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Episode transcript

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Rebecca Parsons: Hello, everyone. Welcome to another edition of the ºÚÁÏÃÅ Technology Podcast. My name is Rebecca Parsons. I'm the Chief Technology Officer for ºÚÁÏÃÅ and one of your recurring co-hosts. I'm joined by my companion co-host, Ken.

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Ken Mugrage: Hi, my name's Ken Mugrage. I'm a Principal Technologist with ºÚÁÏÃÅ and also one of the regular hosts.

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Rebecca: Ken and I are joined today by Gayathri, who has recently published the book entitled Full Stack Testing. We are here to talk to her about her journey writing the book. Welcome, Gayathri.

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Gayathri Mohan: Hello. Thanks, Rebecca, for the introductions. Thank you, Ken. I'm very happy to join you both today on the podcast.

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Rebecca: Let's start with some of the basics. Writing a book is an awful lot of work. What prompted you to actually write it in the first place?

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Gayathri: Yes, definitely, Rebecca. It's just a behemoth of tasks to be done. Particularly, I think my experience working with multiple clients and the discoveries that we have been doing with our clients made me realize that there's a lot of scope in this field to actually be written as a book. For example, I've seen clients where there has been no culture of testing at all or there has been just a silo testing team, who just went about executing some hundreds of test cases just during the release.

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Some clients had production defects and they had to pay penalties for their production defects, which would've been caught during a proper testing phase. Such kind of engagements and when we go there and we had to do enablement and also set up the testing team from the beginning rounds up, there's a lot of content that I could see that could be actually captured as a book. There was the right set of audience for it as well. I just thought maybe it'll be useful to give it a go.

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Rebecca: One of the things I find so differentiating about this book is its breadth. We see books on performance testing or we see books on accessibility or things of that nature. You really have run the gamut of so many different aspects of testing. What was the genesis of that idea?

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Gayathri: I felt like the testing discipline itself was just consolidated into manual and just automation testing. Especially when we talk about manual and automation testing, it's just about functional requirements as well. There was very little focus on the non-functional or cross-functional requirements testing. The terms, just manual and automation testing encompasses everything into it. The focus on those particular cross-functional requirements and non-functional requirements get lost into it.

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That's one of the primary ideas of actually creating the book with a focus on the breadth of testing. To show that testing is, though, you can consolidate it into manual and automation testing, it's just not about functional requirements, but there is a huge list of cross-functional requirements that needs to be tested and to be accounted for in the testing strategy. That's the motivation to cover the breadth of tools, techniques around all these areas.

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Rebecca: You commented frequently in the book about being —I think you called it the champion for testing or the advocate for testing. How would you characterize the role of a quality analyst and the responsibilities that everybody has to think about quality and testing?

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Gayathri: I think every role has something to contribute to the team. A QA's role, I think, primarily owns a large part of testing, but not definitely the only role that contributes to testing. I see the QA role being the champion of testing on the team to take ownership of empowering the team to do the right kind of testing, the right stages of the delivery cycle. For example, starting from the BAs for testing for accessibility, it needs to start from the product design stage itself. The BAs and the UX folks have to start thinking about product design testing, making sure that the accessibility features are included in them.

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Also, during the development stage, the accessibility features have to be tested for and included as part of the development itself, and then comes the manual testing phase where the QAs pitch in and also do write some functional automation tests, and also actually care for accessibility-related features like screen readers and everything. Owning this feature and empowering the team to do the right kind of testing at every single stage is very crucial for the team to successfully deliver a quality product. That's how I see the QA's role. Mainly being the champion for testing and also contributing towards the knowledge regarding testing in each of these phases.

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Ken: It's somewhat related. In the last little bit over a decade, we've seen the movement to DevOps and continuous delivery and all that. You just covered a little bit, but how has the role of QA evolved over just the last few years to deal with those kinds of silos and so forth?

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Gayathri: I think if we have to go back a little and look at the QA's role, we had some siloed QA teams. The development will happen and then a code will be passed onto the testing team, and then they'll do the testing at the end just before the release and find out a lot of issues and then push it back. It was also coming out as testing-as-a-service in the last few years. Some companies still do that.

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We had agile testing coming into picture, where we said we need to have cross-functional teams and integrating testing as part of — the delivery cycle is going to be key for quality. There were a lot of focus during that period. Once again, I feel like today we have a startup culture where testing is taking the backseat and the time to market is taking priority. I see the role of a QA as a very fluid role.

