How Low-Code Enables Continuous Testing in DevOps

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As an industry, we understand that effective test automation is a key enabler - or inhibitor to - realizing the potential of DevOps. While automation is critical to innovating with speed and quality, very few of us are happy with the results. This talk will cover how low-code test automation solutions - like mabl - enable teams to embed automated tests directly into the development pipeline, strategies to overcome traditional challenges with test automation, and how to build a foundation for an efficient and effective test strategy.

This talk has been presented at TestJS Summit 2021, check out the latest edition of this JavaScript Conference.

FAQ

Juliet McVail emphasizes that test automation is central to any successful DevOps effort, essential for continuous testing and ensuring high-quality software deployments.

Low-code automation helps by breaking down silos and enabling more team members, regardless of coding expertise, to participate in quality assurance, thereby enhancing test creation, execution, and overall software quality.

Auto healing in test automation refers to the ability of a system to automatically update tests based on detected changes in the application, such as a changed ID for a button, ensuring tests remain effective without manual updates.

Yes, existing Selenium tests can be imported into Mable, allowing teams to leverage previous work and integrate it with Mable's intelligent test execution capabilities.

Involving multiple team roles in test automation, such as developers, manual testers, and product owners, helps ensure comprehensive test coverage and fosters a collaborative approach to quality, which is crucial for agile and DevOps environments.

Mable supports 'shifting left' by providing tools such as a command-line interface and CI runner that enable teams to execute tests early in the development lifecycle, allowing for quicker feedback and earlier defect detection.

Juliette MacPhail
Juliette MacPhail
31 min
18 Nov, 2021

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Video Summary and Transcription

Today's Talk discusses how Low Code enables continuous testing and DevOps, emphasizing the importance of test automation and the drawbacks of siloed approaches. The next era of quality engineering aims to overcome automation challenges by incorporating machine learning and intelligent automation. The development process involves local testing, pull requests, and comprehensive testing to ensure quality before merging. Low-code tools like Mable help democratize testing and achieve higher test coverage. Mable's coverage report includes performance metrics and test results, making testing easy and accessible for any team member.

1. Introduction to Low Code and DevOps

Short description:

Today we'll be talking about how Low Code enables continuous testing and DevOps. Test automation is central to succeeding in DevOps. Siloed approaches don't work. Low-code automation gets rid of silos and produces better results in quality engineering efforts.

Hi, everyone. Welcome. Today we'll be talking about how Low Code enables continuous testing and DevOps. It is so great to be here with you all today. My name is Juliet McVail, and I'm a Product Manager at Navel, which is an intelligent low-code test automation solution. I've been with Navel for about 2 1⁄2 years now, and I'm currently the Product Manager for our browser and API testing team. And that really focuses on test creation and execution across both browser and API tests.

So in this talk, I'm going to be focusing on three key points. The first is that test automation is really central to any effort in order to succeed in DevOps. The second is an assertion that siloed approaches to test automation don't work. And finally, that low-code automation enables us to get rid of these silos and produce better results in our quality engineering efforts. So let's dive in.

2. Quality Engineering and Test Automation

Short description:

Quality engineering is an enabler for key trends in software development. Test automation is crucial for deploying changes with confidence. However, very few teams have achieved the necessary level of automation. Without it, there is a risk of bottleneck and limited capacity to verify changes. The next era aims to overcome these challenges by building intelligence into the automation process.

So first and foremost, there's really such an exciting time to be someone who's focused on quality engineering because there are so many critical trends in the industry. And we're also realizing that quality engineering plays a critical role in this as well for enabling innovation. And whether you're looking to broaden your adoption of agile or most DevOps, perhaps your team wants to migrate to the cloud or shift left. Quality engineering is really ultimately an enabler for all of these key trends.

And effectively, what we're trying to do in software today is accelerate the pace of innovation with quality. We really want high velocity and throughput in these pipelines, and we want to be able to create and deploy changes constantly. So whether that's through code or configuration or upgrading, or even dealing with a change that's happening with your integrated partners, because you likely consume a lot of services via API from third parties. And so we want to be able to embrace that change with velocity and throughput. And now that also needs to be under the watchful eye of a system that can ensure quality. And that's really where test automation comes into play. And we know that we're successful in implementing test automation when we have a high-level automation, but we also have high confidence in our ability to deploy changes with good quality.

So this is really an interesting point. And it's also the problem that we saw in last year's DevOps report. We know that we have a low level of, when we have a low level of test automation, we also have relatively little confidence in being able to deploy changes. And as that level of testing and deployment automation increases, we can see that confidence increases as well. And this is really key as you move towards continuous testing model. Because despite the fact that we know we want to get to this high level of automation, so we're confident in dealing with all of that change, very few teams have realized the level of automation necessary to deploy with confidence. And we have work to do to get there. And really, the risk here is that if we don't, is that we don't realize this vision of those high-velocity, high-quality pipelines. Because what we have to do here is slow down the throughput in order to manage quality. And that means we have limited capacity that would be able to verify those changes from a QA perspective. And so this is the risk that we end up having this bottleneck, despite the fact that there's been so much innovation on this side. We're not actually able to have that throughput. And so this next era really tries to overcome that. And this all starts with the assertion that if you want to automate a process, you have to build intelligence into it. And we know this intuitively and places outside of test automation. So for example, if I wanted to build a self-driving car, I wouldn't just say, OK, I'm going to build the engine, give it a set of instructions, and then just go have a drive, which is effectively what we've done historically with test automation. You would really recognize intuitively that the car has to have a lot of sensors and a lot of data. We need to use a GPS. And then you have to actually be able to read what's happening in real time when you're out on the road.

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