Welcome to The Long View—where we peruse the news of the week and strip it to the essentials. Let’s work out what really matters.
This week, four things that caught my eye from Amazon Web Services’ re:Invent 2022 conference: SnapStart turbocharges Lambda, Graviton3E ARM HPC SoC, AWS continues hiring, and AWS’s origin story. (more…)
Zilliz this week made generally available a managed cloud service that can now be spun up in less than five minutes. The service is based on the company’s open source Milvus vector database.
Frank Liu, director of operations for Zilliz, said the managed Zilliz Cloud service running on the Amazon Web Services (AWS) cloud will make vector databases much more accessible to a wider range of application developers.
Vector databases are gaining traction because they provide artificial intelligence (AI) application developers with a more efficient method for managing the unstructured data need to train AI models. Currently, many of those teams are relying on labels and tags to classify and manage data. The Milvus vector database employs deep learning algorithms and its own AI model to create and store, index and manage vectors to more efficiently classify data, said Liu.
Based on a cloud-native architecture that, for example, employs Kubernetes to scale consumption of cloud resources more efficiently, Zilliz Cloud can also scale to process tens of billions of high-dimensional vector data for data-intensive applications, he added. The company claims Zilliz Cloud delivers up to 10 times greater throughput and five times lower latency for read and write operations for data-intensive workloads.
Other attributes of the managed Zilliz Cloud include support for multi-language software development kits (SDKs), a web-based graphical user interface to search and explore data, SOC 2 compliance, network isolation using IP whitelists and full data encryption in transit (TLS).
In the future, Zilliz also plans to add support for Microsoft Azure Cloud and Google Cloud Platform in addition to graphical processor units (GPUs) to optimize the performance of its deep learning algorithms.
Potential use cases for a vector database span everything from image retrieval and video analysis to fraud detection to drug discovery. Organizations that already use Milvus, which is being advanced under the auspices of the LF AI & Data Foundation, include eBay, Shopee and SmartNews.
In general, the rise of data-intensive workloads is driving more organizations to embrace DataOps best practices to optimize the management of massive amounts of data. Organizations that build AI models are typically at the forefront of those efforts given the massive amounts of data typically required to train AI models. The challenge organizations have historically faced is not many have the internal expertise required to manage a vector database. The Zilliz Cloud eliminates the requirement by relying on the expertise of a team of database administrators trained by Zilliz to operate a managed service.
It’s not clear at what rate organizations are embracing vector databases to build AI models. As the next era of applications are constructed, it’s only a matter of time before the machine learning operations (MLOps) practices used to build AI models converge with DevOps practices used to build the applications in which those models will be embedded. The challenge is the data science teams that build AI models tend to also have a distinct DataOps culture all their own; DevOps teams will need to find a way to embrace and extend that.
In the meantime, the number of types of databases being used to build applications will only continue to expand so it will be up to each DevOps team to know when to manage each one themselves versus relying on a managed service.
As crazy as it seems, here we are on the cusp of another Thanksgiving. This year has gone by so quickly, and as we enter the holiday season and the end of 2022, those of us in the DevOps community have much to be thankful for. First and foremost, I’m so happy to once again see my colleagues and friends in-person at conferences and events around the world. I’m looking forward to resuming travel not just in the U.S. but the rest of the world and seeing DevOps enthusiasts share their knowledge, their experiences and further the state of DevOps.
While we are thankful for the resumption of a more normal lifestyle, we also need to pause to acknowledge and mourn for those we’ve lost during this terrible pandemic. Their lives and the time taken from all of us during these past years can never be replaced.
They say every dark cloud has a silver lining; if there was a silver lining for DevOps during the COVID-19 pandemic, it was the unprecedented pace of digital transformation. Make no mistake—digital transformation is powered by software development and DevOps powers software development. It wasn’t just the sheer number of applications or lines of code that were developed and deployed during these past few years, but the accelerated maturity of software supply chain software pipelines that became the de facto standard for how software is developed and deployed; that was greatly accelerated as a result of the push to digital transformation.
It was not just software development that was accelerated, software supply chain attacks gave rise to an entirely new subset of security. Software supply chain security and SBOMs are now everyday words in the security lexicon. While I’m not happy about an entirely new class of attacks, I’m grateful for the response the White House and the U.S. government gave to our industry as a whole to address software supply chain attacks. I’m also grateful for the continued evolution of tools and techniques, people and processes around DevOps.
