Software development engineers in test (SDETs) have historically relied on coded test automation as the go-to approach for quality assurance. However, coded test automation calls for extensive coding that’s resource-intensive and challenging to maintain. Although it’s based on free, open-source frameworks, coded test automation requires skilled labor that’s scarce and costly — constraints that hamstring overburdened tech teams. 

Fortunately, not all testing requires coded automation. New advancements in test automation are emerging, and codeless platforms present a key opportunity to streamline software testing.

Coded automation not the only option 

Coded test automation still plays an important role in scenarios like unit testing and component-level testing. But the development arena has changed in the last 20 years, underscoring the fact that coded test automation isn’t an optimal approach to quality assurance for certain use cases — like functional testing.

Coded test automation requires skilled SDETs or software developers to not only write hundreds of lines of code, but also maintain them. That’s increasingly difficult to accomplish with engineers stretched thin and employers facing ongoing talent shortages. As a result, many development teams lack the resources to maintain copious amounts of code once an application is deployed. Supporting code for coded test automation is also expensive, especially if the test framework requires regular updates or modifications.

It’s clear that new testing approaches are needed to maintain software quality and keep pace with technological advancements. And codeless test automation is gaining momentum — fast. 

Revolutionize testing with codeless automation

Codeless automated testing platforms are now available in the commercial marketplace, eliminating the need to write code for automated tests. With these tools, quality assurance (QA) professionals who lack coding skills can develop automated tests alongside SDETs and developers.

Some developers may hesitate to lean on codeless automation. After all, many developers have spent the lion’s share of their careers writing lines of code. But coded test automation isn’t going away — it’s just becoming one of several approaches developers can turn to. In fact, coded automation remains critical in many testing scenarios. 

However, for functional testing, end-to-end testing, data validation, and regression testing, codeless platforms offer a streamlined approach for both user interface (UI) and application programming interface (API) testing that can cut costs and reduce time-to-market.

Consider the benefits that codeless automation can provide:

• Reduced reliance on technical expertise: Codeless testing platforms enable developers to shift testing responsibilities to QA teams, who can focus solely on testing rather than coding and debugging. Codeless platforms also help free up developers’ time and empower them to focus on new technologies and complex software development.
• Accelerated development cycles: Codeless platforms enable QA teams to use pre-built and visual components to develop automated tests, which is a much faster process than writing net-new code. This enables testers to create more test cases in a fraction of the time, which increases test coverage and results in higher quality software. An added bonus? Shorter development cycles also reduce costs.
• Easier maintenance: Codeless testing eliminates the need for programming skills that are typically required to maintain and update coded test suites. This makes maintenance faster and easier when an application changes. Some codeless automation platforms even have self-healing capabilities that enable the testing tool to automatically fix test scripts or test cases when a test fails or the software changes.

There’s always a learning curve when adopting a new approach. But the barrier to entry is low and the rewards are high when it comes to deploying codeless test automation tools. In the current no- and low-code era, the swift pace of innovation demands agile and efficient workflows.

Consider all the factors when determining whether codeless automated testing is right for a specific use case, from resource availability to the category of testing required. But when you discover codeless is the right fit for a use case, your entire team can test faster with greater efficiency and coverage — ultimately reducing time-to-market for new products while maintaining product quality.

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Recent research conducted by Forrester and commissioned by Keysight found that while only 11% of respondents had fully automated testing, 84% percent of respondents said that the majority of testing involves complex environments. 

For the study, Forrester conducted an online survey in December 2021 that involved 406 test operations decision-makers at organizations in North America, EMEA, and APAC to evaluate current testing capabilities for electronic design and development and to hear their thoughts on investing in automation.

The complexity of testing has increased the number of tests, according to 75% of the respondents. Sixty-seven percent of respondents said the time to complete tests has risen too.

Challenges with automated testing 

Those that do utilize automated testing often have difficulty making the tests stable in these complex environments, according to Paulina Gatkowska, head of quality assurance at STX Next, a Python software house. 

One such area where developers often find many challenges is in UI testing in which the tests work like a user: they use the browser, click through the application, fill fields, and more. These tests are quite heavy, Gatkowska continued, and when a developer finishes their test on a local environment, sometimes it fails in another environment, or only works 50% times, or a test works the first week, and then starts to be flaky. 

“What’s the point of writing and running the tests, if sometimes they fail even though there is no bug? To avoid this problem, it’s important to have a good architecture of the tests and good quality of the code. The tests should be independent, so they don’t interfere with each other, and you should have methods for repetitive code to change it only in one place when something changes in the application,” Gatkowska said. “You should also attach great importance to ‘waits’ – the conditions that must be met before the test proceeds. Having this in mind, you’ll be able to avoid the horror of maintaining flaky tests.”

