7 Ways To Increase the Cost-Effectiveness Of DevOps Testing Automation

DevOps testing automation is the key to achieving the rapid delivery, scalability, security, collaboration, and monitoring that allows today’s agile teams to be highly productive.

Testing automation enables businesses to increase efficiency and improve ROI by:

Shortening development cycles and implementation time.
Reducing time-to-market.
Increasing the productivity of business units and IT teams.
Lowering cost associated with development and deployment.
Streamlining workflow through process standardization.
Minimizing human errors.
Improving the quality, reliability, and reusability of system components.

However, having the ability to automate doesn’t mean you should automate anything and everything.

Since there’s an upfront investment in purchasing software, standardizing processes, programming automation, and providing employee training, it’s important that you’re implementing testing automation strategically to maximize cost-efficiency and optimize your ROI.

7 Ways To Increase the Cost-Effectiveness Of Testing Automation

Here’s how to get the most of DevOps testing automation:

1. Identify Workflows Involved

Take inventory of all the applications, data stores, and workflows in your development process. Identify common components (e.g., repetitive tasks) to decide which ones are worth investing the time and efforts to set up automation.

2. Select Processes To Automate

Select workflows that can benefit most from automation. For example, processes that are high in volume, require three or more users to execute manually, rely upon time-sensitive activities, and impact various systems are good candidates for automation. On the other hand, those that require human involvement at multiple decision points and show low ROI should not be automated.

3. Improve And Standardize Processes

The workflows you’re automating should be optimized so it’s as efficient as possible. After all, automating a flawed process just makes a bad workflow happen faster! Upon selecting the workflows to automate, review and fix any inefficiency before setting up automation.

4. Increase Test Coverage Gradually

Start your automation with the simplest workflows and repetitive tasks. Many of them may actually take up most of your testing time so prioritizing them can yield immediate ROI. This will also help you understand how to best utilize the automation software and allow your team to familiarize themselves with the testing tool.

5. Build Test Cases That Isolate Each Element

Create test cases such that each would test one aspect so it’ll be easy to identify what goes wrong when a test case fails. Also, build reusable components instead of bundling multiple tests. You can then reuse the logic in different test cases to minimize the time required to write new test cases.

6. Create Independent and Self-Contained Test Cases

Save time and increase productivity by creating test cases that aren’t dependent on each other so they can be scheduled and executed simultaneously across different environments. Test cases that need to be executed in a particular order can undermine the flexibility and agility of the QA process, which are the inherent benefits of automation.

7. Select the Right Testing Automation Tool

Choose a testing automation software with functionalities that can support all your current workflows and test cases. In addition, identify applications and technologies across the organization that are potential targets for automation so you can maximize the long-term ROI of your investment.

Look for a testing automation tool that:

Uses a cloud-based platform to deliver scalable, reliable, and accessible services.
Allows you to develop and maintain scripts quickly and easily.
Enables continuous development, continuous integration, continuous testing, and continuous deployment.
Provides cross-browser and cross-platform testing in various environments.
Allows you to reuse functional scripts for multiple tests so you can save QA time and efforts.
Supports keyword- and data-driven testing.
Allows you to manage the complete QA lifecycle, from test generation to detailed reporting, all in one place.
Offers 24/7 support, e.g., you can get help from customer service, self-service knowledge base, and user community.

Conclusion

Testing automation is key to lowering cost, improving QA efficiency, and shortening the development cycle. However, you need to plan and implement the process strategically in order to optimize the workflow and get the highest ROI.

In addition, when selecting a testing automation solution, it’s important to keep your organization’s unique circumstances and priorities in mind so you can create processes that meet your business objectives.

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AI in Testing: The third wave of automation

The evolution of agile methodology enforced the enterprises to innovate and deliver at lightning speed. While delivery cycle time is decreasing, the technical complexity required to deliver positive user experience and maintain a competitive edge is increasing—as is the rate at which we need to introduce compelling innovations.

To meet the continuous integration and delivery needs, we have turned to continuous testing backed by automation but how do we test when these trends continue and gaps widen? As this is the time of digital transformation, we need Digital Testing to meet the quality needs of future driven by AI, IoT, robotics and quantum computing.

If we look at how testing practices changed over time, till 2018 it was focused on CI/CD, scalability and continuous testing.