While some look to make marketing hay by proclaiming that DevOps is dead and other such nonsense, the truth is that DevOps is thriving. It is the way software is developed today. In fact, in conjunction with cloud-native, it would seem that we have a whole new stack and an entirely new way that software is created.
Looking forward, on the horizon we continue to see the development of AI that is making a real impact on observability, testing and application management. I think we’re also on the verge of AI having a profound impact on software development, as well. Low-code/no-code development tools paired with AI promise to take some of the most mundane software development tasks and automate them. This will free up overworked software developers to focus on higher-value software development tasks.
Despite macroeconomic conditions putting a damper on the entire tech space (if not the entire world economy), I remain enthusiastically bullish about the continued growth and maturation of DevOps and I am excited to see what the weeks, months and years ahead bring. I am grateful to be involved in our industry during these heady times, and I’m grateful to you, our readers, supporters and the community for your continued support of our company.
To that end, I wanted to announce that we’re trying something new this year at Techstrong Group, and we hope you’ll join us. We want to know what you believe makes tech strong—and we’re asking people to record a quick TikTok video or LinkedIn video with the hashtag #Techstrong. We’ll select the winners and are giving away two (2) Sony PlayStation5 game consoles to the best two entries as selected by our judges. You can get all the details on our social media channels on Twitter, Facebook and LinkedIn.
So, now you know what I’m grateful for—we really want to hear what you’re grateful for and what you think makes tech strong! Throughout the holidays and the new year, we wish you health and happiness—and know that tech will remain strong!
CircleCI has committed to adding additional collective intelligence capabilities to its continuous integration/continuous delivery (CI/CD) platform that will leverage machine learning and other forms of artificial intelligence (AI) to optimize application development and delivery.
CircleCI CEO Jim Rose said that as the company’s namesake CI/CD platform continues to collect more telemetry data from multiple customers, it becomes feasible to create AI models that will optimize everything from application testing to reliability and performance.
As part of that strategy, CircleCI has also added capabilities such as test splitting recommendations that employ machine learning algorithms to estimate the expected time savings of enabling parallelism on test jobs.
In addition, CircleCI is creating a team of analysts and data scientists to develop additional AI capabilities based on the data collected via the cloud version of the company’s platform.
The decision to invest more in developing AI capabilities follows the acquisition of Ponicode, a provider of a unit testing platform, earlier this year. That platform uses AI to augment application testers by providing capabilities such as syntax validation, on-hover hints and autocompletion capabilities to improve the accuracy of software developed using Visual Studio Code. The goal is to integrate that AI testing technology within the CircleCI platform to make it possible to apply algorithms to individual developers’ code as well as to code aggregated from multiple developers within a build as it is continuously updated across a DevOps workflow.
The workflows make up a software supply chain that, in many regards, functions like any other type of supply chain, noted Rose. As such, there is plenty of opportunity to apply AI models to optimize those workflows in the same way AI is being applied to optimize other types of supply chains, he added.
It’s still early days as far as applying AI to DevOps processes is concerned, but it’s clear there is a need to further automate a wide range of manual processes. It’s not likely that AI is going to replace the need for DevOps engineers any time soon, but it will reduce a lot of the toil that currently hinders productivity. The challenge, of course, is that it takes a lot of telemetry data to train an AI model, which typically can only be efficiently gathered and stored with a cloud platform. Less clear is whether AI capabilities might drive more organizations to shift away from legacy CI/CD platforms deployed in an on-premises IT environment where there might not be enough telemetry data available to effectively train an AI model.
The one thing that is certain is there is now a race to apply AI to DevOps workflows. Organizations that have adopted DevOps typically are highly committed to automation, but the amount of AI applied to those workflows has been limited thus far. In the coming year, however, chances are good the amount of AI being applied to DevOps workflows will steadily increase, especially as many DevOps teams struggle to keep pace with complex application environments.
Welcome to The Long View—where we peruse the news of the week and strip it to the essentials. Let’s work out what really matters.
This week: Data centers cause climate change, and Meta is rolling out Precision Time Protocol. (more…)
Welcome to The Long View—where we peruse the news of the week and strip it to the essentials. Let’s work out what really matters.