Then there are issues with the network that can impede automated tests, according to Kavin Patel, founder and CEO of Convrrt, a landing page builder. A common difficulty for QA teams is network disconnection, which makes it difficult for them to access databases, VPNs, third-party services, APIs, and certain testing environments, because of shaky network connections, adding needless time to the testing process. The inability to access virtual environments, which are typically utilized by testers to test programs, is also a worry. 

Because some teams lack the expertise to implement automated testing, manual testing is still used as a correction for any automation gaps. This creates a disconnect with the R&D team, which is usually two steps ahead, according to Kenny Kline, president of Barbend, an online platform for strength sports training and nutrition.

“To keep up with them, testers must finish their cycles within four to six hours, but manual testing cannot keep up with the rate of development. Then, it is moved to the conclusion of the cycle,” Kline said. “Consequently, teams must include a manual regression, sometimes known as a stabilization phase, at the end of each sprint. They extend the release cadence rather than lowering it.”

Companies are shifting towards full test automation 

Forrester’s research also found that 45% of companies say that they’re willing to move to a fully automated testing environment within the next three years to increase productivity, gain the ability to simulate product function and performance, and shorten the time to market. 

The companies that have implemented automated testing right have reaped many rewards, according to Michael Urbanovich, head of the testing department at a1qa, an international quality assurance company. The ones relying on robotic process automation (RPA), AI, ML, natural language processing (NLP), and computer vision for automated testing have attained greater efficiency, sped up time to market, and freed up more resources to focus on strategic business initiatives. RPA alone can lower the time required for repetitive tasks up to 25%, according to research by Automation Alley. 

For those looking to gain even more from their automation initiatives, a1qa’s Urbanovich suggests looking into continuous test execution, implementing self-healing capabilities, RPA, API automation, regression testing, and UAT automation. 

Urbanovich emphasized that the decision to introduce automated QA workflows must be conscious. Rather than running with the crowd to follow the hype, organizations must calculate ROI based on their individual business needs and wisely choose the scope for automation and a fit-for-purpose strategy. 

“To meet quality gates, companies need to decide which automated tests to run and how to run them in the first place, especially considering that the majority of Agile-driven sprints last for up to only several weeks,” Urbanovich said. 

Although some may hope it were this easy, testers can’t just spawn automated tests and sit back like Paley’s watchmaker gods. The tests need to be guided and nurtured. 

“The number one challenge with automated testing is making sure you have a test for all possibilities. Covering all possibilities is an ongoing process, but executives especially hear that you have automated testing now and forget that it only covers what you actually are testing and not all possibilities,” said David Garthe, founder of Gravyware, a social media management tool. “As your application is a living thing, so are the tests that are for it. You need to factor in maintenance costs and expectations within your budget.” 

Also, just because a test worked last sprint, doesn’t mean it will work as expected this sprint, Garthe added. As applications change, testers have to make sure that the automated tests cover the new process correctly as well. 

Garthe said that he has had a great experience using Selenium, referring to it as the “gold standard” with regard to automated testing. It has the largest group of developers that can step in and work on a new project. 

“We’ve used other applications for testing, and they work fine for a small application, but if there’s a learning curve, they all fall short somewhere,” Garthe said. “Selenium will allow your team to jump right in and there are so many examples already written that you can shortcut the test creation time.”

And, there are many other choices to weave through to start the automated testing process.

“When you think about test automation, first of all you have to choose the framework. What language should it be? Do you want to have frontend or backend tests, or both? Do you want to use gherkin in your tests?,” STX Next’s Gatkowska said. “Then of course you need to have your favorite code editor, and it would be annoying to run the tests only on your local machine, so it’s important to configure jobs in the CI/CD tool. In the end, it’s good to see valuable output in a  reporting tool.”

Choosing the right tool and automated testing framework, though, might pose a challenge for some because different tools excel at different conditions, according to Robert Warner, Head of Marketing at VirtualValley, a UK-based virtual assistant company.

“Testing product vendors overstate their goods’ abilities. Many vendors believe they have a secret sauce for automation, but this produces misunderstandings and confusion. Many of us don’t conduct enough study before buying commercial tools, that’s why we buy them without proper evaluation,” Warner said. “Choosing a test tool is like marrying, in my opinion. Incompatible marriages tend to fail. Without a good test tool, test automation will fail.”

AI is augmenting the automated testing experience

In the next three years 52% of companies that responded to the Forrester report said they would consider using AI for integrating complex test suites.

The use of AI for integrated testing provides both better (not necessarily more) testing coverage and the ability to support agile product development and release, according to the Forrester report.