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Even today, I think certain teams place a lot of emphasis on testing like I said in the previous conversation about how some teams didn't have a testing culture at all. Some teams had a lot of emphasis on functional automation. Similar to that, I still see the role has been considered a very fluid role. The QAs sometimes go and pair heavily with the BAs to knock down the requirements, and sometimes they're still kept as a siloed team.

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Sometimes the cross-functional requirements are outsourced. It's not even considered part of the delivery team. It's outsourced for another team to do — For example, an external performance testing vendor is involved or a pen testing team is involved for testing. It becomes a very fluid discipline and something that is sometimes kept aside and not taken care of enough.

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This time, I feel like the QA has given their skill sets for them to take over the testing territory and drive some of these outcomes that we spoke about earlier as part of a CI/CD, adding tests in the right layers and focusing on cross-functional requirements, automation, starting testing right from the software requirements stage, working with the BAs, UX till through the delivery stage.

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Ken: That's really interesting about QA being part of the role in breaking down those silos. One of the things you talk about a lot in the book is not just the quality of the software from the bug perspective, but also how effective is it? What is a QA's role in building the right thing, the effective software versus just build it right?

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Gayathri: Oh, that's an excellent question, Ken. Very interesting question as well. I think the QAs on the team literally are the end-user representatives. I think when we have to QA a particular piece of code, I think we are empathizing with the end users and making sure that we bring out their point of view. Whatever doesn't make sense for the end user is actually something that the QAs bring out abruptly.

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This one was a good story that I remembered. In ºÚÁÏÃÅ University, when I joined as a grad, we used to have a lot of fun ways to learn about QA'ing. One of the games that we played was building a software, building a product with the Legos, and making sure that the BAs come up with the right requirements and the developers build the right thing with the Legos and then ask QAs going and testing the Lego product to make sure that it meets the end user's needs.

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I remember very vividly that the BA wrote, "Build an airplane and it should be able to fly." The developers actually built a toy Lego airplane and passed it on to testing. As QAs, the first thing that we did was just flew the plane and it crashed. [chuckles] It was a fun way to teach us that what is necessary for the end user is what the QA role is about. Anything that doesn't fit into the end user's use case, it just doesn't fit into the product.

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The same thing have applied into day-to-day software delivery as well. I've seen, for example, when we go into mobile, for example, you take the product, the QAs go and test it, and they come up with questions like, "Okay, I'm a left-hand user, and what about the right-hand users? Can the button be in some other place? I'm driving the car. Now, I want the voice-enable feature to use the product, so how about that?"

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Several of these questions do come up from the QAs, which I've seen make the product to be built in the right way rather than just making sure that whatever the requirements are passed and it is working just up to the requirements. I think I answered the questions about build the right thing versus build it right from a QA point of view.

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Rebecca: One of the things that has been a particular issue for me and some of the client work that I've done in the past is the problem of test data generation and getting the right test data in place to be able to properly test the application. Obviously, as we get more and more data-intensive applications, this whole idea of how do we properly test is becoming more important. How do you see that problem evolving?

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Gayathri: Very valid observation, Rebecca. I agree with you. Test data creation is one of the areas where I still see a lot of innovation can happen. Test data is going to be the primary factor for producing the right test results. If your test data is wrong, then the test results are going to be wrong as well and there's no point in testing it. Especially when it comes to scale, test data creation actually has been really problematic, especially in terms of multiple vendor data formats. There's graph-like connected tables, which takes a lot of input that needs to happen.

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Different forms of input data like files and various other formats of input data. Scale has made it even worse definitely. One of the options that we usually tend to suggest is to use the production data as test data to actually be able to produce test results that are close enough to what is actually going to happen in production. Once again, I think that is an area where we have to tackle all the security-related issues. We need to take care of BA information. There are some obfuscation tools. There are some techniques like scrambling and automatization and all of that, but still, an area where some innovation can definitely happen to alleviate some of these problems.

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Ken: One of the terms you use in the book and others use as well is the term "continuous testing." How does that play alongside or what's the difference between continuous integration and continuous delivery?

