Top Software Testing Trends to Watch Out For in 2020

The software testing landscape continues to evolve. We have seen the continuation of developing trends and the emergence of new trends in 2019. This year, our team of automation experts has cast a few predictions on the latest trends in the software testing industry. Check them out!

To see the recap on 2019 software testing trends, read our article here.

1. Artificial Intelligence and Machine Learning in Testing

Intelligent automation will continue to be on the software testing radar in 2020, according to a variety of reports. 

Applications of artificial intelligence and machine learning (AI/ML) have been leveraged in software test automation before. AI makes testing smarter. Teams can leverage AI/ML to optimize their automation strategies, adapt faster, and operate more effectively. 

In 2019, quality assurance (QA) teams have applied AI/ML in predicting test quality, prioritizing test cases, classifying defects, detecting test objects, interacting with applications under tests (AUT), and so on. It is expected that AI will be omnipresent in every sphere of innovative technology. Investments in this area are expected to fall around $6-to-7 billion in North America alone. By 2025, it is forecast to reach nearly $200 billion. We will expect to see applications of AI in more testing areas — most of which will be relevant to reports and analytics:

• Log analytics: Identify unique test cases that need manual and automated testing
• Test suite optimization: Detect and eliminate redundant, unnecessary test cases
• Ensure test requirements coverage: Extracting keywords from the Requirements Traceability Matrix (RTM)
• Predictive analytics: Forecast key parameters and specifics of end-users’ behaviors and identify application areas to focus on
• Defect analytics: Identify application area and defects that ties to business risks 

The other pillar on which intelligent automation rests is machine learning. ML is expected to reach another level of maturity in 2020. According to the Capgemini World Quality report, 38% of organizations have planned to implement ML projects in 2019. Industry experts predict that this number will rise in the next year.

What does this mean for organizations?

Even though there is a rising demand in prospects of AI/ML application in software testing, experts still regard AI/ML in testing is still in its infancy stage. However, we are very much likely able to see maturity coming.

As AI is making new demands in testing and QA teams, Agile teams must start adopting AI-related skillsets—which include onboarding data science, statistics, mathematics. These new skillsets will not replace, but a complement to the core domain skills in automated testing and software development engineering testing (S-DET).

Also, business acumen is another essential skill to adopt. Successful testers need to have a combination of pure AI skills and non-traditional skills. Indeed, last year, new roles have been introduced such as AI QA analyst and test data scientist.

As for automation tool developers, they should focus on building tools that are practical. Companies are running PoCs and reassessing options to make the best use of AI and considering budgets. A good AI-assisted tool has to fulfill both the business cost-efficiency and the technical aspects such as reading production logs, generating test scenarios, or responding to production activities.

2. Test Automation in Agile teams 

Test automation is undoubtedly no longer a foreign idea in quality assurance. Indeed, 44% of IT organizations expect to automate 50% or more of all testing in 2019. We predict that more adoption of automated testing will continue to be on the rise next year.

As more businesses adopt the latest Agile and DevOps processes to fulfill the demand for Quality at Speed, test automation has become an indispensable component. Test automation continues to lead by helping teams perform repetitive tasks, detect bugs faster and more precisely, provide continuous feedback loops, ensure test coverage. Therefore, organizations that implement automated testing in their QA processes can save a significant amount of costs, time, and human resources.

Test automation in 2020 is expected to be championed especially by millennial entrepreneurs, leveraging the combination of open-source and commercial tools.

What does this mean for QA practitioners?

Test automation, however, will not eliminate manual testing. In fact, robust QA teams must appropriately combine manual and automated testing to achieve the most in ensuring software quality. The role of automated testing is undeniable—but some testing types such as exploratory or usability testing still need to be manually carried out.

QA practitioners, in addition, have to develop a smart, common, and end-to-end environment. There has been an increasing need to automated from build through deployment. Test automation is no longer regarded as a functional but as a full-cycle requirement. 

This process is easier said than done. That’s why many organizations have not been able to squeeze the most out of automated testing and received the desired return on investment. The Capgemini World Quality Report suggests that instead of looking at automation as a capability, QA teams should think of it as a broad, smart, and connected platform.

What does this mean for test automation solution providers?