Companies are also looking to add AI for integrating complex test suites, an area of test automation that is severely lacking, with only 16% of companies using it today. 

a1qa’s Urbanovich explained that one of the best ways to cope with boosted software complexity and tight deadlines is to apply a risk-based approach. For that, AI is indispensable. Apart from removing redundant test cases, generating self-healing scripts, and predicting defects, it streamlines priority-setting. 

“In comparison with the previous year, the number of IT leaders leveraging AI for test prioritization has risen to 43%. Why so?” Urbanovich continued, alluding to the World Quality Report 2021-2022. “When you prioritize automated tests, you put customer needs FIRST because you care about the features that end users apply the most. Another vivid gain is that software teams can organize a more structured and thoughtful QA strategy. Identifying risks makes it easier to define the scope and execution sequence.”

Most of the time, companies are looking to implement AI in testing to leverage the speed improvements and increased scope of testing, according to Kevin Surace, CTO at Appvance, an AI-driven software testing provider. 

“You can’t write a script in 10 minutes, maybe one if you’re a Selenium master. Okay, the machine can write 5,000 in 10 minutes. And yes, they’re valid. And yes, they cover your use cases that you care about. And yes, they have 1,000s of validations, whatever you want to do. And all you did was spend one time teaching it your application, no different than walking into a room of 100 manual testers that you just hired, and you’re teaching them the application: do this, don’t do this, this is the outcome, these are the outcomes we want,” Surace said. “That’s what I’ve done, I got 100 little robots or however many we need that need to be taught what to do and what not to do, but mostly what not to do.”

QA has difficulty grasping how to handle AI in testing 

Appvance’s Surace said that the overall place of where testing needs to go is to be completely hands off from humans.

“If you just step back and say what’s going on in this industry, I need a 4,000 times productivity improvement in order to find essentially all the bugs that the CEO wants me to find, which is find all the bugs before users do,” Surace said. “Well, if you’ve got to increase productivity 4,000 times you cannot have people involved in the creation of very many use cases, or certainly not the maintenance of them. That has to come off the table just like you can’t put people in a spaceship and tell them to drive it, there’s too much that has to be done to control it.”  

Humans are still good at prioritizing which bugs to tackle based on what the business goals are

because only humans can really look at something and say, well, we’ll just leave it, it’s okay, we’re not gonna deal with it or say this is really critical and push it to the developers side to fix it before release, Surace continued. 

“A number of people are all excited about using AI and machine learning to prioritize which tests you should run, and that entire concept is wrong. The entire concept should be, I don’t care what you change in application, and I don’t understand your source code enough to know the impacts and on every particular outcome. Instead, I should be able to create 10,000 scripts and run them in the next hour, and give you the results across the entire application,” Surace said. “Job one, two, and three of QA is to make sure that you found the bugs before your users do. That’s it, then you can decide what to do with them. Every time a user finds a bug, I can guarantee you it’s in something you didn’t test or you chose to let the bug out. So when you think about it, that way users find bugs and the things we didn’t test. So what do we need to do? We need to test a lot more, not less.”

A challenge with AI is that it is a foreign concept to QA people so teaching them how to train AI is a whole different field, according to Surace. 

First off, many people on the QA team are scared of AI, Surace continued, because they see themselves as QA people but really have the skillset of a Selenium tester that writes Selenium scripts and tests them. Now, that has been taken away similar to how RPA disrupted many industries such as customer support and insurance claims processing. 

The second challenge is that they’re not trained in it.

“So one problem that we see that we have is you explain how the algorithms work?,” Surace said. “In AI, one of the challenges we have in QA and across the AI industry is how do we make people comfortable that here’s a machine that they may not ever be able to understand. It’s beyond their skillset to actually understand the algorithms at work here and why they work and how neural networks work so they now have to trust that the machine will get them from point A to point B, just like we trust the car gets from point A to point B.”

However, there are some areas of testing in which AI is not as applicable, for example, in a form-based application where there is nothing else for the application to do than to guide you through the form such as in a financial services application. 

“There’s nothing else to do with an AI that can add much value because one script that’s data-driven already handles the one use case that you care about. There are no more use cases. So AI is used to augment your use cases, but if you only have one, you should write it. But, that’s few and far between and most applications have hundreds of 1,000s of use cases perhaps or 1,000s of possible combinatorial use cases,” Surace said. 

According to Eli Lopian, CEO at Typemock, a provider of unit testing tools to developers worldwide, QA teams are still very effective at handling UI testing because the UI can often change without the behavior changing behind the scenes. 

“The QA teams are really good at doing that because they have a feel for the UI, how easy it is for the end user to use that code, and they can see the thing that is more of a product point of view and less of doesn’t work or does it not work point of view, which now is really it’s really essential if you want to an application to really succeed,” Lopian said. 

Dan Belcher, the co-founder at mabl, said that there is still plenty of room for a human in the loop when it comes to AI-driven testing. 