This week: Meta’s latest results are very bad, Apple wants its cut of Facebook ads, and Lennart Poettering proposes a new Secure Boot for Linux. (more…)
At the Ansiblefest 2022 conference today, Red Hat and its parent company IBM announced the Project Wisdom initiative to bring natural language processing (NLP) to the open source Ansible IT operations automation platform.
At the same time, Red Hat announced that the curated instances of Ansible from Red Hat are now available in the marketplaces operated by Amazon Web Services (AWS) and Microsoft.
Richard Henshall, senior manager of Ansible product management at Red Hat, said the goal is to make IT automation much more accessible to a broader range of end users and IT professionals that lack the declarative programming expertise to use YAML files to automate IT processes. Instead, Red Hat and IBM Research teams are pledging to combine their expertise to create an artificial intelligence (AI) model that would enable anyone to use natural language to automate an IT process.
Ansible has emerged as the de facto open source standard for automating a wide range of IT processes, but the number of processes that could be automated still far exceeds the number of IT processes that are currently automated. In recent years, IT automation tools have become more accessible thanks in part to the rise of low-code and no-code tools, but some level of programming expertise is still required. Project Wisdom is being launched to eliminate the need to acquire those skills before being able to automate a process.
Of course, there will still be complex processes that may require the ability to programmatically manipulate YAML files, but the number of trivial IT processes that could be automated is still incalculable. In theory, a natural language interface for Ansible would greatly reduce the time and effort required for anyone to automate an IT process regardless of their skill level.
Interest in IT automation is rising once again as the global economy continues to soften. Organizations are looking to automate as many processes as possible in their efforts to keep IT staff size as small as possible. The more time IT teams spend on manual processes the less they will be able to manage IT environments that get more complex with each passing day.
It’s not clear to what degree IT processes can be automated, but as businesses become more dependent on IT, the line between IT automation and business processes continues to blur. Natural language processing capabilities could make it simpler for organizations to greatly accelerate digital business transformation initiatives.
Regardless of the level of IT automation achieved, it’s clear the way IT is managed is about to evolve once again. The days when IT teams configured and provisioned IT resources using fragile custom scripts are coming to an end as platforms like Ansible are more widely adopted. The only issue that remains to be seen is how quickly IT processes can be automated and whether the rate of automation can keep up with the volume of applications currently being developed.
We are in the middle of a data renaissance. Today, it’s not just about data instrumentation but also learning how to make DataOps a real business advantage for the entire organization. Data has become an inextricable part of products, used to enhance the quality of user experiences. Reliable access to data and the integrity of that data is imperative to drive innovation and business success.
But just like any process, problems will arise. There will be pipelines that break or data that isn’t instrumented properly. The question then becomes, how can teams more quickly identify problems and continually improve? How can the journey toward data integrity become one of iteration and improvement?
DataOps teams are by no means strangers to automation—automated CI/CD pipelines and infrastructure-as-code (IaC) are a huge part of their skill set. But there are some components of automation that are less pervasive throughout DataOps, ones that can help promote data integrity and encourage a culture of improvement.
It all starts with a different approach to automation—a human-in-the-loop (HITL) approach. Human-in-the-loop automation enables parts of a process to be fully automated while enabling humans to step in at critical points to take action, make decisions and decide the path forward.
Here are five ways to use human-in-the-loop automation to drive continuous improvement and achieve optimal data integrity.
As is life, inevitably things break, issues arise and teams need to step in to remediate. The goal should be to enable better interruption of these problems when they do occur—identifying issues earlier and bringing the right stakeholders together more easily and swiftly.
HITL automation can help teams not just identify issues earlier but kickstart the incident response and coordinate across teams and stakeholders. Ideally, automated workflows can be triggered from an incoming signal; for instance, from an observability tool like DataDog or BigPanda. An incident response could be started automatically, triggering further automation—like creating tickets, starting a Zoom meeting, creating a Slack channel and bringing together the right team members.
By instantly bringing the right people together, the team can investigate collaboratively and more quickly understand the state of the issue. HITL automation should then enable teams to run further scripts to take action and remediate as needed.
Many companies today are asking, “How do we make sure that we have that full integrated loop, down to the collection point?” Communication between teams is critical to this endeavor. But even before teams can collaborate on improvements, they need to understand the current state of their data so that if, for instance, a team finds that a report isn’t providing what’s needed, there’s a really clean way to get that communication back to the DataOps team to improve the instrumentation in the first place.