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Gayathri: I think you would've definitely heard about this, Ken, and you're a master of the subject. I came across this term, "continuous testing," via one of Jez Humble's book. I think the book Accelerate talks about it. The concept has introduced something like continuous testing is one of the capabilities for performing continuous delivery. Alongside continuous integration, I think continuous testing is another capability that we need to do continuous delivery.

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The differentiation primarily is, I think, when we're talking about continuous integration, we're talking about having short-lived code branches, being able to integrate the code in frequent intervals, small changes, incremental changes, and all of those stuff. Continuous testing is a capability where we have automated tests as part of the CI pipelines, both for functional and cross-functional requirements, and the capability to run them in the development environment.

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Not only that, but we are also including a stage where there is mandatory manual exploratory testing after the code is deployed. The manual exploratory testing is an important phase of continuous testing, where there are new scenarios that are being discovered during that stage. All the scenarios that have to be automated then gets automated so that the continuous testing process happens. By way, we are able to do continuous delivery, all in the process of being able to keep the code production ready at any point in time.

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Ken: There was a quote I took out of the book that said, "A wise way to balance the testing capacity in a project is to perform manual exploratory testing to find new test cases and automate them to aid in regression testing." Instead of TDD, should all your tests be coming from this manual testing, or is this in addition to TDD? How does that fit?

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Gayathri: Definitely not. I think TDD is a very important way to achieve automation. I think the context of the particular statement that you're quoting is-- so I've seen teams where I've--- I've told you that there are some silo testing teams and there are 400 test cases, which is manually executed every time. A release has to happen. When more features get added, there are more test cases. The only solution the business could think of is, "Okay, let's add more people to the testing team, and now let them start testing."

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That's an absurd way to manage the testing capacity itself, not really utilizing the testing team's full power. That's what I was addressing when I was saying that when you are using the testing capacity, use it to perform manual exploratory testing. That way, you could actually gain some value out of the people that you hired. You could actually find new test cases, find some new places that your developers and your BAs haven't thought about. Once again, automate that. That way, you'll be able to attain the right balance between just hiring more people and also getting the right value out of them.

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Rebecca: Yes, and I want to follow up on that because I believe that exploratory testing doesn't have the level of prominence that it should. I think that's effectively what you said. Very often, people are just focusing on, "Okay, I'm going to automate what's here." To me, the real value of a QA is, "Okay. Now, let's look at this in different ways." One of the things I often say is a development team is a finely-honed machine to get requirements out the door.

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They're working from a known context. Part of the value of the QA is to explore around the edges of that context. How do you learn to do that? What is the process that you went through in making the switch from, "I'm just going to think about automating these things," to "Now, I'm going to go push the boundaries"? What does it take to do that well?

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Gayathri: That's a fantastic question, Rebecca. Definitely, as you said, manual exploratory testing is one of the key areas that the QAs can contribute to. When it is actually skipped, then there's a lot of edges that is missed and just the requirements that is actually thought about is only focused upon. The experience that I went through in gaining that skill particularly, I can talk about that.

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There are some techniques definitely that are prescribed in the industry. There's some help there like boundary-value analysis. There are some all-pair testing. All of those techniques are still available. What is important in manual exploratory testing for me is to have that mindset of curiosity and also the tendency to challenge. Basically, you go ahead and challenge what is already known.

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The mentality of just putting yourself in the end user's place and also figuring out what has been missed, how can I break it, or that's kind of challenging mentality along with the curiosity to know how exactly your application is behaving in certain situation creates that or even amplifies that skill. That's how I went about-- I would say one can actually trigger that skill set learning.

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Rebecca: It sounds like it's probably the really fun part of being a tester. [laughs]

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Gayathri: Absolutely. [laughs] It's almost a crown when you actually discover something that's totally outside the boundaries.

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Rebecca: We touched on data a little bit, but I want to go back to it because, obviously, data quality is such an issue. When we're looking at things like machine-learning models that are built off of data, obviously, the data is what determines the model. What kinds of things are we learning as an industry about data cleansing and data quality and how do you see the role of QA in the broader questions of data governance?

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Gayathri: Data quality is definitely one of those evolving areas. Data has become the primary asset for any company these days. The scale at which the data has been presented for testing is also one of the key areas that a lot of learning has to happen yet. Having said that, I think there are a lot of tools already in place for testing the data quality itself. We have automation tools that can actually check for certain types of data like the data tapes.