“So far, what we’re doing is supercharging quality engineers so human is certainly in the loop, It’s eliminating repetitive tasks where their intellect isn’t adding as much value and doing things that require high speed, because when you’re deploying every few minutes, you can’t really rely on a human to be involved in that in that loop of executing tests. And so what we’re empowering them to do is to focus on higher level concerns, like do I have the right test coverage? Are the things that we’re seeing good or bad for the users?,” Belcher said.

AI/ML excels at writing tests from unit to end-to-end scale

One area where AI/ML in testing excels at is in unit testing on legacy code, according to Typemock’s Lopian.

“Software groups often have this legacy code which could be a piece of code that maybe they didn’t do a unit test beforehand, or there was some kind of crisis, and they had to do it quickly, and they didn’t do the test. So you had this little piece of code that doesn’t have any unit tests. And that grows,” Lopian said. “Even though it’s a difficult piece of code, it wasn’t built for testability in mind, we have the technology to both write those tests for those kinds of code and to generate them in an automatic manner using the ML.”

The AI/ML can then make sure that the code is running in a clean and modernized way. The tests can refactor the code to work in a secure manner, Lopian added. 

AI-driven testing is also beneficial for UI testing because the testers don’t have to explicitly design the way that you reference things in the UI, you can let the AI figure that out, according to mabl’s Belcher. And then when the UI changes, typical test automation results in a lot of failures, whereas the AI can learn and improve the tests automatically, resulting in 85-90% reduction in the amount of time engineers spend creating and maintaining tests with AI. 

In the UI testing space, AI can be used for auto healing, intelligent timing, detecting visual changes automatically in the UI, and detecting anomalies and performance. 

According to Belcher, AI can be the vital component in creating a more holistic approach to end-to-end testing. 

“We’ve all known that the answer to improving quality was to bring together the insights that you get when you think about all facets of quality, whether that’s functional or performance, or accessibility, or UX. And, and to think about that holistically, whether it’s API or web or mobile. And so the area that will see the most innovation is when you can start to answer questions like, based on my UI tests, what API tests should I have? And how do they relate? So when the UI test fails? Was it an API issue? And then, when a functional test fails, did anything change from the user experience that could be related to that?,” Belcher said. “And so the key is to do this is we have to bring kind of all of the kind of end-to-end testing together and all the data that’s produced, and then you can really layer in some incredibly innovative intelligence, once you have all of that data, and you can correlate it and make predictions based on that.”

6 types of Automated Testing Frameworks 

1. Linear Automation Framework – Also known as a record-and-playback framework in which testers don’t need to write code to create functions and the steps are written in a sequential order. Testers record steps such as navigation, user input, or checkpoints, and then plays the script back automatically to conduct the test.
2.  Modular Based Testing Framework – one in which testers need to divide the application that is being tested into separate units, functions, or sections, each of which can then be tested in isolation. Test scripts are created for each part and then combined to build larger tests. 
3. Library Architecture Testing Framework – in this testing framework, similar tasks within the scripts are identified and later grouped by function, so the application is ultimately broken down by common objectives. 
4. Data-Driven Framework – test data is separated from script logic and testers can store data externally. The test scripts are connected to the external data source and told to read and populate the necessary data when needed. 
5. Keyword-Driven Framework – each function of the application is laid out in a table with instructions in a consecutive order for each test that needs to be run. 
6. Hybrid Testing Framework – a combination of any of the previously mentioned frameworks set up to leverage the advantages of some and mitigate the weaknesses of others.

Source: https://smartbear.com/learn/automated-testing/test-automation-frameworks/

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We sat down with Torsten and talked about the report and his insights into the impact that AI is having in Software Testing:

Q: What’s wrong with the current state of testing? Why do we need AI?

Organizations reliant upon traditional testing tools and techniques fail to scale to the needs of today’s digital demands and are quickly falling behind their competitors. Due to increasing application complexity and time to market demands from the business, it’s difficult for software delivery teams to keep up. There is a growing need to optimize the process with AI to help root out the mundane and repetitive tasks and control the costs of quality that have gotten out of control.

Q: How can AI help and with what?

There are five key capabilities where AI can help: smart scrawling/Natural Language Process (NLP) driven test creation, self healing, coverage detection, anomaly detection, and visual inspection. The report I wrote highlights six critical pain points where these capabilities can help. For example: false positives, test maintenance, inefficient feedback loops, rising application complexity, device sprawl, and tool chain complexity.

Leading organizations have already adopted some level of self-healing and AI driven test creation but by far the most impactful is Visual Inspection (or Visual AI), which provides complete and accurate coverage of the user experience. It is able to learn and adapt to new situations without the need to write and maintain code-based rules. 