HITL automation can bake improvement into DataOps workflows by automatically documenting every human and machine action throughout a process. Because it’s connected through APIs to all a team’s tools and services, a self-documenting workflow slurps up every action and creates an audit trail. This audit trail is the basis by which teams can more accurately understand not just the integrity of their data, but also how teams are implementing and running processes.
DataOps teams play a big role in making sure the right data is being collected. However, a lot of people are still using systems that are very manual. They may have a good understanding of their log and monitoring data but still don’t have a good sense of the human data around their organization—what processes and workflows people are running.
Understanding how humans run processes is just as important as understanding technology problems. The self-documenting workflow described above should include documentation of human data, from manually-run scripts to Slack or Teams conversations and, ideally, even auto-update tickets so even maintaining a system of record becomes less manual. Only by looking at the full picture—of human and machine data together—can teams find areas of improvement in their automation, communication and workflows.
Don’t let “perfect” be the enemy of “done”. Every great transformation I’ve seen started with a small team, a great mandate, and strong support from the executive team. Adding tons of new technology and hiring more people may not be the silver bullet teams need.
Instead, teams should look at the expertise they have in-house first—the people, technology and processes. Then, begin by taking an incremental approach. HITL promotes incremental, approachable automation by enabling teams to first codify institutional knowledge, analyze, learn and then begin to automate pieces in small batches.
Diving head-first into a fully automated approach can leave teams in a worse state than nothing at all. Automation is a journey—we learn by doing, by recording human and machine data and then, using insights as our guide, begin to find real value in small doses of automation. These small steps can make a huge impact and, over time, add up to substantial business value.
As I mentioned above, teams should first start by evaluating their current resources. What skill sets and technology do they already have and how can they use those resources to achieve their data goals? At the same time, many teams starting their automation journey face challenges—lack of development resources, scripts that live on one person’s machine, difficulty bringing all the human and machine data together into a recorded timeline.
To help data teams effectively and reliably implement HITL automation, investment into an off-the-shelf platform can help bridge the gaps teams and organizations have. Platforms should enable automation through low-code interfaces while providing code-level customization. This combination enables teams to buy for industry standards while building for the gaps—not managing the platform means development resources can be focused on expanding and improving automation.
Platforms should enable teams to easily connect to APIs and adapt to custom APIs. The continuous change of DevOps processes and tooling coexist with rapidly changing APIs. Therefore, the most promising automation platforms must manage the complexity of APIs, enabling users to focus on the intent, not the mechanics of custom APIs. Automation technologies should seamlessly handle identity, authentication, pagination and caching.
As data becomes increasingly ingrained in product life cycles and user experience, automation holds the key to faster innovation, product quality and enhanced reliability. Creating a culture of continuous improvement and implementing technology like HITL automation that promotes data integrity throughout the product life cycle is where I’ve seen the greatest leverage for organizations.
To hear more about cloud-native topics, join the Cloud Native Computing Foundation and the cloud-native community at KubeCon+CloudNativeCon North America 2022 – October 24-28, 2022.
The gaming industry has exploded since the start of the COVID-19 pandemic. Many people turned to social and casual gaming to entertain themselves at home and interest is expected to continue to rise. According to PwC, the global gaming industry is forecast to increase revenue from $214 billion in 2021 to $321 billion by 2026. With a booming and competitive market, gaming companies must deliver a seamless experience to their end users. A recent survey found that 47% of gamers demanded fast performance, citing it as the most important aspect of gameplay. If users encounter poor performance such as glitchy graphics, crashes or freezes, long load times and in-game purchasing errors, they’ll move on—damaging the company’s brand and revenue. Companies need to have the right technology in place before things go wrong. Here are five strategies game developers must focus on to provide the best gaming experience for end users.
Gaming is a demanding application for developers. Instant response time—delivering processing speeds measured in milliseconds—is required for any gaming experience, even when there are substantial usage spikes. It can be a challenge as developers have to account for vast numbers of reads and writes against massive data volumes from millions of simultaneous players interacting in real time and sophisticated fraud detection rules.
Personalization helps a game attract a broader audience by offering in-game features that match their preferences. It is an essential way for game developers to retain and monetize their users. With so many different games available for users to play, engaging them for a long time will determine a game’s success. To offer this type of personalized experience, developers need to integrate data silos from different sources, where they continuously update all available historical data from their system of record with data streaming in from the digital edge.
