Software testing trends in 2021 are leaning towards security needs and advances in machine learning technology as priorities. These trends also incorporate devices, device connectivity, consumer personalization, and how consumers access services.
Below are seven software testing trends that are passing with flying colors in 2021.
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Using Agile and DevOps
Agile and DevOps are organizational philosophies that are quickly becoming the driving forces behind many successful software testing practices. Deciding between Agile and DevOps depends on a company’s objectives and focus points.
Agile is a set of management practices that software developers use to stay on top of their ever-changing software. Applying Agile to large-scale projects can result in dramatic gains, such as the instant personalized offerings of Spotify’s Discover Weekly playlist.
Development Operations, or DevOps, focuses on bringing people, processes, and technology together in real-time to make a product of top value to the consumer. It focuses on continuous improvements throughout the infinite looping cycles of software development. Companies like Amazon, Etsy, Netflix, and Google all use DevOps processes.
Regardless of which philosophy is best for your team, your customers will still deserve the best quality possible from the finished product. Many development teams partner with third-party software testing companies like XBOSoft to ensure thorough ongoing testing to ensure customer satisfaction. You don’t want your end-users to become your software testers, and that’s why it’s best to iron out the kinks before your product goes to market.
Blockchain-powered services can be an excellent fit for software that needs to converse between platforms while safeguarding transferred data. Blockchain solutions have had a compound annual growth rate of 73.2 percent over the last five years, and they won’t be slowing down any time soon.
Banking and stock trading aren’t the only industries with software that needs blockchain testing. Enterprises that need to keep secure records will insist on more blockchain-powered services in the future. These include retail companies, healthcare, airlines, education, and real estate.
Software that includes blockchain technology must submit to rigorous testing to ensure that data remains encrypted and valid during the transmission process. Integrating blockchain technology into dated legacy programs requires vigilant attention to detail to avoid losing or compromising data.
Artificial Intelligence and Machine Learning Testing
Artificial intelligence (AI) and machine learning (ML) have become some of the trendiest software testing approaches. Software testing need not be a manual process when it’s possible to allocate testing tasks to automated programs.
There are differences between artificial intelligence and machine learning. AI can’t wholly replace humans since humans need to input data and provide algorithms for testing purposes. ML is a subset of AI, wherein software learns to recognize patterns, solve problems, learn, and make predictions based on data.
AI and ML have applications in test creation, analysis, and maintenance. AI allows clicking through a test case to create an automated test script in minutes rather than hours. Meanwhile, ML models the site, records data, and learns what each button press means.
ML makes testing more reliable because modeling compares how the design intended the software to run with how it actually does. Additionally, it can deduce when something has changed in the testing environment and still perform tasks correctly without error.
Chatbots are becoming increasingly popular as a way for companies to provide round-the-clock customer support. The industry estimates that around 85% of customer interaction will be with chatbots by the end of 2021.
Chatbot trends currently edge toward using AI chatbots rather than decision-tree chatbots. ML-integrated chatbots can even access user details from previous conversations and update profiles.
Chatbot testing with different human users ensures chatbots are effective and efficient in answering both basic and domain-specific questions.
It’s vital to test AI chatbot conversation flow, its understanding of text intent, its ability to make small talk, and how it handles irrelevant questions. A chatbot should also be able to navigate skipped conversational steps and provide appropriate responses to emotional humans. Though they’re not the same as a human customer service representative, these talking machines are the next big thing for customer relationships.
Internet of Things (IoT) Testing
The Internet of Things (IoT) refers to all physical objects that connect to and exchange data with other devices and systems over the internet or within a network. All objects may have their sensors, software, and technology. An example would be scanning devices in a store connected to a store database or appliances users can control with a phone app.
Demand for IoT technology is only increasing among companies and individuals. This trend promises to be more vulnerable to cyberattacks, especially when adapting legacy devices to an IoT network. One weak device can leave the entire network open to compromise.
Unfortunately, IoT malware worms are becoming more common, while only 41% of companies have mature IoT strategies. Differences in hardware and operating systems make it impossible to protect all devices the same way, so IoT security testing should be thorough in 2021 and beyond.
Automated Mobile Application Testing
With new Android and iOS devices and new operating systems (OS) rolling out constantly, testing mobile apps on new devices and OS is necessary. Mobile app testing also determines whether an app is still compatible with older devices and OS versions.
Manual app testing is cumbersome and time-consuming, involving testing on real devices or using individual android emulators or iOS simulators.
Automated app testing is a far more efficient way to test apps. The process involves using a platform that offers a real device cloud for testing the app on an extensive range of new and old devices and OSs.
Automated testing provides quick feedback, faster bug discovery, and extensive system testing. Thus, developers can make updates more quickly to accommodate new devices and OS changes.
Codeless Automated Testing
The future is here when it’s possible to create an automated test without writing a single line of code.
Codeless automated testing sounds too good to be true since it can free up hours of time software developers would typically have to spend automating a test. Codeless automated testing includes codeless user interface tools, flow-based scenario testing, few coding skills, and only around an hour per test.
Codeless automation can also be self-healing with the help of ML, so software changes don’t affect the test’s ability to run.
It’s important to note that cloud-based codeless testing can be more reliable, faster, more collaborative, and more secure than on-site codeless testing.
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The Future of Software Testing
The need for software testing to be more robust and comprehensive will only become more necessary going forward.
Some testing still relies on an old-fashioned human touch. However, many ML software testing functions make software testing easier, take less time, and produce fewer errors. These 2021 software testing trends will keep passing the test for the years to come.
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