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We can actually have some contracts in place for ensuring the data comes in certain way. There are some approaches already in place. The one way I tend to look at the data testing itself is basically to understand, what are the data systems that we are dealing with? Is it database or is it event-driven systems? Is it a bad job? Putting together some test harness specific to that particular data system is a way that I've approached this kind of data testing.

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When we are dealing with database, there could be some concurrency-related test cases. There could be some data type-related test cases. When it comes to bad jobs, there could be data skew-related test cases. Understanding what is the kind of data system that we are dealing with and putting a test harness around it has been one of the ways that has worked for me so far.

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Ken: Switching gears just a little bit. We talked about the breadth of the book. You have a chapter on mobile testing. There was another quote there that I stuck in my head. It was that the shape of the testing pyramid, if you will, from mobile is often inverted, depending on the characteristics and features of the app. A lot of our clients, they have applications that are web and mobile, so how do we balance this when the total solution isn't one or the other?

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Gayathri: I think it totally depends upon the type of the application that we are dealing with. For example, if the application is going to be logic-heavy like a banking system and then we're just having a banking mobile site, mobile app, and also a web application, then all of the logic is going to be abstracted for us into the services layer. There's going to be a very thin presence on the mobile UA layer.

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Whereas on the other hand, if we are looking at some sort of gaming or site mobile apps, then there are a lot of native interactions that need to happen on the mobile application. That's where, I think, the mobile pyramid gets inverted. Then there are a lot of native elements that need to access hardware and all of those or specific stuff. Then the mobile testing pyramid naturally gets inverted because there are very little that we can do to create automation tests. We have to rely on a lot of manual testing and also end-to-end test scenario.

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Whereas on an application, which has a lot of logic like a banking site, there's a lot of logic and there is abstraction in different layers. We could definitely adopt the testing pyramid. It totally depends upon what are the kind of features that we are dealing with. The only thing that we can do is understand the nature of the application and probably plan for testing capacity accordingly. If it's going to end up in an inverted testing pyramid, then plan for additional capacity around manual testing it and also putting together a thin end-to-end testing automation in place.

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Rebecca: Let's turn our attention now to some of the more flashy things. What's the role of AI these days in supporting testing and a QA?

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Gayathri: Yes, AI has definitely started to penetrate into the testing space, especially in test automation space. There are several features that they tend to offer. One that actually caught my eyes, which was something that I was longing for for a very long time, was the cell-feeling capability. The AI could actually figure out why the test is failing and actually prompt us with what changes can it make to the test so that it'll pass.

That has been something that I've been wanting for a very long time mainly because sometimes it'll just be the ID change. There'll be tens of tests that are failing, but the functionality and the feature, the look and feel everything will be the same. This kind of cell-feeling capability where it identifies that the element hasn't actually really changed, but only the ID has changed. Prompting for just a click on the button and it goes and changes all the ID naturally is a great benefit.

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Some of these kind of features are coming up and it is starting to come up into the testing space. Although most of the tools are still in the beta stages, I see that there's a lot of potential that the AI can take up in the test automation space. They're even saying that it could author test if we just have to go on the screen manually and show what the test cases have to be. That is still yet to prove itself, but some of the fancy features are coming into play.

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Rebecca: What does the future hold, do you think? Where do you see things moving overall in thinking about testing?

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Gayathri: I feel like testing is a space where a lot of research and innovation can happen. I feel like there are a lot of tools that are also coming up day by day. We have to see, there are so many tools in the same space itself for testing certain things. Still, there are a lot of challenges that are pending to be solved in the testing space. For example, there are legacy enterprise systems that deal with mainframe systems. We have to move them.

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What about testing the mainframe systems and then move to a new estate? What is the solution there? There are also problem statements. Now, we have a huge tech estate and there are lacks of tests that are there. Now, how do we handle that many tests and that many failures? What about the infrastructure there? There are also emerging technologies that are coming up like the AI and XOR space. What about testing and automation there? I'm really looking forward to seeing how some of these problem statements are being solved and I'm going to have a chance in solving some of them as well.

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Rebecca: Excellent. Well, thank you so much, Gayathri, for joining us. It's been a fascinating exploration of all things testing. Thank you, Ken, for joining us as well.

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Gayathri: Thank you. Thanks a lot for having me, Rebecca and Ken.

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Ken: Thank you.

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