Test automation tools developers must continuously update and upgrade tools to fulfill QA teams’ demands. Future test automation solutions must follow some basic criteria, for example: 

• Easy to adopt and use for end-users at any testing level 
• Provide smart frameworks, meaning letting issues resolve themselves See Autohealing Smart XPath and Katalon Smart Wait 
• Ensure full test coverage and quality bugs detection
• Cross-platform testing for web, API, mobile, and desktop automation
• Integrate with CI/CD tools and allow Continuous Testing
• Integrate with intelligent dashboards and analytics for quality insights See Katalon TestOps

3. Big Data Testing 

Big data has served an essential role in a variety of business sectors including technology, healthcare, banking, retail, telecom, media, and so on. There has been more focus placed on using data to segment and optimize decision-making processes.

Big data testing allows industries to deal with huge data volumes and diverse data types. It also helps make better decisions with precise data validations, as well as enhancing market strategizing. Big data testing is no longer a new phenomenon. However, it is expected to grow exponentially as many industries are shifting toward a data-oriented world.

The trend of testing big data has been widely adopted, mainly because of the robust processes that most of the enterprises are following make the most of their marketing strategies. Big data testing is not an uncommon practice and it is expected to become popular in the next year. Therefore, we forecast that the need for testing big data applications will see a new rise in 2020.

4. QAOps: Quality Assurance Sees Changes in DevOps Transformation

If you haven’t heard of the term ‘QAOps’ yet, now’s the time. 

You might have been familiar with ‘DevOps’—a set of software development practices that combines development (Dev) and information technology operations (Ops). The goal of DevOps is to shorten the systems development life cycle (SDLC), while teams can focus on building features, fixing bugs, and pushes frequent updates that are in alignment with business objectives. DevOps abridges the collaboration between developers and business operationalists.

In the same spirit, QAOps helps increase the direct communication flow between testing engineers and developers by integrating software testing into the CI/CD pipeline, rather than having the QA team operate in isolation. In short, QAOps is defined in two key principles:

1. QA activities should be incorporated into the CI/CD pipeline
2. QA engineers should work in alignment with developers and be involved throughout the CI/CD process.

Facebook is one of the best examples of QAOps adoption. In 2014, the Facebook team decided to migrate to Facebook Graph API version 2.0 and enforce Login Review across all apps. To ensure a smooth migration process, the team wanted to test out this new version on the 5,000 largest apps. In-house testing did not allow this to be possible, so they chose to apply QAOps through outsourcing. Eventually, the team was able to test across 5,000 apps in one month and managed to address critical problems—which could have been impossible had the process been carried out by the internal team alone. 

QAOps can be applied not only in giant tech companies but also in medium and small teams. This practice can be flexibly scaled down or up to fit any business size. Because more teams are gearing toward DevOps, we will expect to see QAOps as a growing trend in 2020. 

5. IoT Testing

The rise of testing Internet of Things (IoT) devices was already prominent in 2019. The number of IoT devices all around the world will reach 20.5 billion by 2020, according to Gartner. 

IoT testing means testing the IoT devices for security assurance, ease of use, trustworthiness, compatibility of device versions and protocols, versatility of programming items, monitoring connection delay, scalability, data integrity evaluation, device authenticity, so on and so forth. IoT testing engineers often face an overwhelming amount of work in this area, especially with monitoring communication protocols and operating systems and multiple combinations of different elements of an IoT system. Therefore, QA teams should expand their knowledge and enhance their skills in usability, security, and performance IoT testing. 

Another challenge that IoT testers will face in the upcoming years lies in strategies. Although IoT devices and applications have been growing exponentially, 34% of respondents said their products have IoT functionality, but their team still does not have a proper testing strategy, according to the World Quality Report.

6. Demands for Cybersecurity and Risk Compliance

The digital revolution brings about increasing security threats. CIOs and CTOs from almost every enterprise across all sectors continue to acknowledge the importance of security testing of their software, applications, network, systems. Software developing teams even work with their partners to make their products more resilient to threats, taking the cybersecurity shield to the next level.

Testing for security helps secure not only transactions (be it money or data), but also protection of their end-users. Because cyber threats can take place in any form, at any moment, security testing will continue to be a popular topic in the following year.

