Quality & Release Strategy for Native Android & iOS Apps at AppiumConf 2019

What an amazing time speaking at the first AppiumConf 2019 in Bangalore, India. I spoke about my experiences in setting "Quality & Release Strategy for Native Android & iOS Apps"

Abstract:
Experimentation and quick feedback is the key to success of any product, while of course ensuring a good quality product with new and better features is being shipped out at a decent / regular frequency to the users.

In this session, we will discuss how to enable experimentation, get quick feedback and reduce risk for the product by using a case study of a media / entertainment domain product, used by millions of users across 10+ countries - i.e. - we will discuss Testing Strategy and the Release process an Android & iOS Native app - that will help enable CI & CD.

To understand these techniques, we will quickly recap the challenges and quirks of testing Native Apps and how that is different than Web / Mobile Web Apps.

The majority of the discussion will focus on different techniques / practices related to Testing & Releases that can be established to achieve our goals, some of which are listed below:

• Functional Automation approach - identify and automate user scenarios, across supported regions
• Testing approach - what to test, when to test, how to test!
• Manual Sanity before release - and why it was important!
• Staged roll-outs via Google’s Play Store and Apple’s App Store
• Extensive monitoring of the release as users come on board, and comparing the key metrics (ex: consumer engagement) with prior releases
• Understanding Consumer Sentiments (Google’s Play Store / Apple’s App Store review comments, Social Media scans, Issues reported to / by Support, etc.)

Slides:

Quality & Release Strategy for Native Android & iOS Apps from Anand Bagmar

Visual Validation - The Missing Tip of the Automation Pyramid at QuaNTA NXT at Globant

I spoke about Visual Validation - The Missing Tip of the Automation Pyramid at QuaNTA NXT event organised by Globant India Pvt. Ltd.

The event was very well organised and I had the opportunity to interact with a full house, and also later meet and talk with a lot of interesting people - curious about current state of testing, test automation and how AI can impact it in the future.

Agenda:

Below is the abstract of my talk:

The Test Automation Pyramid is not a new concept. While Automation helps validate functionality of your product, the look & feel / user-experience (UX) validation is still mostly manual.

With everyone wanting to be Agile, doing quick releases, this look & feel / UX validation becomes the bottleneck, and also is a very error-prone activity which causes brand, revenue and leads diluting your user-base.

In this session, we will explore why Automated Visual Validation is now essential in your Automation Strategy and also look at how an AI-powered tool - Applitools Eyes, can solve this problem.

Recording from the talk:

Some pictures:

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Collaboration - A Taboo!

In AgileIndia 2019 in Bangalore, as part of the Agile Mindset theme, I played a tweak of the Taboo game - to make it a Collaboration game.

Abstract: 

When one has fun at work, work becomes fun. However, daily pressures, metrics, KPIs, and what not, have dissolved the fun, and made work drudgery in various ways. 

This creates stress for individuals, in teams, and across teams, there is mistrust, unnecessary competition, blame, finger-pointing ….

What better way to learn, and re-learn the basics of life, work, team-work - than to play a game, have fun, and correlate it with how life and work indeed should be treated as a game, and we should have fun in this journey. Only then can people truly succeed, and so can organisations.

Here, we will play a game – “Collaboration - A Taboo!” – where you will 

• Re-learn collaboration techniques via a game! 
• Learning applicable for individuals and teams, in small or big organisations
• Re-live your childhood when playing this game

Be prepared for a twist which will leave you thinking!

Slides:

Collaboration - A Taboo! from Anand Bagmar



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Visual validation - The Missing Tip of the Automation Pyramid

At yet-another-vodQA at ThoughtWorks, this time in the Pune edition on 16th March 2019, I spoke about Visual validation - The Missing Tip of the Automation Pyramid

Abstract:

The Test Automation Pyramid is not a new concept. The top of the pyramid is our UI / end-2-end functional tests - which should cover the breadth of the product.