Gaming applications need to store, access and retrieve enormous amounts of data, such as player profile information, game performance, statistics and rankings and session information, for millions of users. Developers need to be assured their system can scale out easily and quickly to handle any load at any time, without the need for coding or other manual processes. Relational databases aren’t the best fit for gaming as they are built to scale vertically. On the other hand, modern data platforms scale horizontally across servers, clusters, and data centers to ensure consistent and predictable performance.
As companies seek ways to monetize gaming through microtransactions, where they offer in-game purchases for additional features, the threat of malicious activity increases. According to TransUnion’s 2022 Global Digital Fraud Report, the U.S. gaming industry had the largest percentage of annual fraud growth last year, increasing 60.3% from the previous year. With more players and financial transactions, companies must take their fraud prevention activities to the next level to ensure smooth and secure transactions. Developers need to continuously update the rules engines for their fraud detection systems. They can do this by processing huge amounts of streaming and historical data that they can feed into machine learning algorithms.
Gaming companies should turn to artificial intelligence (AI) and machine learning (ML) systems to improve the gaming experience for their players. The technology can power interesting enhancements to games. For example, it can be used to turn speech into text to document and report bad behavior or to support an in-game assistant that helps players based on their habits, preferences, and abilities. To provide these experiences, developers need to feed their AI/ML systems an unending amount of data to deliver better results.
The stakes for gaming companies have never been higher. With the gaming market expected to reach three billion global users by 2023, companies must provide the best gaming experience to gain players and protect their reputations. Players demand flexibility, speed and reliable performance. That means delivering smooth gameplay with no hiccups.
Guljeet Nagpaul, Chief Product Officer for ACCELQ, explains why automating testing has become a necessity. The video is below followed by a transcript of the conversation.
Mike Vizard: Hey, guys. Thanks for the throw. We’re here with Guljeet Nagpaul, who is chief product officer for ACCELQ, and we’re talking about all things related to testing. Guljeet, welcome to the show.
Guljeet Nagpaul: Thanks, Mike. Thanks for having me.
Mike Vizard: Do you think the whole testing focus has increased somewhat in the last few months with the whole shift to digital transformation, and people are trying to develop more applications than ever? I think it’s kind of critical to get it right the first time these days. I think the tolerance for experimenting on end users, especially in a digital business context, is a lot lower, but is there some sort of fundamental shift going on with testing that you’re seeing?
Guljeet Nagpaul: Yeah, both things you mentioned, Mike, are right on. The digital transformation has clearly been on steroids almost now for the past couple of years, since we’ve seen the world kind of changing around us with remote work. A lot of processes which were even in healthcare and financial space, which were kind of buried with red tape, all of them have been kind of almost on steroids to become more automated, and organizations have had to kind of put their transformation objectives much more on a fast track. That, like you rightly said, automatically leads to having to ship the applications must faster to their customers to run their businesses faster. The need for automated testing for shipping their software faster gets kind of even more critical.
Mike Vizard: Where is that automation taking place? Are we automating tests for the developer as we shift things left, or is the automation more in the CI/CD DevOps platform, and we’re trying to automate it there because that may be the place where the application itself comes together; otherwise, each developer is testing a small piece of something but maybe not the whole thing and then?
Guljeet Nagpaul: That’s exactly right. We live in a world where there is an app for everything. That has been true from a consumer perspective, but now, interestingly, that is more so true on our enterprise application side. Back in the day, if you remember, we had a couple of ERPs and then we had some homegrown systems, and that’s kind of how the IT landscape of organizations looked. But now, with the onset of cloud apps, with packaged applications, with vertical applications, there is an app for everything for enterprises to be able to go more automated. What that translates to is a highly-integrated enterprise application ecosystem. What that means is unit testing or just shifting left in terms of automation is not going to be enough.
You would really need that end-to-end validation, and each app has at least five upstream and five downstream integrations, which means the real automation, and the real customer experience in terms of your end-to-end workflow has to be automated across the board. That DevOps CI/CD pipeline you talked about, that gets even more kind of focused in terms of being able to validate that end-to-end workflow. That is kind of where we are really seeing the need for automation, more than kind of what we would have thought from a shift-left perspective. That’s absolutely necessary because you want to build a stronger product. You don’t want to be reactive in building a strong product, you want to bake quality into the product, but then downstream, you want to make sure your end-to-end workflows work as designed in terms of an integrated end-to-end system.