Conclusion

These are our compiled list of predictions on the most popular software testing trends in 2020. No matter how the digital transformation is going to turn out in the following year, it is certain that testing engineers, as well as software products enterprises, will continue to witness changes and adjustments. As a result, quality assurance teams, leaders, and practitioners must constantly evolve in order to stay agile in this ever-changing industry.

Software Testing Trends 2019 Recap | Industry Insights

2019 is almost over. The software testing landscape has seen numerous introductions in new testing approaches and innovations at an exponential rate. It has also witnessed the continuation of technological improvement, evolution, and reinvention. As we are progressing to 2020, let’s take a retrospective look at the top trends in test automation and see how we stand after one year.

Our team at Katalon has reflected on the most popular trends that took place in the software testing industry over the course of one year. We have compiled the five most influential software testing trends in 2019. Check them out!

1. Continuous Testing Gets Even More Popular

Continuous testing persisted in going mainstream. Although this concept was coined back in the early 2010s, it was forecast to become trendy in 2019.

Continuous testing is a software testing method that allows a constant flow of feedback between the developers and testers — throughout the entire software development lifecycle. Its value? A faster, more cost-efficient, and less perilous way to reduce bottlenecks among departments. To enable continuous testing, teams must reach an automation rate of 85% or higher — and we expected to witness this phenomenon in 2019.

Moreover, as more and more software organizations embrace the practice of Agile and DevOps, continuous testing is widely adopted. “Quality at Speed” is no longer a new norm in software delivery. Many practices have been introduced and recommended to attain this desired scenario, including continuous testing. Therefore, this method was predicted to have an enormous impact on achieving both the “quality” and “speed” factors of this puzzle.

2. Artificial Intelligence and Machine Learning in Quality Assurance

The state of 2019 AI/ML in testing

It was expected in 2019 that there would be more artificial intelligence and machine learning (AI/ML) applications in quality assurance such as quality prediction, test case prioritization, defects classification, computer vision, interaction with the application under test, and so on.

Organizations have been scouring for ways to make the best of technological advances so that they can cope with fast-paced releases, frequent changes (see Autohealing SmartXPath), mass operating environments, and everything operates in a state of flux. As a result, more test cases have to be generated, more test scripts have to be written, more test data have to be collected, and more reports have to be evaluated.

With such a vast amount of workload and information to handle, organizations must figure out how to optimize the execution process, process all the data, and provide feedback in not only a fast, but also an accurate fashion.

AI/ML is one of the promising solutions. New algorithms are developed to help users generate better test cases. Predictive modeling is leveraged to help decide where, what, and when to test. Smart analytics and visualization will help teams understand the big picture of their test scenarios and make decisions faster, better.

Challenges and potentials of AI/ML in software testing

However, the maturity of these technologies is still under development. Budget allocations for AI projects seem to have dropped, compared to 2018 (Capgemini World Quality Report 2019-20). Feedback on AI project commitment also decreased in a lot of scenarios. The assumptions are that organizations are still not confident enough to invest in AI. Furthermore, the maintenance cost may be higher than what organizations desire. 

In contrast, adoption levels for ML projects seem promising in 2019, and are used to predict defects and prioritizing which test cases to use. Huge collections of data need to be gathered, the ML mechanisms need to prove that they work — but the anticipation for this technology is, no doubt, growing.

 

We’ve been surrounded by a world of AI/ML. These two notions are widely applied to a majority of aspects — including software testing. 

Although these concepts are no longer new, the increasing abundance of available data and technological advancements opens up more opportunities for AI and ML in testing.

3. Intelligent Automation

The next item in this software testing trends list is about applying intelligent automation frameworks, tools, and techniques. 

In early 2019, it was speculated that more organizations would continue to apply automation to software testing projects. 

This is mainly due to the shift toward Agile and DevOps. The increasingly high demand for Quality at Speed requires teams to automate the mundane activities, so that they can focus on strategic planning and evaluating decisions. 

Automation — if applied properly — will allow software development teams to increase test coverage, improve test efficiency, receive faster feedback, reuse test cases, detect bugs early, and more. As a result, teams can ensure a higher quality of the delivered software. 