Q: Are people adopting AI?

Yes, AI adoption is on the rise for many reasons, but for me, it’s not that people are not adopting AI – they’re adopting the technical capabilities that are based on AI. For example, people want the ability to do NLP-based test automation for a specific use case. People are more interested in the ROI gained from the speed and scalability of leveraging AI in the development process, and not necessarily how the sausage is being made.

Q: How does the role of the developer / tester change with the implementation of AI?

When you look at test automation, developers and testers need to make a decision about what belongs under test automation. How is it categorized, for example. Then all you need to do is basically set the framework for the AI to operate and provide it with feedback to continuously enhance its performance over time.

Once this happens, developers and testers are freed up to do more creative, interesting and valuable work by eliminating the toil of mundane or repetitive work – the work that isn’t valuable in and of itself but has to be done correctly every time. 

For example, reviewing thousands of webpage renderings. Some of them have little differences, but they don’t matter. If I can have the machine filter out all of the ones that don’t matter and just highlight the few that may or may not be a defect, I’ve now cut my work down from thousands to a very small handful. 

Auto-classification is a great example of being able to reduce your work. If you’re reducing repetitive work, it means you don’t miss things. Whereas, if I’m looking at the same, what looks like the same page each time, I might miss something. Whereas if I can have the AI tell me this one page is slightly different than the other ones you’ve been looking at, and here’s why, iit eliminates repetitive, mundane tasks and reduces the possibilities of error-prone outcomes.

Q: Do I need to hire AI experts or develop an internal AI practice?

The short answer is no. There are lots of vendor solutions available that give you the ability to take advantage of the AI, machine learning and training data already in place.

If you want to implement AI yourself, then you actually need people with two sets of domain knowledge: first, the domain that you want for the application of AI, but second, a deep understanding of the possibilities with AI and how you can chain those capabilities together. Oftentimes, that is too expensive and too rare.

If your core deliverable is not the deliverable of the AI but the deliverable of the ROI that the AI can deliver, then it’s much better to find a tool or service that can do it for you, and allow you to focus on your domain expertise. This will make life much easier because there will be a lot more people in a company that understand that domain and just a small handful of people that will only understand AI.

Q: You talk about the Visual Inspection capability being the highest impact – how does that help?

Training deep learning models to inspect an application through the eyes of the end user is critical to removing a lot of the mundane repetitive tasks that cause humans to be inefficient. 

Smart crawling, self healing, anomaly detection, and coverage detection each are point solutions that help organizations lower their risk of blind spots while decreasing human workload. But, visual inspection goes even further by aiming to understand application workflows and business requirements.

Q: Where should I start today? Can I integrate AI into my existing Test Automation practice?

Yes – example of Applitools Visual AI.

Q: What’s the future state?

Autonomous testing is the vision for the future, but we have to ask ourselves, why don’t we have an autonomous car yet? It’s because today, we’re still chaining together models and models of models. But ultimately, where we’re striving to get to is AI is taking care of all of the tactical and repetitive decisions and humans are thinking more strategically at the end of the process, where they are more valuable from a business-focused perspective.

Thanks to Torsten for spending the time with us and if you are interested in reading the full report http://applitools.info/sdtimes .

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“As The Qt Company continues its growth, the acquisition of froglogic is an important milestone in  broadening Qt’s best-in-class software development tools and building in automated testing and code coverage analysis directly into our suite of products. Understanding that speed of delivery for new products is crucial to our customers, our goal is to improve developer productivity and make the product development process as streamlined as possible,” said Juha Varelius, president and CEO of Qt Group Plc. 

Froglogic GmbH offers tooling to support GUI test automation, code coverage analysis and test result management, enabling customers to assess and steer their quality assurance efforts across an application’s life cycle. 

Embedded Software Testing & Compliance Summit announced
Parasoft announced that it is hosting a live virtual event on May 6th in which industry leaders will share their embedded software quality stories of overcoming safety-critical compliance and security challenges with automated software testing solutions. 

“Companies across all industries need to have confidence in their software quality and deliver safe and secure software to their users,” said Arthur Hicken, evangelist and event moderator at Parasoft. “Many embedded software companies are turning to automated and integrated testing that includes static code analysis, unit testing, regression testing, code coverage, and requirements traceability to ensure compliance with functional safety, security, and coding standards. In this summit you’ll hear how organizations are solving real safety and security software issues.”

The talks will cover how a medical device technology company successfully adopted a unit testing solution, how an avionics developer and manufacturer achieved code compliance and streamlines productivity and much more. 

Additional details on the event are available here.

Catchpoint announces virtual SRE community event on June 10th
Catchpoint announced that it will launch its SRE from Anywhere, a virtual, interactive event that focuses on helping SREs connect with peers to share best practices, industry trends and organizational dynamics.