Mike Vizard: How far down the path are we to achieving that goal, because I think somewhere along the line everybody maybe thinks that testing is somebody else’s job, but what’s the maturity level of testing these days in terms of automation?
Guljeet Nagpaul: Yeah, like with a few different functions within our IT, we kind of see kind of a full circle come back, right? A lot of testing was starting to get perceived as commoditized, and we saw a lot of testing kind of getting shifted from that perspective as a cost function. But, quickly, organizations realized that without continuous testing, there is no continuous delivery, and that’s really the crux of it, that if you can’t have a dependable, reliable, continuous test automation to bridge that gap between your dev and ops, there is no DevOps, because that’s the key bridge between your dev and ops, right? With that notion, the testing maturity level has struggled.
To be honest, a lot of automation approaches, and I spent over a decade with HP, a lot of traditional automation tools and approaches have focused more on silos because our app ecosystem was not that integrated back in the day, right? It used to kind of have integration, but not at the level that you’re seeing today, so the need for an automation approach that can span across these technology stacks is now kind of more new and is now more critical. The testing maturity in terms of what approaches are out there has struggled, but we are now starting to see customers really needing that across-technology-stack approach, and that’s kind of where ACCELQ has a sweet spot.
Mike Vizard: Do you think the rise of micro-services is forcing the automated testing issue because we’re reaching a level of complexity that can’t really manually test all these things?
Guljeet Nagpaul: I would say yes. Just in the last five years, interestingly, Mike, we have seen API testing as part of critical proof-of-concepts to when customers come and try a platform like ACCELQ out in terms of seeing whether it’s fit for their needs or not. Just in the last five years, we have seen at least three times the interest in making sure that APIs can be validated apart from their UI. But, like I said, it’s really more about how that validation can be done across the technology stacks. To give you an example, a healthcare customer of ours, one of the top five companies in the world, they had a use case where a patient walks into a pharmacy to put in their prescription, but now with COVID and everything, the automation is kind of boosted.
Now they can key in their prescriptions directly through their mobile apps connected to their healthcare providers. Once that prescription is keyed in, there are at least 20 downstream processes that are kicked in, including mainframe systems, micro-services, a database that’s maybe sitting on a cloud, but then coming back to a pharmacy system which is still living in an old-school desktop world. The real demand, what we are seeing is kind of this across technology side, because that’s what the user experience is.
Mike Vizard: How smart can testing get? We hear a lot about artificial intelligence these days. Will the whole thing become automated or what will be the role of the tester, per se?
Guljeet Nagpaul: Yeah, that’s a great question. AI unfortunately gets thrown around very loosely these days, especially more so with talking about how AI is the platform. We have looked at it always as a tool in our toolkit. Ever since the inception of our platform, we have always looked at how we can leverage AI to make things more efficient as opposed to saying AI is the platform. We have always looked at it more realistically. If you forget AI for a second, that’s kind of how as people we have embraced technology. In various aspects, technology has enabled us to increase our throughput regardless of which process area are we picking, and that is kind of how we see AI. If it’s leveraged accurately, it’s going to increase the throughput of our test engineers. It is going to increase the throughput of our automation engineers. At various process areas that then test automation life cycle, that’s kind of how we’ve looked to leverage AI.
Mike Vizard: Do you think we’re approaching a point where the number of applications being built is overwhelming the testers that are out there, and the pace of that work is getting to a point where they become a bottleneck?
Guljeet Nagpaul: Yeah, I was having a discussion on the same lines with our Forrester analysts, and one of the interesting discussion points around that was we all as testers within the DevOps space have to almost take a step back and understand what needs to be tested before going about kind of in the traditional manner testing it. Yes, it can be overwhelming with so many apps coming together so quickly in terms of being able to serve our businesses, but a lot of these apps which are getting shipped for our businesses, for these organizations are cloud apps, or maybe packaged apps, vertical apps. All they need is configuration and integration with other apps, and then they can kind of start doing the job in terms of the outcome that the businesses are expecting. It almost needs some level of refactoring, a new mindset when you think about in this new world of facts, how should you be testing and what you should be testing to get the maximum bang for your buck, so it’s easy to get overwhelmed. But, with automation and the right approach to what needs to be tested, you can kind of be more efficient with your efforts.