About 44% of organizations expect to automate 50% or more of all testing in 2019, according to a study on test automation trends. Teams that reach this level of test automation see numerous benefits.

Promising adoption rates of test automation 

The adoption rate of automation saw progress in 2018, and was expected to escalate in 2019. This has been shown to be true. 

Research shows that organizations were positive about the benefits accrued from automation in 2019. More teams have realized the benefits of applying automation to their SDLC, including better control of test activities, more transparency, and more accurate detection of defects. They also reported that automation helped them reduce unpleasant outcomes, such as test costs, test cycle time, and overall security risk.

 

Challenges with test automation

The other side of the coin is the challenges faced by organizations while adopting automation. 

Almost two-thirds of the respondents in a study found it difficult to automate because their applications change too much with each release. Lack of skills and appropriate resources are also major obstacles of automation.

What’s next for software test automation?

Moving forward, the concept of test automation has been popularized for about 20 years now. However, many dilemmas are still in the picture. 

A key reason why teams have not been able to achieve their desired outcomes of automation is because most automation frameworks were designed to automate only manual tasks. We need an automation framework that:

• significantly reduces the programming effort, especially for teams that do not have much programming expertise see Katalon Manual View
• intelligently decides when to perform certain tasks such as execution, without human interference see Katalon Smart Wait 
• is dynamic enough, such as using cognitive computing techniques to identify test objects and screen elements effectively 
• prioritizes, identifies, and executes the critical test cases from the automated suite
• provides its own test data

4. Test Data and IoT Testing

The continuous expansion of the Internet of Things (IoT) has immersed over the past years. According to Gartner, by 2020, the number of IoT devices all over the world will reach 20.4 billion. More IoT devices means more online connection and data exposure — which means, more risks. 

In 2019, IoT was expected to be conducted in testing. IoT testing is the technique of checking IoT devices. These types of testing include:

• Usability testing: tests the usability of IoT systems
• Performance testing: tests the performance of the connected devices in an IoT network
• Compatibility testing: checks the compatibility of devices in IoT systems
• Security testing: validates user authentication processes and data privacy controls
• Data integrity testing: validates data integrity
• Reliability and Scalability testing: sensors simulation using virtualization tools

The rise of IoT systems is closely connected to the growth of applications of AI/ML to help generate test data and data projects. The automation industry also expects to see an increase in usage of cloud-based and containerized test environments, and solutions for the lack of test data.

It is suggested that QA teams need to step up their game if they want to ensure security in IoT systems. Three critical steps that they need to take on include: applying continuous security testing, being strategic on what needs and does not need to be tested to be operationally efficient, and implementing service virtualization as part or their automation strategies.

5. Behavior-Driven Development Reaches New Maturity Stage

The final item of the latest trends in software testing is about behavior-driven development (BDD).

As mentioned in the 12th Annual State of Agile report, only 16% of organizations apply BDD methodologies in 2018 — but this number was forecast to increase in the next year. 

A byproduct of increasing test automation is the growing maturity of BDD. As a matter of fact, more teams were expected to flow through the BDD maturity model. This model includes five stages:

1. Embrace BDD collaboration
2. Implement BDD tools and frameworks 
3. Connect systems for development and automation
4. Standardize continuous integration and systemic collaboration
5. Report on BDD success

Conclusion

We hope this recap has played a part in giving better insights on the software testing trends of 2019 — so that organizations can reflect to reinforce their strategies. These trends are among the latest trends in the software testing big picture. The quality assurance landscape will continue to evolve. We’re excited to see what’s going to change and what’s going to be introduced in the next year.

Among these trends, which one has worked for your organization? Which is your favorite? Share your thoughts with us.

Original post: Software Testing Trends 2019 Recap | Industry Insights

Top 10 Benefits of Continuous Integration & Continuous Delivery

Continuous Integration (CI) allows you to continuously integrate code into a single shared and easy to access repository. Continuous Delivery (CD) allows you to take the code stored in the repository and continuously deliver it to production. CI/CD creates a fast and effective process of getting your product to market before your competition as well as releasing new features and bug fixes to keep your current customers happy.

Want some more information about CI/CD? Check out our previous article: Complete Introduction to CI/CD

In this blog, we’re going to delve into the top 10 benefits of continuous integration and continuous delivery to help you decide if this is the right step for your organization to take.