What the functional tests cannot capture though, is the aspects of UX validations that can only be seen and in some cases, captured by the human eye. This is where the new buzzwords of AI & ML can truly help.

In this session, we will explore why Visual Validation is an important cog in the wheel of Test Automation and also different tools and techniques that can help achieve this. We will also see a demo of Applitools Eyes - and how it can be a good option to close this gap in automation!

Slides are available from here

Visual Validation - The Missing Tip of the Automation Pyramid from Anand Bagmar

Video is available here:

Thanks to Priyank Shah for this pic!

I also received some awesome feedback for the same.

Thanks vodQA Team! Till next time, adios!

Talks and workshops in Agile India 2019

In the upcoming Agile India 2019 in Bangalore, I will be speaking about:

If you have not yet registered, you can use this code to get a discount on your registration - anand-10di$c-agile 

In addition, there are some great pre and post conference workshops as well. I will be participating in "Facilitating for Effective Collaboration...One Nudge at a Time" workshop - conducted by Deborah Hartmann Preuss and Ellen Grove. 

This is going to be one amazing conference to learn, network and share ideas and experiences. See you there!

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Test Automation in the World of AI and ML

My article on "Test Automation in the World of AI & ML" recently got published on InfoQ.


Here are the key takeaways mentioned in the article -

• There are many criteria to be considered before building framework / selecting tools for Functional Test Automation
• It is very important to prioritise framework / tools capabilities needed for the software-under-test
• A good, scalable Test Automation Framework that provides fast and reliable feedback to the team enables collaboration and CI/CD
• Debugging / RCA (root cause analysis) and support for libraries / tools used is an afterthought in most cases. Do not fall in that trap.
• There are some promising commercial tools that fit seamlessly in the Agile way of working. Depending on the complete context, these tools may be a good choice over building your own framework for Functional Automation.

You can read the full article from here. 

Looking forward to comments on the same!

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Recording from webinar on The Missing Feedback Loop now available

On 21st Nov, TestCraft.io hosted me in a webinar where I spoke about - "The Missing Feedback Loop - The Tools, Techniques, and Automation to Solve It". 

You can get the recording from here (https://hubs.ly/H0fBDN50).

A blog about my blog

With the risk of this ending up becoming a recursion, I want to share interesting statistics about my blog - essenceoftesting.blogspot.com and content shared on my SlideShare account.

• First blog: Future of Test Automation Tools & Infrastructure, published on 25th Aug 2010
• Number of blogs published: 218, not counting this one
• Number of viewers: 400K+

Here are some charts from Analytics of my SlideShare and blog:

Top content from my SlideShare:

• Agile QA Process - 75K+ views, 2.75K+ downloads
• Career Path of a Tester - 1.4K views, 50 downloads

Blog Overview:

Audience:

Popular posts:

• Career Path of a Tester!
• Introducing MAD LAB - for Mobile Automation
• WAAT - Web Analytics Automation Testing Framework
• Any WAAT (Web Analytics Automation Testing Framework) users out there?
• Taking Protractor to the next level
• Features of my Android Test Automation Framework

Referrers:

Update: Upcoming webinar - The Missing Feedback Loop

Please note that the webinar will be on 21st November. 

Register here (https://hubs.ly/H0fp4by0)

Is the Future of Test Automation I predicted already here?

Today, almost at the end of 2018, I have come across many tools focused on making Test Automation, easier, faster, reliable and more valuable to the teams & the product - like testim.io, testcraft.io, katalon, cypress.io, mabl, test.ai, etc. These tools are very interesting and very promising for the value proposition they are bringing to the table. 

As I reflect on these shiny new tools, my mind wanders back to 2009 / 2010 when I was toying with the idea of what would be next in Test Automation Tools & Infrastructure space. I had penned my thoughts and published an article on ThoughtWorks Insights with the title - "Future of Test Automation Tools & Infrastructure" (https://www.thoughtworks.com/insights/blog/future-test-automation-tools-infrastructure). 