The event will feature panel discussions, practitioner sessions and lightning talks to foster an open forum for inclusion and learning. 

Other talks include results from the 2021 SRE survey sponsored by Catchpoint, VMWare Tanzu, and the DevOps Institute about true observability, DevOps principles and the latest use cases and trends such as Platform Ops. 

Accolade for Smart Products
Sopheon announced Accolade for Smart Products, a new management solution that brings together traditionally siloed software and physical product development. 

The solution aims to foster cross-functional collaboration and synchronization that results in trusted, timely data for faster, better and more dynamic decision making.

“As the digital and physical worlds collide, many companies struggle to find the best ways to manage innovation across different disciplines. Accolade for Smart Products enables companies – from traditional manufacturers to new technology stars – to accelerate product delivery, while also implementing the best practices needed for product reliability without dragging down innovation,” said Paul Heller, the chief technology officer of Sopheon.

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Ninety-seven percent of respondents said that functional testing automation was important for success, and 96% said test automation patterns, principles, and practices were also critical. 

This automation isn’t necessarily leading to shrinking test teams. In fact, the number of companies that have test teams of 16 or more people grew by 10% in 2020, bringing the total to 34%. According to PractiTest, this indicates that companies are becoming more reliant on their testing teams and are investing in their growth. 

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The report also found that while 59% of testing teams are shifting left, about 40% are shifting right, implementing practices such as testing in production or chaos engineering. PractiTest noted that while a large number of companies are shifting right, it is on a downward trend, which is contradictory with the increase in chaos engineering. 

PractiTest also looked into how COVID-19 impacted testers. It found that 71% didn’t report any income changes as a result of the pandemic, but the past year did affect how they learn new skills, with the number attending online conferences, meetups, and seminars increasing to 49.5%, up from 40.5%. In addition, the use of online communities grew from 32.5% to 44%, and the use of formal training dropped by 16%. 

“Testing still seems strong and it looks like we are in our way to increasing the value we provide in our teams by perfecting the operations we perform day to day, and also by expanding towards additional areas of the process where the increase of visibility and faster understanding of issues arising can become critical,” PractiTest wrote in the State of Testing report. 

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The cookbook was created by a team from Applitools Test Automation University. According to Colby Fayock, a developer advocate at Applitools who contributed to the cookbook, the goal is to give engineers quick visual answers to their frequently asked questions instead of them having to sift through long video tutorials, online forms or Q&A threads to find answers. 

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Automated testing is a must in CI/CD pipelines
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Angie Jones, senior director of developer relations at Applitools and the Test Automation University who lead the initiative, added: “For many engineers, it’s common to run into a problem while writing a test. Whether you’re trying to work with alerts or upload a file, you may not be looking for an entire end-to-end course, but a quick solution to a single problem. We have designed this short-form education program to answer your questions accordingly.”

The launch of the Automation Cookbook includes 12 videos featuring test automation frameworks Selenium WebDriver and Cypress. New videos and additional frameworks will be added in the future and the videos will range from three to 10 minutes long. Cookbook recipes also include uploading files, interacting with alerts, selecting from dropdowns, verifying sortable tables, interacting with browser notifications, and taking screenshots. 

“Unlike Test Automation University, which focuses on long-form education of complete learning pathways, the Automation Cookbook focuses only on specific tasks,” the company explained its announcement. 

The Test Kitchen will act as a “pantry” for web components to be used for automated testing. Users can use the Test Kitchen to practice with common web widgets and hone their test automation skills and knowledge. 

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Version 2.0 enables users to save tests and reports on TestProject’s secure hybrid cloud and to benefit from zero server maintenance, end-to-end test creation, a collaborative testing repository, friendly dashboards and more, the company explained. Users can also save tests as local files directly on their machines with no cloud footprint and get a completely offline experience. 

“TestProject now aligns its free test automation platform to today’s various industry standards, accommodating its powerful capabilities for all and providing users the freedom to choose the option that works best for their organization’s restrictions,” TestProject stated in an announcement.

IBM Quantum Composer and IBM Quantum Lab announced
The two tools replace IBM Quantum Experience and include improvements into how users can manage files, receive notifications when jobs complete and view results. 

Users can search their files or view job results alongside the Jupyter notebook they are working on in IBM Quantum Lab. 

Meanwhile in IBM Quantum Composer, users can design and visualize circuits and the tool has an updated Setup and Run interface.

Sonatype acquires MuseDev
Sonatype announced that it is acquiring MuseDev, a startup that was incubated by Galois, Inc. 

MuseDev can install into any source control repo, automatically begin to analyze pull requests, and provide developers with accurate and actionable feedback.