Top 10 Benefits of Continuous Integration and Continuous Delivery

1. Smaller Code Changes

One technical advantage of continuous integration and continuous delivery is that it allows you to integrate small pieces of code at one time. These code changes are simpler and easier to handle than huge chunks of code and as such, have fewer issues that may need to be repaired at a later date.

Using continuous testing, these small pieces can be tested as soon as they are integrated into the code repository, allowing developers to recognize a problem before too much work is completed afterward. This works really well for large development teams who work remotely as well as those in-house as communication between team members can be challenging.

2. Fault Isolations

Fault isolation refers to the practice of designing systems such that when an error occurs, the negative outcomes are limited in scope. Limiting the scope of problems reduces the potential for damage and makes systems easier to maintain.

Designing your system with CI/CD ensures that fault isolations are faster to detect and easier to implement. Fault isolations combine monitoring the system, identifying when the fault occurred, and triggering its location. Thus, the consequences of bugs appearing in the application are limited in scope. Sudden breakdowns and other critical issues can be prevented from occurring with the ability to isolate the problem before it can cause damage to the entire system.

3. Faster Mean Time To Resolution (MTTR)

MTTR measures the maintainability of repairable features and sets the average time to repair a broken feature. Basically, it helps you track the amount of time spent to recover from a failure.

CI/CD reduces the MTTR because the code changes are smaller and fault isolations are easier to detect. One of the most important business risk assurances is to keep failures to a minimum and quickly recover from any failures that do happen. Application monitoring tools are a great way to find and fix failures while also logging the problems to notice trends faster.

4. More Test Reliability

Using CI/CD, test reliability improves due to the bite-size and specific changes introduced to the system, allowing for more accurate positive and negative tests to be conducted. Test reliability within CI/CD can also be considered Continuous Reliability. With the continuous merging and releasing of new products and features, knowing that quality was top of mind throughout the entire process assures stakeholders their investment is worthwhile.

5. Faster Release Rate

Failures are detected faster and as such, can be repaired faster, leading to increasing release rates. However, frequent releases are possible only if the code is developed in a continuously moving system.

CI/CD continuously merges codes and continuously deploys them to production after thorough testing, keeping the code in a release-ready state. It’s important to have as part of deployment a production environment set up that closely mimics that which end-users will ultimately be using. Containerization is a great method to test the code in a production environment to test only the area that will be affected by the release.

6. Smaller Backlog

Incorporating CI/CD into your organization’s development process reduces the number of non-critical defects in your backlog. These small defects are detected prior to production and fixed before being released to end-users.

The benefits of solving non-critical issues ahead-of-time are many. For example, your developers have more time to focus on larger problems or improving the system and your testers can focus less on small problems so they can find larger problems before being released. Another benefit (and perhaps the best one) is keeping your customers happy by preventing them from finding many errors in your product.

7. Customer Satisfaction

The advantages of CI/CD do not only fall into the technical aspect but also in an organization scope. The first few moments of a new customer trying out your product is a make-or-break-it moment.

Don’t waste first impressions as they are key to turning new customers into satisfied customers. Keep your customers happy with fast turnaround of new features and bug fixes. Utilizing a CI/CD approach also keeps your product up-to-date with the latest technology and allows you to gain new customers who will select you over the competition through word-of-mouth and positive reviews.

Your customers are the main users of your product. As such, what they have to say should be taken into high consideration. Whether the comments are positive or negative, customer feedback and involvement leads to usability improvements and overall customer satisfaction.

Your customers want to know they are being heard. Adding new features and changes into your CI/CD pipeline based on the way your customers use the product will help you retain current users and gain new ones.

8. Increase Team Transparency and Accountability

CI/CD is a great way to get continuous feedback not only from your customers but also from your own team. This increases the transparency of any problems in the team and encourages responsible accountability.

CI is mostly focused on the development team, so the feedback from this part of the pipeline affects build failures, merging problems, architectural setbacks, etc. CD focuses more on getting the product quickly to the end-users to get the much-needed customer feedback. Both CI and CD provide rapid feedback, allowing you to steadily and continuously make your product even better.

