makes us perfect only when you share your knowledge to the peers. Irrespective of any best practice, process, tools that we follow. A good example is an ATA forum that would facilitate and groom many with knowledge across the fraternity.
3. The most challenging bug or issue that I have found and learnings from it ? Or The most challenging testing task that I have done and how I accomplished the same
The most challenging testing task that I came across was providing quick turnaround solutions for technologies were there are less & competitive tools with higher cost. In addition, I have always managed to provide a valuable solution by R&D and getting Subject matter experts guidance.
#ATAGTR2019 is one of the largest, most fun filled and learning filled global testing conference. #ATAGTR2019 is back again in its 4th edition with more fun and more learnings than in the past. The conference is scheduled on 14th and 15th December in Pune.
- 2 Days
- 2 Panel Discussions
- 4 Keynotes
- 5 Skits and Games
- 38 Interactive Sessions
- 18+ Labs
Loads of fun, competitions and much much more.
Day 1 has 5 tracks and Day 2 has 6 tracks.
Focusing on interactive sessions, labs/workshops, skit performances and fun activities and quizzes and much much more.
The conference scale as is evident has increased substantially. We hope that everyone can be part of this most fun filled and one of the largest global testing conference.
To know more about the conference, click here or on the image below