The Future of Software Testing
Which testing technology, tool or practice will be the “next big thing” in Software Testing?
In recent years, there has been a great evolution in the field of software testing, with new trends coming into IT industry services. The introduction of new technologies has brought the latest updates in the software design, development, testing, and delivery.
We have experienced a shift from manual execution of tests to automation. With a click of a button in Automation, for example, not only we can automatically run a lot of test cases, but also we can generate a detailed report that illustrates which test case, even which steps are failed.
Automation has covered many gaps during the last period, however it wasn’t alone enough to cover all requirements. It is nowadays impossible to imagine an automation framework without visual validation. Therefore, image-based testing using automated visual validation tools is getting more and more popular every day. There are many ML-based visual validation tools that can detect minor UI anomalies that human eyes are likely to miss.
Now, AI/ML tools have gone far ahead to learn the business usage scenarios of the application under test. While learning the application, they automatically collect useful data like screenshots, HTML pages and page loading time. Over time they collect enough data from the application so that they can train the ML model for expected patterns of the app.
As we see, changes in the testing trends would also have a significant impact on the quality assurance and software testing. In my opinion, there will be even bigger changes in the software testing industry than we have ever seen. Let’s have a look at the testing trends, which we might face in the near future.
The increasing expectations from the QA engineers
Software testing is an essential process for developing the bug free app. As a QA engineer, it is essential to have certain skills which in turn will help with testing the applications better. In the near future, it is highly possible that to be able to execute only functional or non-functional tests either manually or automatically will not be enough to stay competitive in the market.
QA engineers will have to learn more about the latest test management tool and testing strategies. Awareness of latest web and mobile technology trends is inevitable. There are several ways you can try to keep up to date with technology, such as checking for the top trends that well-known publications and online tech news website publish on a regular basis. Following people who are industry leaders on social platforms. Platforms like LinkedIn and Twitter allows you to follow leaders in the industry who regularly post the latest things on their feed.
Performance, security, compatibility, and usability testing will be demanded by the QA. It is not a surprise to expect from software testers to understand the source code, which is written by the developers. Likewise, it is also inevitable to have DevOps skills such as using Docker, Kubernetes, VMs, having CI/CD pipeline knowledge, Database knowledge, basics of AI and ML, and effectively using cloud platforms to deploy and the test the application. In addition to all these, project management skill will be demanded. As a software tester, being able to manage a project means delivering the project after a complete testing. Hence, Project management will be an important skill not only because it leads to better management and delivery of results but also because it promotes a sense of responsibility.
All in all, it will most probably not enough to say, “I can only make functional or non-functional tests, and I don’t care in the rest of the application.” 😃
Automation’s Popularity Will Be Balanced
Surely automation testing have the benefits of increasing efficiency, getting faster regressions and thus contributing to timely project deliveries. It also removes the execution of repetitive test cases or regression cases manually and saves a software tester’s life. It’s obvious that the popularity of the test automation is growing tremendously in the recent years. We could confirm it by just checking the latest test automation tools. Every other year a new test automation tool is introduced in the market.
Currently, there has been a hike in automation and the requirement for automated QA engineers. Although the speed and efficiency of software testing augment considerably with automation, it can’t cover various aspects like design, user experience, and usability. This is where the manual test is better used, so it picks up where test automation leaves off. There are some certain areas, which you can not avoid using manual testing strategies. To name a few — partially Usability testing, UI and UX testing, Exploratory testing, and Ad-hoc testing. Either only manual or only automation is not the right approach.
Additionally, it should be considered that automation tests can work when the test scripts are ready, whereas manual tests are only as perfect as the QA engineers capability. The more experienced QA engineers you have, the better the test coverage. Incorporating both these testing can result in a harmonious balance of usability, functionality, speed, minimized bugs, and an overall better user experience. which we will be faced often in the future. Instead of trying to automate everything, which is also against one of the main software testing principle, the balance for both automated and manual testing in a software development procedure might be the expectation from the software testing in the future.
Codeless Automated Testing
Codeless automated testing tend to be more adopted in the future. In order to maximize the scalability of test automation, ‘Codeless Test Automation’ is introduced. Codeless test automation enables the testers and business users to automate test cases without worrying about the coding. It helps to achieve faster results and reduces the time expended to understand the code.
The quicker you automate, the earlier you get the desired results. Using codeless automation, the testing team can automate large test suites rapidly. The speed of delivery, as well as the go-live time, improves rapidly. Besides this, it decreases the time spent on automation. This ensures early detection of bugs during the software development lifecycle. Ease of test case creation, reusability, maintainability, time-efficiency are the main benefits of using codeless automation test.
Codeless testing tools are built on sophisticated AI technology plus visual modeling. Using such tools, software testers can generate easy test case scenarios with no coding knowledge. However, how does Codeless Automated Testing work? For instance, using Testsigma, the test cases are majorly written in an easy language like English, using NLP. These reports are transformed to code (in the backend) for implementation. Ghost Inspector, for example, each move in this tool can be created without the necessity of any coding. The tool makes it easy to ensure your website is properly working. Similarly, Katalon Studio and TestProject offers a recorder, which helps software testers to create automated test scripts in a very short time.