Now the expectations from testing are more about real-time risk assessment. To cope up the testing expectations in current scenarios, Artificial intelligence (AI), imitating intelligent human behavior for machine learning and predictive analytics, can help us get there.

Beyond Continuous Testing

If we analyze the journey since agile came into the picture, it has completely changed the way applications are delivered. Before agile, there used to be a release in a month or sometimes more than a month. With agile companies are aligned to have a two- weeks sprint and make a release in two weeks. To meet this, Continuous Testing came into the picture where automation suits were developed for regression and sanity testing. This supported quick deliveries and fast-paced testing cycles.

Now as the world is moving towards Digital transformation, the pressure to anticipate market requirements and build a system which is predictive and scalable enough to cater to future trends, going beyond continuous testing is inevitable. Testing will need additional assistance to accelerate the process. AI, imitating intelligent human behavior for machine learning and predictive analytics, can help us get there.

What is Artificial Intelligence?

Lets first understand what does artificial intelligence mean. Forrester defines AI as-

“A system, built through coding, business rules, and increasingly self-learning capabilities, that is able to supplement human cognition and activities and interacts with humans natural, but also understands the environment, solves human problems and performs human tasks. “

In simple words, AI enables machines to learn through data giving them the capability to make a decision. The algorithms are not written to solve a particular problem rather they are designed in such a way to enable the system to make a decision based on data.

How AI can be used in software testing?

Using AI and machine learning to automate-

a) Unit tests – Unit testing is very important to make sure that the build is stable and testable. With AI-powered unit test tools like RPA, a developer can get reduce the flaky test cases and maintenance of unit tests.

b) API testing- API testing saves time and effort by getting into the root cause of the issue. The problem with UI tests is that they are not reliable anymore as UI keeps changing in agile, while API tests give a deeper insight into the application and directly hit the root cause of an issue eventually making the application more robust.

There are many tools which are using artificial intelligence to help take the complexity out of API testing by converting manual UI tests into automated API tests, lowering the technical skills required to adopt API testing and helping organizations build a comprehensive API testing strategy that scales.

c) UI testing- The first step in automation is to convert manual UI tests into automated tests. There are tools which leverage AI to run the test cases on multiple platforms and browsers and also learn from the functional flow, reducing the maintenance effort and making testing more reliable.

Some of the most popular tools are mentioned below-

AI Powered testing tools- There are various testing tools which are using AI, though not harnessing the best of AI, they are still able to help testers a lot-

1. Applitools- It is an AI-powered visual testing and monitoring tool that can run tests on different browsers and platforms. It uses AI to identify the meaningful changes in UI and also identify them as bugs/ desired changes.

It also leverages ML/AI-based for automated maintenance (being able to group together similar groups of changes from different pages/browsers/devices)

2. Testim- It leverages machine learning into the most critical part of automation which is execution and maintenance of tests.

3. Sealights- Sealights uses AI and machine learning to analyze the code and run tests which cover the impacted area. It can be any kind of test- unit, functional, performance, manual, etc.

It provides a useful insight ‘Quality Risks’ which focuses user efforts on the things that matter by letting him or she knows exactly which files/methods/lines have changed in the last build that wasn’t tested by a specific test type (or any test type).

4. Test.AI- Test.AI is building as a tool that will add an AI brain to Selenium and Appium. It was created by Jason Arbon, co-author of How Google Tests Software and the founder of Appdiff. Tests are defined in a simple format similar to the BDD syntax of Cucumber, so it requires no code and no need to mess with element identifiers.

AI just like a real person, IDENTIFIES the screens and elements in your app.
AI EXECUTES user scenarios—test on-demand whenever you’re ready
AI RECOGNIZES elements so that even if things change, your test doesn’t break.

5. MABL- Like the other AI-based test automation tools, MABL can automatically detect whether elements of your application have changed, and dynamically updates the tests to compensate for those changes. You just need to show the workflow that has to be tested and MABL does the rest.

6. Retest- Retest propagates an innovative testing approach, which is a combination of “intelligent” monkey testing and “difference testing” and works actually more like a GUI version management than conventional testing.

This tool does Monkey testing whereby the monkey( is called Surili) is artificially intelligent and can be trained by users by capturing user actions.