If we look deeper in my post, the tools I mentioned above (and many others that I probably am unaware of), are conceptually on the lines of what I had sort-of thought in 2009 / 2010. They are using a very interesting blend of past experiences, in some cases advanced technology like AI & ML, in some cases leveraging cloud / SaaS model, and more importantly - pushing the boundaries to do things differently! I am personally very happy to see this happen.

That gets another set of questions in my mind now - if what I had thought of back then is now true, and a reality, then what is next? What will the next generation of new, interesting, shiny tools look like in the next 5 years?

Upcoming webinar - The Missing Feedback Loop

I am very excited to share that I am going to conduct a webinar hosted by testcraft.io on "The Missing Feedback Loop - The Tools, Techniques, and Automation to Solve It". 

You can register for the webinar from here (https://hubs.ly/H0fp4by0).

Date & Time:
Thursday, November 21, 2018 at 02:00 PM New-York (EDT), 11:00 AM San-Francisco (PDT) and 08:00 PM Amsterdam (UTC+2)

Agile Testing, Analytics Testing and Measuring Consumer Quality from Poland and USA

The last few weeks have been very hectic for me. In between my consulting assignments, I traveled to Krakow, Poland for Agile & Automation Days 2018, and then to Arlington, Virginia in USA for STPCon Fall 2018.

In the Agile & Automation Days 2018 conference, I spoke about "Measuring Consumer Quality - The Missing Feedback Loop" and conducted a 1/2 day workshop on "Analytics Rebooted - A Workshop".

In STPCon Fall 2018, I conducted 2 workshops - 1/2 day each - "Practical Agile Testing Workshop" and "Analytics Rebooted - A Workshop" and also spoke about "Measuring Consumer Quality - The Missing Feedback Loop"

Overall, I had a very good trip, amazing conversations and interactions with the attendees and the speakers. I would be lying if I say I am not tired and my throat has gone sore. But, would I do this again? Absolutely! Going to conferences and meeting people, sharing my experiences with them, and learning from their experiences gives me a lot of happiness and satisfaction.

Below are the abstracts of the workshops and the talk. 

Contact me via LinkedIn, or twitter, or my site - essenceoftesting.com if you need any additional information, or if you want help in learning / implementing these or other topics related to Quality / Testing / Automation.

Practical Agile Testing Workshop

Workshop Description:

The Agile Manifesto was published in 2001. It took the software industry a good few years to truly understand what the manifesto means, and the principles behind it. However, choosing and implementing the right set of practices to get the true value from working the Agile way has been the biggest challenge for most!

While Agile is now mainstream, and as we get better at the development practices to “being Agile”, Testing has still been lagging behind in most cases. A lot of teams are still working in the staggered fashion (a.k.a. Iterative waterfall way of working). Here teams may be testing after development completes, or Automation is done in the next Iteration / Sprint, etc.

In this workshop, we will learn and share various principles and practices which teams should adopt to be successful in testing (in-cycle) in Agile projects.

Workshop Agenda:

• What is Agile testing? - Learn what does it mean to Test on Agile Projects
• Effective strategies for Distributed Testing - Learn practices that help bridge the Distributed Testing gap!
• Test Automation in Agile Projects - Why? What? How? - Why is Test Automation important, and how do we implement a good, robust, scalable and maintainable Test Automation framework!
• Build the "right" regression suite using Behavior Driven Testing (BDT) - Behavior Driven Testing (BDT) is an evolved way of thinking about Testing. It helps in identifying the 'correct' scenarios, in form of user journeys, to build a good and effective (manual & automation) regression suite that validates the Business Goals. 

Key learning for participants in this workshop:

• Understand the Agile Testing Manifesto.
• Learn the essential Testing practices and activities essential for teams to adopt to work in Agile way of working.
• Discover techniques to do effective testing in distributed teams.
• Find out how Automation plays a crucial role in Agile projects.
• Learn how to build a good, robust, scalable and maintainable Functional Automation framework.
• Learn, by practice, how to identify the right types of tests to automate as UI functional tests - to get quick and effective feedback.