MuseDev orchestrates 24 pre-configured code analyzers ranging from lightweight linters to deep static analysis tools and it can cover a wide variety of coding languages and bug types.

Additional details are available here.

Netlify announces Next.js integration
The integration enables users to install Next.js applications with zero configuration while having them fully integrated with the Netlify developer experience. 

This opens up many features for enterprise teams including role-based access, SAML single sign-on, two-factor authentication, integration with self-hosted GitLab and GitHub, and SOC 2 Type 2 attestation, the company explained.

Netlify also announced that it is developing a solution to effectively handle Incremental Static Regeneration (ISR) in Next.js. 

Docker announces new funding to help it focus on developers
Docker raised $23 million to capitalize on the accelerating demand for modern apps and to increase developer velocity. This round brings Docker’s total funding to $58 million. 

Docker’s collaborative application development platform accelerates software development from source code to cloud by simplifying developer workflows, providing trusted application components and integrating with leading developer tools 

“In the past year, applications have become paramount to not only all modern businesses but also as the primary means to connect society, all of which has greatly accelerated the need for developer velocity,” said Scott Johnston, the CEO of Docker. “This new investment, combined with our user and ARR growth momentum, validates Docker’s mission of helping developers and development teams bring their ideas to life by conquering the complexity of app development.” 

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According to Sauce Labs, the acquisition enhances its real-device cloud capabilities along with  its emulator/simulator offering with a developer-centric mobile testing solution to help developers deploy beta apps quickly and get real-user feedback.

Key features of the TestFairy platform include app distribution, crash reporting, in-app bug reporting, live support, remote logging, test automation, and session video recording.

Additional details on the acquisition are available here.

Git 2.31 released
In 2.31, Git gained the ability to serialize the reverse index into a new, on-disk format with the ‘.rev’ extension, which now uses up as much time to print an object’s contents as it does its size. 

“Reverse indexes can help beyond synthetic experiments like these: when sending objects for a fetch or push, the reverse index is used to send object bytes directly from disk,” Taylor Blau, a senior software engineer at Git wrote in a blog post that contains additional details on the new update. 

Git 2.31 also introduces background maintenance, a cross-platform feature that allows Git to keep your repositories healthy without blocking any interactions. This improves Git fetch times by pre-fetching the latest objects from one’s remotes once an hour.

Jscrambler now integrates with GitLab
Jscrambler announced a new integration with GitLab to improve the user experience and security protocols for GitLab customers that are using Jscrambler to protect their JavaScript applications. 

The integration now enables users to protect the source code at build time, add runtime protection capabilities in the source code, instill threat detection and reduce the attack surface.

“Client-side attacks are on the rise, with attackers blindsiding companies by targeting their source code and other client-side weak links. Jscrambler’s integration with GitLab will allow development teams to seamlessly protect their source code and reduce their exposure to reverse-engineering, tampering, and data exfiltration attacks,” said Rui Ribeiro, the CEO of Jscrambler. 

Google and Automation Anywhere team up on RPA
Google announced a partnership that will combine Google Cloud’s AI and ML technology with Automation Anywhere’s cloud-native RPA capabilities. 

“By partnering with Google Cloud, we can help organizations leverage intelligent automation capabilities at a massive, global scale, and dramatically decrease the amount of time that teams spend on their most common, repetitive business tasks,” said Mihir Shukla, the CEO and co-founder of Automation Anywhere. 

Google Cloud will integrate with services such as Apigee, Appsheet, and AI Platform and it will enable customers to scale the application of automation with API management, low code and no code development, and the development of ML workflows.

Additional details are available here.

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The highly integrated nature of most enterprise applications further complicates efforts. According to MuleSoft’s 2020 Connectivity Benchmark the average organization today uses more than 900 applications, and a single business workflow may touch dozens of these applications via microservices and APIs. 

To ensure business processes keep running, testers must replicate the work users perform across multiple applications and ensure none of those workflows are impacted when any of the applications are updated. That means that tests must work seamlessly across multiple applications, architectures, and interfaces. Whether your organization is adopting new technologies, incrementally modernizing legacy systems, or both, leveraging automated software testing means there’s no striking a balance between innovation and risk mitigation — you can go fast, safely.

Align testing with DevOps and Agile models
In order to align innovation and testing, many organizations are shifting enterprise application delivery to Agile and DevOps models. While this is an invaluable approach to bring about new software updates and features, it also tightens the timeline to thoroughly test new code — which is often reported as the biggest delay in the delivery process. 

Therefore, modernizing software testing represents the most significant opportunity for improvement in delivery timelines. The faster delivery teams can ship updates, the faster the organization can build innovation that streamlines business processes and unlocks new streams of revenue.