7. ReportPortal- ReportPortal, as the name suggests, is an AI-powered automation tool which focuses more on report analysis and management. As per its website it-

Manage all your automation results and reports in one place
Make automation results analysis actionable & collaborative
Establish fast traceability with defect management
Accelerate routine results analysis
Visualize metrics and analytics
Make smarter decisions together

8. Functionlize- Functionlize provides an overall solution for seamless automation with less/no efforts in maintenance all with the help of AI. Its AEA tool finds and fixes the broken test scripts thus eliminating the manual maintenance.

Functionize uses machine learning for functional testing and is very similar to other tools in the market regarding its capabilities such as being able to create tests quickly (without scripts), execute multiple tests in minutes, and carry out in-depth analyses.

It also gives scalability to test suites by maintaining them in the functionlize test cloud.

Challenges of AI in Testing

The machine learning process is completely dependant on the data thus leading to a large volume of the dataset. AI model test scenarios should be equipped to identify and remove human bias which often becomes part of training and testing datasets.

There is a lack of awareness about AI and Machine Learning process and proper training is required to the testers.

AI in 2019 and beyond

As we have progressed from a linear waterfall model to agile, the future is all about AI and machine learning technologies. As a tester, we need to be upfront and start digging more about the various aspects of AI, take the hands-on in AI-powered tools and utilize them.

There are so many places where AI has already paved its way whether it be chatbots or Amazon’s Alexa, we need to be very keen about how we are going to the device out test cases to test such applications and deliver quickly.

With the increasing demand of AI-powered testing tools, testers might need training at earliest. Having advance knowledge of AI and its applications will be very helpful.

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How Are Robotics Going to Change the Field of Automated Testing?

It’s quite staggering to think about just how much testing needs to be done across the world on a daily basis. It’s a natural consequence of the overwhelming pace of technological development, born of unprecedented scale and complexity — just consider how sophisticated the average smartphone is relative to a comparable item of just a decade ago, and how hard it is to get things done these days without powerful software tools.

This has given rise to the automated testing industry: the ongoing pursuit of using yet more software to make the design and implementation of testing procedures as easy and efficient as possible. Today, if you’re preparing a customer-facing program for release, you don’t need to manually run through every last element — you can use a cloud service to simulate customer actions and rapidly get the data necessary to make final changes.

But a lot of automated testing is still built from scratch, with sequences set out and only repeated rote with any decent speed. Robotics greatly expand the possibilities. Essentially, they can generate and execute overarching macros regardless of the system. How is this going to change the field? Let’s take a look at the broader impact robotics are likely to have:

They’ll make it easier to operate across systems

Disparate pieces of software typically rely on APIs to communicate. Essentially, they translate each program’s language into a common language that can be easily used by other programs. This is important for testing because many everyday procedures in the modern business world rely on APIs for multiple applications being used in combination.

But having to mess around with APIs isn’t ideal. It requires you to connect your testing tool to each and every program that plays a role in the process you’re trying to record — if it can’t connect to even one of them, it will leave a gaping hole in the chain that will almost certainly prevent it from serving the desired role.

A robotic testing setup, however, can imitate how a human interacts with software, using GUIs (not seeing them, as such, but still being able to navigate them) to execute actions. This means that any program is on the table, regardless of the presence or quality of an API — which means it will be so much easier to test hyper-complex processes.

They’ll introduce NLP-parsed test creation

An alternative title for this point could be “They’ll make testing tools intelligent”, but that’s a contentious claim, so I prefer this one. This is likely to be the most impactful change overall. Testing procedures currently need to be established with meticulous attention to detail, because programs aren’t capable of simulating how humans assess things — but progress marches on.

Using machine learning, it will (sooner or later) be possible for a testing program to take a set brief and generate an appropriate testing process without anyone needing to get manually involved. For instance, you could issue the command of “Check that the customer registration form is functioning correctly” and see the utility work from there. There may be some pseudo-creative elements, but for the most part, it will about drawing from a library of preset testing routines and adapting them to suit.

NLP (natural language processing), of course, is necessary for the very first step: gleaning from the brief (and the context) what the purpose of a particular test is, and what specific elements need to be tested. There will likely come a time when someone can tell their voice assistant to check a particular system, and everything following that will be handled for them — this is already a common feature in industries from ecommerce (Shopify’s ecommerce suite has the Kit assistant) to fintech (Capital One’s banking service has the Eno assistant).