There are also some other test automation tools that use codeless testing techniques such as TestComplete, Ranorex, ACCELQ, TOSCA…etc. Because of the simplicity, speed, and the support from AI, it seems to be more adopted in the future.
ML and AI Adoption for the Test Automation
ML (Machine Learning) is an application of AI (Artificial Intelligence) that enables the software to automatically learn, explore, and imagine outcomes without human intervention. Machine learning has been used in various fields, and currently, it is inevitable to use it in software testing process.
Thanks to the rapid development in AI and ML, we, software test engineers, could tremendously increase our working efficiency. Currently, this technology has already been adopted with some automation tools. TestProject, for instance, provides us adaptive wait to prevent test failures due to application slowness or internet speed problems. There is also healing functionality, which heals the applications whenever UI changes, dynamic ID is available, or ID changes frequently… etc. Those capabilities help us not only to fix flakiness problem, but also to prevent false actions.
Likewise, Appvance (AI-based automated testing tool) uses AI for generating test cases based on user behavior. Test.ai (another AI-based automated testing tool) is one of the popular mobile test automation tools that use Artificial Intelligence to execute regression tests. Katalon Studio leverages AI-powered XPath options to locate objects, auto-heal during runtime, and adapt to UAT changes. There are other popular testing AI-based automation tools, such as Sauce Labs, Applitools, TestCraft… etc. also available.
What is more, in the future, AI-based automation tools can also provide suggestions and modifications for testers in all aspects, especially in coding such as giving suggestions to delete redundant codes and unnecessary steps. It might also provide solutions to those failed steps and enhance efficiency, which shortens the time to run a test and quickly finds any possible defects, and ensures the entire testing process becomes more efficient. It would also help QA teams to be not overloaded with work while testing large volumes of data, and running numerous repetitive tasks.
Robotic Process Automation (RPA)
Advances in software and AI world have paved the way for Robotic Process Automation (RPA). It is the most recent technology which has the capability to re-invent the business process management landscape. RPA Testing Services ensures faster test creation & test execution, lesser maintenance effort with cost savings. As tasks are automated, the process involves minimal workforce, which might bring changes in the company structure in the future.
Both functional and non-functional testing can be executed with the help of RPA robots. Automation testing strategy provides many features including starting from test data creation to triggering the bot, creating test cases and scripts, executing test cases by enabling automation scripting by leveraging automation tools and analyzing and delivering reports.
Cybersecurity & Penetration Testing
Cybersecurity testing, has been turned out to be a growing trend in Quality Assurance and software tests. Security tests offer you a comprehensive understanding of your enterprises’ weak points before hackers/ attackers do, and assist in detecting areas susceptible to security or cyber threats. Cybersecurity tests guarantee that if any downtime happens, it is not as expensive and damaging as if you were not prepared.
By learning the most common cyber-attacks the industry faces, the organisation can better prepare itself to stop those attacks and find out where its strategy is weak. Implementing robust security measures, you deter all but the most skilled and determined attackers. In order to do this you need to know where to focus. Conducting cyber security testing has three core benefits:Cyber security testing helps businesses prove and maintain compliance, Uncover vulnerabilities, and identify threats.
Since security testing is obligatory for innovative technologies to meet their full market value, it is inevitable to have more focus on security areas, and with regard to that growing need for software testers, who has a knowledge about cybersecurity.
Presently, a majority of the business operations are adopting Internet of Things (IoT), and the trend illustrates that it is going to be more adopted.
IoT apps and devices are tested for usability, security, connectivity, functionality, compatibility, and performance.IoT applications are complex as they blend together software, hardware, sensors, and databases. As the IoT market grows across verticals such as health, retail, automobile and more, the IT industry also needs to re-visit products and services offered in the IoT spectrum for development and testing.
Big Data Testing
Big data is the huge amount of data produced at a high speed. Testers have to confirm that terabytes of data are effectively processed using other supportive components and commodity cluster. This form of testing concentrates on functional testing and performance testing.
The major challenges faced by big data testing are scale, performance, continuous availability, data diversity, data security and QA infrastructure. The big data testing market is segmented into functional testing and non-functional testing. Functional testing involves data integration testing, extract, transform, load and quality testing, data repository testing, and data warehouse testing. Non-functional testing involves performance testing, load testing, velocity testing, failover testing, security testing, and infrastructure testing.
In the future, I believe, data will be the most valuable asset while gold and silver are the most precious in the past. Nowadays majority of big companies collects and analyses customer’s data to build business strategies. Amazon, or any other E-commerce like Alibaba recommend products according to customers searching behavior in online. Therefore Companies across industries continue to cope with immense data volume and different data forms. Higher dependency on data demands big data testing to guarantee integrity, accuracy, reliability, and quality of data necessary for making informed decisions by all enterprises.
It is of course not limited only to above mentioned areas, however it gives us an insight for the trends of the software testing. As a conclusion we could say, there will be a lot of changes and challenges in the future from testing tools to testing practices. As we know, whenever a new technology or trend enters the market, it stays longer if it is able to fulfil the majority of requirements and provides ease and comfort to the user. As quality assurance engineers, we should embrace constant development in software development and testing, constantly upgrade our technical skills and our mindsets. And who knows, maybe we will have a robot colleague, who will help us in all testing aspects from writing test cases to auto generate test codes, in our Agile team in the near future.
☕️ Happy testing! ☕️