9. Reduce Costs

Automation in the CI/CD pipeline reduces the number of errors that can take place in the many repetitive steps of CI and CD. Doing so also frees up developer time that could be spent on product development as there aren’t as many code changes to fix down the road if the error is caught quickly. Another thing to keep in mind: increasing code quality with automation also increases your ROI.

10. Easy Maintenance and Updates

Maintenance and updates are a crucial part of making a great product. However, it’s important to note within a CI/CD process to perform maintenance during downtime periods, also known as the non-critical hour. Don’t take the system down during peak traffic times to update code changes.

Upsetting customers is one part of the problem, but trying to update changes during this time could also increase deployment issues. Make sure the pipeline runs smoothly by incorporating when to make changes and releases. A great way to ensure maintenance doesn’t affect the entire system is to create microservices in your code architecture so that only one area of the system is taken down at one time.

Conclusion

There are many tools that can help enable a smoother transition to a CI/CD process. Testing is a large part of that process because even if you are able to make your integrations and delivery faster, it would mean nothing if was done so without quality in mind. Also, the more steps of the CI/CD pipeline that can be automated, the faster quality releases can be accomplished.

Check out Katalon Studio for better test automation and CI/CD implementation.

Originally published at https://www.katalon.com/resources-center/blog/benefits-continuous-integration-delivery/

Apply Shift-Left Testing Approach to Continuous Testing

As the demand to release quality software products in the short amount of time continues to accelerate, Agile and DevOps teams have to adopt new approaches to avoid falling behind. Continuous testing and shift-left testing are two breakthrough solutions in this modern era of software delivery. In this blog, we will walk you through these two notions, their benefits, and how to implement them into your software development life cycle (SDLC).

What is Continuous Testing?

Continuous testing uses a procedure of testing early and often throughout the development lifecycle. Incorporating continuous testing into your organization’s testing strategy accelerates your time-to-market but keeps (or even improves) the quality your customers expect.

Additionally, continuous testing enables constant feedback for developers to fix bugs before being released to production. Imagine being able to increase your level of quality output even faster through automated tests performed at every stage. While automation is not a requirement of continuous testing, it does help the process run much faster and, as such, is highly recommended.

To learn more about the benefits and challenges of continuous testing, refer to the article Introduction to Continuous Testing.

What is Shift-left Testing?

Shift-left testing comes about to test earlier in the development process. Even with Agile teams breaking away from the traditional Waterfall development model, testing always seems to be the last step.

For example, if everything is completed on the developers’ side and they begin new projects, but then tests come up with bugs that have to be fixed before it can be released to production. The developers would then have to shift focus from the new projects to fix bugs in the last project or the last project would have to wait until a new release cycle. Talk about a waste of time!

In an effort to bring testing earlier into the development lifecycle while improving quality measures, tasks are being shifted left rather than the traditional method of keeping delivery and testing requirements to the right side of the development model. If possible, testing should occur from the very beginning of the design phase to build an appropriate testing strategy.

As testing will occur throughout every stage, even at the end, finding problems as soon as possible reduces the amount of time spent resolving them and reduces the change of testing becoming a bottleneck to a fast release.

Key Benefits of Shift-left Testing

• Early detection: Find bugs early and fix them before they become a problem in production
• Cost savings: Time and resources can be quickly used up. Shift-left testing helps reduce that problem and saves you money.
• Reliable testing: Increase your testing reliability by using the shift-left testing procedures
• Teamwork makes the dream work: Create a sense of unity amongst your developers and testers by keeping differences between them from creating a bottleneck
• Fast delivery: Deliver your product to market faster
• Development pipeline: Shift-left testing helps to perform testing as soon in the development pipeline as you can achieve.

Challenges of Shift-left Testing

• Planning: Shift-left testing can be difficult to incorporate without an effective plan in place before you begin
• Project Management: Properly prepare and train your project managers to incorporate shift-left testing into their processes
• Quality control: Maintaining excellent quality levels during the training and transition phase
• Developers: Developers can be resistant to testing and should be prepared to add testability to their skillset
• Silos: Reduce the silos in your organization to provide swift feedback to fix problems faster and more efficiently
• Audits: If your organization does not actively participate in regular code audits, make sure this is set up to ensure the new testing procedure is working as intended

Why Shift-left Testing Matters in Continuous Testing

Shift-left testing should matter to you because it emphasizes the test early principle. More and more, companies are finding out that style is simply not conducive to rapid releases. Time is money, after all. Shifting testing practices left and incorporate testing as early as possible allows software businesses to beat their competition to the market.

