Insights Blog Current Trends in Software Testing (IoT Testing, Rise of Automation, and More)


Current Trends in Software Testing (IoT Testing, Rise of Automation, and More)

As technology develops, software testing becomes more important than ever before. Learn how automated testing is impacting the software testing industry.

From automated testing to the rise of AI-driven applications and systems, here’s what you need to know about software testing this month. 

Our webinarThe 4 Stages of Achieving Agile Testing Maturity will help your organization achieve improved product delivery predictability, faster time-to-market (TTM) and efficient QA-managed optimization.

IoT Testing

The Internet of Things (IoT) refers to the network of devices that connect and share information on the internet. 

IoT devices include mobile smartphones, laptop computers, tablets and more. These machines have the ability to wirelessly connect to the internet and can “talk” to one another online. IoT-connected technology is rapidly growing in popularity, especially in the industries of fitness, automotives, healthcare, and home automation. 

This constant connection provides users with faster access to information and digital intelligence, but it can lead to challenges for quality assurance testing engineers because IoT devices don’t work with traditional software testing. 

IoT testing can have many different facets, but often focuses on specific aspects of technology:

  • Security 
  • Analytics
  • Device
  • Networks
  • Processors
  • Operating Systems
  • Platforms
  • Standards

The upcoming shift to highly-available and constantly connected 5G technology will only increase the need for efficient IoT testing. The intricacy of 5G networks can benefit from service-oriented architecture testing, which helps QA engineers leverage and enhance IT-based technology to improve, enhance and troubleshoot broadband wireless communication. 

As IoT-enabled machines become more commonplace, the importance of bug-free, top-performing software systems is a necessity. QA testing engineers must know how to effectively test embedded systems and hardware. Specialized quality assurance software not only enhances the overall user experience, but also allows devices to connect safely and securely—without risking user data or personal information. As the demand for IoT-connected technology grows across a variety of industries, comprehensive IoT testing becomes even more imperative in the software testing space. 

Check out our webinar to learn about the importance of achieving more efficient product delivery predictability, faster time-to-market (TTM), and accurate QA-managed optimization.

The Rise of Automated Testing

Software testing inevitably becomes more difficult as technology develops, so testing platforms must evolve alongside changes in tech. Software QA testing was once completely manual, but automated testing has since developed into a complementary aid to manual testing, and using it can be beneficial when checking for performance and functionality shortcomings. Here are four types of automated testing

  1. Codeless or scriptless automated testing: Reduces or removes total amount of coding necessary for tests
  2. Agile and DevOps: Focuses on continuous improvement by running frequent tests 
  3. Big data testing: Extracts, transforms and loads (ETL) testing to make sure data is correct before sending it to the database
  4. Mobile app testing: Requires quick end-to-end testing on a regular basis

Automated testing has become more important than ever, and automated processes and development tools must effectively carry out various forms of load testing quickly and efficiently. 

Software Testing for AI-Driven Apps and Systems

How do you accurately test artificial intelligence? Unlike software systems of the past, AI-based technology can adapt, learn and change its behavior over time, which often makes it unpredictable and difficult to test. 

AI testing must be approached with flexibility and the ability to modify testing methods. Although tried-and-true software testing formulas can sometimes be used to assess AI-driven applications, QA engineers should also apply various methods of A/B testing and non-functional testing to predict various outcomes. 

AI testing should include testing methods that input information similar to the data that the AI receives. This data should be as specific as possible to allow the algorithm to analyze all possible testing outcomes and compare the expected output to its own output. 

Problems with AI testing occur when previously established testing models fail to predict or coincide with real AI behavior. To compensate for this misalignment, testing engineers should be willing to tailor ineffective testing methods when necessary. 

The software testing industry is constantly learning, adapting and eliminating the unnecessary. For more information on how Qualitest can help, contact one of our experts today.

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