They’ll destroy some jobs, but create others

As nice as it is in some ways to envision this industry of hyper-convenience, it’s also worrying for some, because they imagine the automation wave as more of a cudgel that will beat countless job types into obsolescence. Admittedly, it’s entirely true that automation testing will make some jobs redundant (a product quality tester can’t realistically rival the efficiency and reliability of an automated testing service), but it will also create jobs.

Why? Because automated systems can (and do) go wrong. Updates break things. Code systems get out of date. Framework errors go unseen. And the “smarter” we make automated software, the more opaque its functionality will become. What exactly will it mean when such an application states (using an approximation of human assessment) that something is fine?

To keep this technology in line, the world needs people with skills in the AI field. It needs people who can manually test the automated testing systems — digging into them and vouching for the results they produce so that everyone down the line can feel reassured that they didn’t exclusively accept the conclusions of an algorithm.

The robotics field is affecting everything at the moment, but automated testing, in particular, stands to change hugely because it’s massively important and ripe for machine learning in a way that few other pursuits are.

In the coming years, expect to see more companies automating their testing, software becoming capable of spontaneously generating tests, and the job market moving away from unskilled labor but just as strongly towards professionals with AI-relevant skills.

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Bigger, Better And Faster – Your Favorite Testing Software Just Leveled Up!

Selenium Test Automation

Bigger, Better And Faster – Your Favorite Testing Software Just Leveled Up!

CloudQA is committed to offering its clients the most advanced and the most efficient technologies. We spend a lot of time and effort in R&D, and try to bring the most stable and effective strategies to the market. We know the value of your time and money, and don’t skimp about features and capabilities. Keeping that in mind, we’ve been working our butts off to bring you even better features in your favorite testing software.

Now, using CloudQA, you can customize your execution by recording steps updates! We’ve given you a lot of control over this, and you can now define the speed of execution steps on the basis o your application being tested, and set element wait time at the application level in your app preferences. Besides that, you can now begin your testing before the UI is developed, saving everyone a lot of time, and hastening your software development process.

You can test “get”, “post”, and “delete” HTTP requests easily, as per your requirements. You can also save the API test case, and even reuse them when you need it next. You can also add assertions to confirm the response of the API, and view the response in both raw and JSON formats. Additionally, you can add parameters and headers based on the API you are currently using.

Finally, CloudQA has made monitoring your web application so much easier, it’s child’s play! You can check your URL every 5 minutes (free of cost, we might add) and upload all your URLs in a .csv file. You can test your website’s performance, and view up and down times of your website in both heat map and graph formats. Finally, we’ve also made sharing reports with your team members much easier with shareable links that improve team communication efficiency!

Phew! That’s been a lot of work. But, as they say, no rest for the wicked! We earnestly hope you like all these new updates we’ve rolled out for you. Get in touch if you have any queries about these or any other features of the site, and we’d be more than happy to help (In fact, get in touch with us anyway, we love feedback and communication!). In the meantime, we’ll be over here working on getting you even more awesome new features and updates that will help you get your work done faster, more easily, and more efficiently!

For now – that’s all, folks!

If you want to learn more about being more productive with Test Automation, contact us at CloudQA (info@cloudqa.io)

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Automated Functional Testing – Why you should seriously consider it

What is Functional Testing?

A properly written functional test ensures that the system is functioning exactly as the USER expects it to. Functional tests target business goals and typically defined and validated by an expert end user. Since it may not be possible for an expert to test the functionality of the system at each and every step of development, the test cases/scenarios are often documented with their help. This document defines the step by step instructions of system interactions along with the expected outcomes for every test case defined by the expert users. It can then help any user (not just the Expert) to be able to follow the steps to test the software and verify if the outcome is exactly as the expert has defined. To ensure completeness, functional tests should cover both the positive and negative scenarios.

Challenges:

Manual functional testing can be a tedious process as it can take quite a while for a tester to step through each of the steps and verify the outcome/results. When the user goes through these steps each time there is a software update, there is a high probability of erroneous information being entered or captured by the tester resulting in incorrect validations. If the number of test cases is extremely large and complex this problem gets magnified. The manual testing process can, therefore, be extremely laborious, monotonous and error-prone and requires highly dedicated and qualified resources to execute.