While shift-left testing might sound like the perfect approach to help you in the testing process before rolling out new products, the technique alone is not enough.

Your ultimate objective is to automate and test as many aspects of the product as possible while reducing the amount of time invested in the testing phase. This is why shift-left testing should come into the picture.

Shift-left testing should be incorporated into continuous testing so that testers can generate more frequent, holistic and more practical tests with the implementation of real functional data. The combination “shift-left continuous testing” adds to the automation of your application testing, ensures that it is utilized as much, as early, and as continuous as possible throughout the product development pipeline.

In other words, by incorporating shift-left testing and continuous testing, bug detection can be done more efficiently in the early stage, resulting in higher quality feedback and faster issue resolution with lesser effort.

How to Apply Shift-left Testing in Continuous Testing

Now that you have understood the two definitions, let’s unravel these concepts to see how they fit into the big idea.

Shift-left testing is a part of continuous testing, as it employs the same process of testing at every stage and as often as possible. While continuous testing is part of the process of executing tests in the DevOps pipeline to get feedback on bugs as soon as possible, shift-left testing focuses on defined objectives. Therefore, if continuous testing incorporates the entirety of the DevOps pipeline (i.e. from planning, designing, developing to deploying), shift-left takes a part of each cycle and sets testing strategies for each task in the cycle. Shift-left testing enhances continuous testing and makes it a stronger weapon against bugs.

In addition, you can apply shift-left testing through behavior-driven development (BDD). BDD is a versatile Agile software development approach that enhances collaboration between developers and non-technical team members such as managers.

Katalon Studio is a powerful tool for you to implement BDD into shift-left testing through Jira integration.

By integrating with Jira, the non-technical department such as testers and managers can directly pull requirements from Jira to BDD files, which are already integrated in Katalon Studio. QA testers can then write test cases and send back to developers to run.

With this approach, managers will understand what happens throughout the software delivery process thanks to the ubiquitous language of BDD; testers will not have to wait until developers finish coding. If defects or errors occur, either the QA or development team can quickly re-run test cases and make changes accordingly. Discover Jira in Katalon Studio

Check out Katalon Studio for better test automation and CI/CD implementation.

Originally published at http://www.katalon.com/resources-center/blog/shift-left-testing-approach

What is End-to-End (E2E) Testing? All You Need to Know

End-to-End Testing: What is it?

End-to-end (E2E) testing is a technique that tests the entire software product from beginning to end to ensure the application flow behaves as expected. It defines the product’s system dependencies and ensures all integrated pieces work together as expected.

The main purpose of E2E testing is to test from the end user’s experience by simulating the real user scenario and validating the system under test and its components for integration and data integrity.

Software systems nowadays are complex and interconnected with numerous subsystems. If any of the subsystems fails, the whole software system could crash. This is a major risk and can be avoided by end-to-end testing.

Benefits of End-to-End Testing

End-to-end testing has been more reliable and widely adopted because of these following benefits:

• Expand test coverage
• Ensure the correctness of the application
• Reduce time to market
• Reduce cost
• Detect bugs

Modern software systems allow subsystem interactions through advancements in technology. Whether the subsystem is the same or different from the main system, within or outside the organization, subsystem failures can cause adverse effects throughout the entire system.

System risks can be avoided by performing the following:

• Verifying the system flow
• Increasing test coverage areas
• Detecting issues associated with the subsystem

E2E testing broadly appeals to many groups:

• Developers benefit from it because most of the testing is offloaded to others.
• Testers find it useful because writing tests that verify real-world behavior help them avoid problems and achieve a greater sense of accomplishment.
• Managers like it because user simulation tests let them know how a failing test would impact the user.

When to Apply End-to-End Testing

There are many scenarios to apply end-to-end testing. Here’s an example:

Before testing, designers and developers create a list of which UI, functions or features that need to be performed. Data flow should be tracked between systems to find dependencies, flaws, and inaccuracies. Teams also have to prepare the conditions – factors that may change any interface options – before testing.