Analytics Rebooted – A Workshop

Workshop Description:

I have come across some extreme examples of Business / Organizations who have all their eggs in one basket - in terms of # understand their Consumers (engagement / usage / patterns / etc.), # understand usage of product features, and, # do all revenue-related book-keeping

This is all done purely on Analytics! Hence, to say “Business runs on Analytics, and it may be OK for some product / user features to not work correctly, but Analytics should always work” - is not a myth!

What this means is Analytics is more important now, than before.

In this workshop, we will not assume anything. We will discuss and learn by example and practice, the following:

• How does Analytics works (for Web & Mobile)? 
• Test Analytics manually in different ways 
• Test Analytics via the final reports
• Why some Automation strategies will work, and some WILL NOT WORK (based on my experience)!
• We will see demo of the Automation running for the same.
• Time permitting, we will setup running some Automation scripts on your machine to validate the same

Measuring Consumer Quality – The Missing Feedback Loop

Session Description:

How to build a good quality product is not a new topic. Proper usage of methodologies, processes, practices, collaboration techniques can yield amazing results for the team, the organization, and for the end-users of your product.

While there is a lot of emphasis on the processes and practices side, one aspect that is still spoken about “loosely” - is the feedback loop from your end-users to making better decisions.

SO, what is this feedback loop? Is it a myth? How do you measure it? Is there a “magic” formula to understand this data received? How to you add value to your product using this data?

In this interactive session, we will use a case study of a B2C entertainment-domain product (having millions of consumers) as an example to understand and also answer the following questions:

• The importance of knowing your Consumers
• How do you know your product is working well?
• How do you know your Consumers are engaged with your product?
• Can you draw inferences and patterns from the data to reach of point of being able to make predictions on Consumer behavior, before making any code change?

Attendees will have deeper understanding and appreciation of the following:

• What is Consumer Quality and how does it help shape your business!
• Ways to measure Consumer Quality
• Why is understanding Consumer Engagement vital to the success of your product

Conference season here is - talks, workshops, travelling, networking!

September & October 2018 is a busy conference season for me.

On 27th September, I played a game - "Collaboration - A Taboo!" at ATA GTR 2018 with an audience of 100+ people. There was absolute chaos in the game - a lot of it self-inflicted ... and thankfully - exactly was I wanted it to be. So much fun, energy and enthusiasm in the room meant there was no one feeling drowsy in the post lunch session! 

Typically I play this game in 45-min to 1 hour duration. At ATA GTR 2018 though, I had only 30 min to play the game, and add my own twist on top of it. But, never have I ever taken more than the allocated time - and I managed to get the objectives of the game achieved as well in these 30 min.

Below are some pictures from the game.




Then on 28th September, I spoke on "Measuring Consumer Quality - The Missing Feedback Loop" at StepIn's PSTC 2018. Slides from that talk can be found here.

In October, I will be off to Agile & Automation Days in Krakow, Poland. Here I will be speaking about "Measuring Consumer Quality - The Missing Feedback Loop" and also conducting a workshop on - "Analytics Rebooted - A Workshop". See detailed schedule here. 

Then I fly directly to Arlington, VA to participate in STPCon Fall 2018. Here I will be conducting 2 workshops - "Analytics Rebooted - A Workshop" and "Practical Agile Testing Workshop". I am also speaking about "Measuring Consumer Quality - The Missing Feedback Loop".

Will share experiences from these conferences soon!