This can be overcome by automating the functional tests as much as possible. Granted that not every test can be automated, but even a 50% coverage via test automation can bring about huge relief in time, cost and resources. Once a test scenario is “automated”, it can be run on demand after each software release cycle. The feedback from an automated test is extremely fast compared to the same test run manually.

Consider an example where a test scenario requires a user to traverse through multiple screens, enter data into 20+ fields, select various check boxes, radio buttons, pick a value from a drop-down options, enter date in the right format, validate the response on 5 submit buttons, etc.. A real-life scenario like this can easily take the user over 5-10 minutes each time to run this test. For the same scenario, if the test requires a different data set or a negative test, it means another 5-10 minutes. When you look at multiple data sets and 100’s of test scenarios, it translates to weeks of testing that is required after each major software release. This also causes a bottleneck for the development team to move any further before getting a complete feedback from the test users. If all these test scenarios could be automated, it would take only a couple of hours to go through the tests and feedback cycle to the developers would be extremely fast compared to manually testing all the scenarios. That is a huge saving in terms of time. effort and resources and easily justify adopting functional test automation as much as possible.

A lot of companies, as a standard practice, do not thoroughly test their software application after each minor or major release because of the aforementioned manual testing challenges. The risk that they undertake, however, is extremely high if the software fails in a real-world environment – brand reputation, lost clients, fixes that take too long and that potentially break other parts of the software.

Automation Frameworks

One way to speed up functional testing is through web automation tools. Selenium is one of the few software testing automation frameworks for web applications out there which is open source and has a very large following. It has a set of tools to support test automation in multiple browsers. Web test automation with Selenium, however, is a programming activity and people creating tests should be comfortable with one or more of the few programming languages that Selenium supports (Java, C#, Python, etc.). Even though it might take some time (a few hours or days depending on the complexity of the test case), it can greatly reduce manual repetitive/regression testing efforts after each software release cycle.

There are a few companies that are working towards reducing the time to automate the tests with Selenium by providing a “wrapper” solution which essentially means “automating the Selenium test automation” efforts. However, since there are many types of browser interactions in modern web technologies, not everything can be addressed easily. The goal of a tester should always be to evaluate and use tools & methods that can address the maximum number of test cases with minimum effort.

Automation with CloudQA

There are quite a few testing tools that can assist in automating the functional tests but the challenge faced by most tools is the automation coverage (especially in large software applications). CloudQA has been designed to cover a lot of complex test scenarios. The goal of CloudQA is to cover automation of as many functional test cases as possible. It starts with a unique ability of CloudQA to discover as many test cases as possible of the software application via an auto-learning process. This, in itself, can save test managers weeks (if not months) of discovering scenarios that can be possibly tested. Besides that, some of the complex scenarios that CloudQA handles right out of the box includes

* Table data access
* Auto Suggest boxes
* Variable storage of a dynamic data set
* File Upload tests
* Data-driven testing (for multiple data sets)
* Various types of user assertions (check, check not, etc.)
* Various actions – click, alert, screen snapshot, wait

If you are thinking about automating your functional tests, talk to one of the CloudQA specialists (email: info@cloudqa.io)

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Benefits of Automated Functional Testing with CloudQA

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7 Ways To Increase the Cost-Effectiveness Of DevOps Testing Automation

7 Ways To Increase the Cost-Effectiveness Of Testing Automation

1. Identify Workflows Involved

2. Select Processes To Automate

3. Improve And Standardize Processes

4. Increase Test Coverage Gradually

5. Build Test Cases That Isolate Each Element

6. Create Independent and Self-Contained Test Cases

7. Select the Right Testing Automation Tool

Conclusion

Talk to our Test Engineers

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AI in Testing: The third wave of automation

Beyond Continuous Testing

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How AI can be used in software testing?

AI in 2019 and beyond

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How Are Robotics Going to Change the Field of Automated Testing?

They’ll make it easier to operate across systems

They’ll introduce NLP-parsed test creation

They’ll destroy some jobs, but create others

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Micro Services – The new kid on the block

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What is Functional Testing?

Challenges:

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