E2E testing is regularly conducted on finished products and systems, making each review a test of the completed system. A second test will take place if the system does not output what is expected or if a problem is found. In this case, the team will have to record and analyze the data to determine the issue’s origin; then fix and re-test them.

End-to-End Tests vs. Functional Tests

End-to-end tests are not just several unit tests and functional tests strung together – they are more complex and carry more risks. We’ve listed the main differences between functional and E2E tests to illustrate this further.

Functional Tests	End-to-End Tests
Testing is limited to one single piece of code or application.	Testing crosses multiple applications and user groups.
Ensures the tested software meets acceptance criteria.	Ensures a process continues to work after changes are made.
Tests the way a single user engages with the application.	Tests the way multiple users work across applications.
Validate the result of each test for inputs and outputs.	Validate that each step in the process is completed.

End-to-End Testing Lifecycle

An end-to-end testing lifecycle consists of four components: test planning, test design, test execution, and results analysis.

• Test planning: Specifies key tasks, associated schedule, and resources
• Test design: Test specifications, test case generation, risk analysis, usage analysis, and scheduling tests
• Test execution: Executes test cases and documents testing results
• Results analysis: Analyzes test results, evaluate testing, and perform additional testing if necessary

End-to-end Testing Methods

Horizontal E2E testing

A commonly used method occurring horizontally across the context of multiple applications and easily takes place in a single ERP (Enterprise Resource Planning) application. Here’s an example. A Web-based application of an e-commerce system includes accounts, product inventory status, and shipping details.

Vertical E2E testing

This method refers to testing in layers, meaning that tests happen in sequential, hierarchical order. To ensure quality, each component of a system or product is tested from start to finish. Vertical testing is often used to test critical components of a complex computing system which does not typically involve users or interfaces.

Metrics for End-to-end Testing

Some of the many metrics used for E2E testing are:

• Test Case Preparation Status: Used to determine the specific position of the test cases that are under preparation, compared to planned test cases.
• Test Progress Tracking: Test progress should be tracked on a weekly basis. This step will provide regular details of test completion percentage, such as passed/failed, executed/unexecuted, valid/invalid test cases, and so on.
• Defects Status and Details: It gives a weekly percentage of open and closed defects. Also, weekly defect distributions are based on severity and priority.
• Environment Availability: The actual number of operational hours and hours scheduled per day for testing.

Automate End-to-End Testing in Katalon Studio

Katalon Studio is an excellent automation tool that can make your end-to-end tests less complicated. There are three core features in Katalon Studio that you can use to apply E2E testing: recorder, built-in keywords, and custom keywords.

Recorder

Different use cases are often mixed together in different orders and variations. But we can call each grouping of use cases a user journey. Technically, a user journey is a collection of steps in which each step corresponds to a user action. Collectively, they represent a typical user session.

Katalon offers the Recorder feature on Web and mobile to help you accomplish the task of creating user journeys without any hassle. The Recorder essentially watches and records all your movements on the application so that you can focus on creating the journey itself.

Built-in keywords

A user journey usually consists of hundreds of steps. When a test case – which represents a user journey – fails, it may be easy to pinpoint superficial causes (e.g. a change in a Web element’s property). However, it is harder to diagnose the actual cause solely based on the fact that the test has failed. It could have been due to the current page is not the same page that was recorded because at some points the test case went off the right track.

To ensure that the user journey has to go through certain milestones, test oracles are necessary. Test oracle refers to a mechanism to ensure that the test case is being executed as expected. In Katalon, a set of built-in keywords that implement this concept is provided. You can assert or verify a web element against certain text that you expect it to contain, or its properties against some expected properties and many other types of expectations check.

Custom keywords

Depending on the business logic of your application, there may be behaviors that occur across different pages but differ only in some known characteristics. In such cases, it is a best practice to capture these behaviors in a behavior template that can be filled with specific information when necessary.

In Katalon, a custom keyword can be defined to represent such a behavior template. Once defined, it can be reused in different test cases and even in different test projects. You can even share your custom keyword as a plug-in through Katalon Store so that others can benefit from it.

Originally published at https://www.katalon.com/resources-center/blog/end-to-end-e2e-testing/