Interesting reads - Data, Analytics, Security, DevOps, Python

Here is some interesting reading material I came across today - related to Data, Analytics, Security, DevOps and Python

• AI & Analytics in Production  
• Case Studies: Machine Learning
• Putting the Power of Kafka into the Hands of Data Scientists
• Building a Real-Time Bike-Share Data Pipeline with StreamSets, Kafka and MapD
• Real-time data foundations: Kafka - The what, why, and how of Kafka
• A Short Summary of Smoothing Algorithms
• AVA: The Art and Science of Image Discovery at Netflix
• Deploy only what you trust: introducing Binary Authorization for Google Kubernetes Engine
• A Clean Start: Finding Vulnerabilities in your Docker Base Images
• The Best User Behavior Analytics for Your Web or Mobile Product
• Mozilla identifies 10 open source personas: What you need to know
• 18 Python programming books for beginners and veterans
• Making the Adobe Cloud More Resilient — with Chaos
• 6 Patterns for Continuous Delivery with Serverless Applications
• How Kubernetes Is Transforming into a Universal Scheduler
• InSpec: Auditing and Testing Framework
• ChaoSlingr: Introducing Security into Chaos Testing
• Retire.js - What you require you must also retire

Happy Reading to you!

Testing in the Agile World

Thanks to ThoughtWorks, I was introduced to many things - 

• Agile
• Blogging
• Speaking at conferences
• Contributing to Open-source
• ...
• ...

The list is actually quite long - but that is not the intention of this post.

The main takeaway in my learning at ThoughtWorks though, is how to Test better, and be more effective in that for the end-user. 

Even before my time at ThoughtWorks, I never agreed with the thought process that Functional Automation can / should be done only when the feature is stable. But at ThoughtWorks, I did learn many more tips and tricks and techniques and processes how to do this Functional Automation in a better way, as the product is evolving.

On 9th April 2011, I had written a detailed blog post / article regarding how can we test better in the Agile world. 

This post was titled - "Agile QA Process", and the document was uploaded to slideshare with the name - "Agile QA Process". I am very pleasantly surprised that till date, that document has had over 74K views and almost 2.7K downloads, and is still my topmost viewed post on slideshare.

When I look back at the document, it still seems very relevant and applicable, to me! 

What do you think?

Some good examples of Data Science, AI & ML

Following up on my earlier post about ODSC - Data Science, AI, ML - Hype, or Reality?, I thought it is good to also share some of the good examples of work happening in the field.

Here are some of the examples I got to hear in the ODSC conference, most of which are available to the common human:

• Amazing work done in the complex field of Speech recognition 
• Why complex? Think about languages, dialects, multiple conversations at the same time, different speed of talking, etc.
• Text to speech
• Ex: This is especially very helpful for people with disabilities
• Speech to Text
• Ex: Alexa, Google Voice, etc. type of applications
• Traffic control / Routes / Navigation
• Ex: Google Maps
• Recommendation engines
• Ex: eCommerce products
• Preventive maintenance
• Lot of advanced vehicles have a number of sensors that can alert the driver / car manufacturer about potential issues coming up / service due for the vehicle
• Autonomous vehicles
• Ex: Self driving vehicles
• Ex: Optimizing Cab scheduling / routing - There was a good session on how OLA manage its complexity in scheduling and routing - which is very applicable to eCommerce, Aviation industry, Hotel industry, etc.
• I recently also saw a video about Volvo truck driver getting out of the truck in a difficult terrain, and walking in front of the truck, controlling its movement using a game-like controller
• Medical equipment / gadgets for preventive / alerting health-care products

Also, Dr. Ravi Mehrotra, from IDeaS made a very powerful statement in his keynote - that I loved!! 

He said - "Best way to learn, is to forget what is not important".

This statement resonates a lot with what I think .... one needs to forget what is not (as) important, in order to focus and prioritize on what is important and can add value.

Especially true for Testers to keep in mind!

ODSC - Data Science, AI, ML - Hype, or Reality?

I got a chance to attend ODSC India, held in Bangalore on 31st Aug / 1st Sept. For those who don't know, ODSC is the largest Applied Data Science and AI conference, and it was conducted in India the first time this year.