Hypercare puts you at risk
To overcome testing challenges, some organizations have pivoted away from extensive pre-release testing in favor of hypercare. This is a post-release period that can last weeks or months in which an organization’s most talented (and usually most expensive) resources dedicate their time to quickly fixing defects in production. Hypercare recognizes that business users are unlikely to catch all defects in pre-release testing, but it is not an adequate replacement: tying up resources means the innovation backlog continues to grow, costs rise and so does risk. 

Organizations are better off learning to prioritize and deliberately test specific aspects of their software using a risk-based approach. However, designing an effective risk-based testing strategy requires collaboration between business users and IT — and this is much easier said than done.

Use a risk-based approach
Risk-based testing improves quality and reduces production defects. It can also significantly reduce software testing effort. This approach shifts the focus from test coverage to risk coverage. While both metrics are important, incorporating risk coverage into the testing strategy makes it easier to align testing activities with business objectives. 

Risk coverage tells you what percentage of business risk is covered by test cases. It is often the case that 10% of a test suite will cover 80% of the business risk. This approach significantly reduces the number of tests needed to deliver high-quality releases to production.

A risk-based approach is also helpful in the event of a major upgrade or migration. Some enterprise application vendors offer readiness checks, but enterprises should consider seeking advanced assessments from third parties. These advanced assessments focus on identifying the impacts of the upgrade on development, testing, integration, and security. The result is a clear picture of the risk the upgrade poses, the tests that need to be executed, and the test cases that are redundant or no longer required in the new environment. 

Scale test automation
A key to cutting costs lies in an organization’s ability to efficiently scale test automation. With test automation, organizations can significantly improve test coverage and catch defects much earlier in the delivery lifecycle, when they are less expensive to fix. 

Organizations can scale test automation both horizontally across applications and vertically within an application with low-code and no-code solutions. These solutions can be adopted and learned quickly by existing testing resources, regardless of technical skill. A model-based approach extends the benefits further, by organizing test cases into reusable building blocks that can be repurposed across projects and teams. By increasing test resiliency, this approach eliminates the high maintenance costs associated with script-based tools.

As the infrastructure that supports the whole business, enterprise applications are central to both internal and customer-facing innovation. Modernizing this infrastructure gives enterprises a solid foundation for digital transformation — but only if they can do so without introducing business risk. The complex nature of these applications, coupled with misconceptions about the level of testing required, means many enterprises face an uphill quality battle that could stall transformation efforts. 

Enterprises that recognize the importance of a modern testing approach that is aligned to business objectives, measures risk, and can effectively scale upwards, stand to make significant gains in 2021. 

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The new offering enables teams to execute codeless test scripts on real devices for native Android and iOS mobile apps, with web support coming soon. Later this year, a test case management product will also launch, according to the company. 

“Customers today are won and lost through digital experiences. That is why the quality of digital assets – mobile apps, websites, voice-driven experiences and more – is critical to get right,” said Doron Reuveni, the CEO of Applause, founder and chairman of the board. “To meet these needs, Applause continues to disrupt the testing market, having evolved from a services and solutions company to one that delivers a complete platform for driving product excellence.”

Hasura 2.0 released
Haura release version 2.0 of its open-source GraphQL Engine and the release enables organizations to deploy REST and GraphQL APIs from one configuration.

This release includes a GraphQL API gateway which provides granular authorization to any GraphQL API and it also supports Google’s BigQuery database in addition to PostgreSQL, SQL Server and MySQL.

Hasura also announced the managed service offering of GraphQL Engine, Hasura Cloud, now has AWS VPC peering capability to securely connect their data and infrastructure to Hasura Cloud in a secure private network.

Planview acquires PPM providers Clarizen and Changepoint
With the acquisition customers of all three platforms will benefit from a premier community of project portfolio management and professional service automation practitioners. 

“The nature of work has changed significantly in recent years, causing leaders across industries to rethink how to best strategically plan, execute, and empower teams in today’s all-digital world. This shift has placed a spotlight on the growing importance and strategic value of Portfolio Management, Work Management and Enterprise Agile Planning capabilities, as evidenced by the recent wave of IPOs, consolidation, and acquisitions of several key players in this category,” Planview wrote in a post.

The acquisition closely follows Planview’s acquisition by TPG Capital and TA Associates in December 2020. 

Pega’s enhancements for low-code mobile app development
The newest features of Pega Mobile provide UX and app authoring enhancements, customizable branding options, and expanded offline capabilities in which Pega mobile apps are designed to be used offline and synced later when an Internet connection is restored.

“Pega Mobile makes it fast and easy for the user to create and manage as many mobile apps as the business needs. Instead of deprioritizing mobile, organizations can adopt a true mobile-first approach with powerful apps that help make them more productive,” said Eric Musser, the general manager of intelligent automation at Pegasystems. 

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