Mike Vizard: Who writes the tests these days? It seems to me a lot of time and effort has been spent on creating the tests, and, as we automate that process, can we get to the point where the machines can create the tests? I mean, to what degree do I need to create the tests versus run the tests?
Guljeet Nagpaul: Yeah, that’s an interesting point. The way we look at it is, unfortunately, the way testing and automation specifically has evolved, the traditional approaches I talked about, they have lived on two ends of the spectrum, Mike, one end of the spectrum being very record-and-playback oriented. But, we have seen in the past 20 years that automation is not new; it has been around for at least a couple decades. Back when we released Windrunner, and I don’t know if that rings a bell, I was part of the original Windrunner team, at one point we had about 70 percent market share, and record-and-playback was the thing automation. But, in the last ten years, there have been a plethora of tools which have come on the same lines of record-and-playback but we have seen that they just are not scalable enough to deal with the changing technology stack. They are just not real-world enough to work across technology stacks, and a lot of other things in terms of design challenges and so on.
The other end of the spectrum that we live in is open source, and you kind of pointed to how testing had been shipped out as a cost function, and open source made a big kind of wave into the test automation area, but that became a specialist function. The reason this is important, to your point of the question, is when automation has become a specialist function, because we have seen about 60 to 65 percent market share ‒ Gartner did a survey of open source taking over a lot of kind of testing and automation approaches. But, when you see that, basically it becomes a specialized function, and all my testing team is a mixed bag of testers, maybe some automation engineers, but a lot of business subject matter expertise. If you really want a strong test approach, you need to make the subject matter experts people who understand business rules.
That needs to be validated as part of the automation, and that’s kind of where both ends of the spectrum have to meet, where a non-technical, non-specialized tester should also be able to be part of that automation. That can only become if our platforms are smarter, right? Maybe depending on that suite to be adaptable, maybe bridging machine in certain cases, to your point, to be able to write smarter test cases, to basically have your automation more leaner and meaner so that it can do more with kind of less in terms of authoring and design changes and change with so many releases happening, so absolutely. I mean, testing has to get out of the specialized function role.
Mike Vizard: All right. We hear a lot of focus these days on security, of course. Do you think that security ultimately will become part of a quality assurance testing process, because it’s not clear to me that those are two distinct functions anymore. They might be joined at the hip in some fashion.
Guljeet Nagpaul: I sure hope so. The strongest of the software and the organizations just in the past one year, we have seen outages which we would have never imagined, right? AWS went out a few times, Facebook, Meta, went out a few times, and you name it. Security has to become an integral part, and we are seeing this more and more. As part of functional regression tests, customers want to have those security regression tests. Security lived in two worlds until now. One was static code analysis, which is, “Let’s do static code analysis,” which is good, it’s kind of like shifting left to your unit tests. Yes, you’ve got to find your vulnerabilities right when your code is written, but, once the code is in action, what vulnerabilities come can be a different game.
Static code analysis is where security lived, and, like you said, that’s why it was a little bit in the different world. Then, you had dynamic security firewalls, which are kind of more glorified firewalls. But, in the middle, that functional testing, leveraging security validation is a gap in the industry right now, and we are certainly looking to fill that gap. We have already helped a lot of Fortune 100 customers, including certain financial services customers, fill in that gap, leveraging the functional automation approach because they’ve had some solutions around these two things but functional is where it needs to get seated into, right? Like you said, it has to become mainstream into the testing and function automation space.
Mike Vizard: All right. Other than hanging out on your website, what is your best advice to folks about how to get to this continuous testing nirvana, I guess, because, ultimately, time is money, so every time an application has a disjoined experience, somebody is losing money somewhere, right?
Guljeet Nagpaul: Totally. Automation first. Automation first is the key. A lot of traditional mindset still remains at, “Let’s write my manual tests and let’s write the manual tests them into convert them into automation.” The mindset has to change. The organization approach has to change to automation first. For automation first, you can only do automation first if it’s not a specialist function. Don’t get caught up in getting buried into a technical debt. You don’t want a million lines of code to test a million lines of code, right? Figure a more robust solution and go automation first, and that will set you on the right path.
Mike Vizard: All right. Guljeet, thanks for being on the show.
Guljeet Nagpaul: Thanks, Mike. Thanks for having me.
Mike Vizard: All right. Back to you guys in the studio.