I was very excited to attend this for couple of reasons:

• I was attending a conference after a long time (i.e. where I was not speaking). So this was going to be a pure learning and knowing expedition for me.
• Data Science / AI / ML have become huge buzzwords in the industry now. I had some opinions about it - but that was with limited knowledge / understanding about it. I was hungry to learn some specific of these buzzwords.

Since I was going to travel to Bangalore for ODSC anyway, I also decided to participate in the pre-conference workshop - Advanced Data Analysis, Dashboards And Visualization. I thought it would be interesting to learn about the What, Why and How of the techniques of Data Analysis, Dashboards and visualization - which would help me as I rebuild / extend TTA (Test Trend Analyzer). Though the workshop was good, it focused completely on Tableau as a tool and unfortunately did not meet my objectives / expectations. That said, there is another tool I came across in the conference - KNIME - seems interesting and am going to try it out.

The conference was good though. I attended a lot of sessions and had lot of hallway-conversations with many interesting people. Typical outcome of attending a conference, some sessions I liked better than others, some were amazing, some were mediocre. 

Here is my unstructured assessment of what I now think about what I heard and discussed:

• Advanced mathematics learnt in colleges has an application in data science. So if children / kids ask why should they study Statistics - here is an answer!
• Creating data models without Business Context will not work. If it does, you have been lucky :)
• There are some interesting case studies and success stories of AI & ML. But these are the same success stories around since quite some time. All the other "noise" of AI & ML so far seems a hype so far.
• There is a lot of value in understanding historical data better. Based on that understanding, there can be opportunities to forecast the future. There is a huge risk of doing this forecasting, IF % of uncertainty is not included as part of it. However, it is very easily ignored.
• Understanding of Neural Networks, computing, and algorithms is essential to building intelligent solutions for complex problems.
• It is not sufficient to get better / accurate prediction results. Being able to explain how and why those results are better / same / worse is equally important. In many cases, this would be a regulatory requirement.
• Data Science is the "art" & "science" of understanding data better. To do this, we need to first cleanse / prep the data, simplify it using various techniques, and learn techniques to visualize the data.
• There is a "grammar of graphics" and a "grammar of interactive graphics" - which helps in thinking about data visualization.
• Deploying these AI / ML solutions to production is not a trivial task - mainly due to the fact of high computing and huge volume of data processing required to make it production ready. - This is a huge opportunity for the general Software Development / Testing/ DevOps community to solve problems faced by data scientists / people in the data science / AI / ML domain.
• With data privacy laws rightly becoming stricter, you need to be careful and use only legally obtained sample datasets for analyzing / training the data models - else there is going to be huge penalties for companies involved. (This is in reference to GDPR, a new law coming up in USA and also India.)
• Earlier, only PhD holder were qualified folks to work on Data Science. Now-a-days, the trend is to get relevant training to interns, and have them work on these problems, and then get the results validated / explained by the PhD specialists.
• In a nutshell - Data Science, AI, ML are using specialized types of tools and technologies to solve different problems. People / organizations have been doing these activities before the buzzwords were formed / or got popular.

So, what is my core takeaway from this? 

• As with any new buzzword, there is interesting work happening in Data Science, AI & ML - but the majority claiming to be in the field are just creating and riding the hype!

That said, I want to do the following:

• Find opportunities to investigate and understand the Data Science + AI + ML in more detail. 
• Understand the skills and capabilities required from a software developer + QA role perspective to contribute more effectively in solving these newer problem statements
• Learn python / R 
• Experiment with various tools / libraries related to data visualization

Converting JSON into usable objects

JSON is a great way to specify data / information and, off late, it is the format of my choice to specify test data.


I find it to be -

• light weight 
• easy to understand 
• almost very intuitive to know if you have made an error in the syntax 
• easy to read into code and parse 
• easy create meaningful custom objects and use in code 

Recently, thanks to a friend - Abhijeet Vaikar, I came across a tool - quicktype.io - that helps in transforming the raw JSON (from various sources) directly into custom objects, in a variety of languages.

Site: https://quicktype.io/

The tool: https://app.quicktype.io/

I got to know about this tool at perfect time as I am building a new tool for dynamic logging in Java - AutoLogJ (but more about AutoLogJ later). quicktype.io does what it promises - and it saved me a lot of time to build the custom POJOs for the same.

Thanks Abhijeet Vaikar and the quicktype team!

A few thoughts on Test Automation

Deepanshu Agarwal and Brijesh Deb asked some very interesting questions on a LinkedIn post. Since I have some verbose thoughts on this, thought it is better to respond via a blog post instead.

• Why is Test Automation still considered suitable only for regression testing? What about writing automation tests sooner as in case of Test Driven Development?
• [Anand] - Depends what you call test automation? If ONLY FUNCTIONAL, then its better to explore the product first, investigate / have conversations with developers on what lower-level tests are already automated, and then based on cost / risk-value analysis, decide what else needs to be automated at Functional layer.
  
  
  A tangential rant ....

  The reason we think about classifications such as SMOKE, SANITY, REGRESSION in Functional Automation ONLY has a big reason. These tests are inherently very slow, brittle and it takes a lot of effort to ensure these tests give poor feedback on exact point / reason of failure. 

  
  

  I have never seen any other form of tests - say Unit tests, which would be magnitudes in number larger than the functional tests (hopefully) ever have any such classification. We all just say, the unit tests ran, not the smoke unit tests ran. 

  
  

  We need to grow up and understand the reason behind this. We need to make our top-of-the-pyramid tests as less in number as possible. We need to ensure we use good programming / development practices and get quick and reliable feedback from these tests. Else we will keep focussing on the symptoms, and never get to the root cause.

  
  

  --- Rant ends

  
  

  Once we understand this, then it is a matter of understanding in the context what can and needs to happen first, and what next. In most cases TDD will work. But TDD as a Functional Spec may, or may-not be an overkill .... the team has to decide that.

• Why do the automated tests always have to derived from manual tests?
• [Anand] - What is a manual test? Something that a machine is not performing? How do you do "manual testing"? Is Exploratory Testing subset of Manual Testing, or the other way, or any other thoughts on that?
  
  
  From the perspective of "automated tests" - I read it as "automated functional tests" here. In that case, the answer for the above question holds true here as well.
  
  
  Continuing from that thought - I think the approach (of deriving automated tests from so-called manual tests) is better than thinking upfront what tests I am going to automate and then proceed with the implementation without any thought or regard to any other learning along the way.

• The tests classified as manual tests are only focused at ensuring certain checks. What about actually running some tests to discover the unknown?
• [Anand] I don't want to get into the 'checks' debate. It is futile!
  
  
  All I have understood is - you cannot just spend time looking at the requirements / specs and write down (in your mind / bullet points / story cards / some fancy ALM tool) your test cases / scenarios. 
  
  
  That list is just a starting point of your journey of exploration and experimentation with the product-under-test. If you think that what you have identified is your actual scope of testing, then ALL THE BEST to you, your team and your product - because there are going to be so many opportunities you have missed out to make the product better and usable for the end-user. Unfortunately, lot of organisation still look for "regression" testing cycles - where (you think) you execute all the tests that were identified in a time long ago. However, everyone knows, it is best case / best effort, IF AT ALL, of actually following each and every step of that regression cycle. Such a waste of time and effort - when more meaningful testing could have been performed during that time.

• Why is that exploratory tests are still considered suitable only for manual testing? How about automating exploratory tests using AI?
• [Anand] What is the meaning of "exploration"?
  As per a quick online search, this is what it means:
  
  
  
  
  
  Now - how can you automate the unknown / unfamiliar? You can use tools to help figure out what is unknown / unfamiliar ... but once you know it, then it does not remain 'unknown'. I think buzzwords like AI and ML are tools to help bridge the gap in the known and the unknown. But we would still need to guide and use these tools and technologies to our advantage, to aid in our exploration.