The Future of Automated Testing

DZone 's Guide to

The Future of Automated Testing

Artificial intelligence and machine learning will make up for a dearth of talent.

· DevOps Zone ·
Free Resource

To understand the current and future state of automated testing, we spoke to 14 IT professionals intimately familiar with automated testing. We asked them, "What’s the future of automated testing from your point of view? Where do the greatest opportunities lie?"

Here's what the respondents told us:


  • Automated testing will be influenced a lot due to artificial intelligence (AI) and machine learning (ML). There are a lot of opportunities to leverage ML techniques to learn from past test executions and optimize automated tests. Used to autogenerated test cases. Leverage NLP to query the test results, analyze, and identify the root cause. We’ll also see more visual test development and automation. We will be able to virtually design unit tests. This allows us to focus on designing the test rather than the complexity of the code. Continuous testing has become the norm and Agile development strategies need to integrate tests into the CI/CD pipeline.
  • Our enterprise customers are doing digital transformation and technology upgrade programs. Quality is becoming more prominent as never before, the number of channels and platforms to be tested have increased multifold. Automated testing is key for companies to achieve their business goals of time to market, with optimized cost and quality. An automation-first culture will become pervasive, and the traditional approach of adding to testing debt will diminish over time. The greatest opportunities lie in 1) AI-powered automated test designing based on the concept of model-based testing. It is adopted by one of our major travel testing customer. 2) Adaptation of AI and ML techniques for next-generation cognitive automation have the capability of self-learning, self-healing, and self-adopting. 3) Leverage AI-powered cognitive vision tools to emulate the human eye and brain for reporting visual differences. 4) There is also an emergence of cognitive testing platforms (e.g. Mabl, Functionize, etc.). We are in the process of partnering with such niche players to accelerate adoption. All the above will be done using the business-driven mindset.
  • There will be an increase in the adoption of AI tools and techniques to optimize testing to reduce the time to develop and run and increase efficiency. Given the complexities in reliably creating test environments and test data, improvements in these areas will enable better and more consistent test automation throughout the pipeline. A combination of these is likely to go even further to create self-healing test environments that will automatically resolve issues, thus saving time instead of debugging and fixing.
  • We’re in a state of flux right now. In the near-term, we’re focused on who is doing the automation which is being driven by who is on staff. We need automation and who is on staff is driving the how. The future in the long-term is robotics according to Jason Huggins the co-author of Selenium and founder of Tapster which conducts robotic testing on mobile devices. Another thing is AI/ML what should be tested in test coverage. Ultimately, AI/ML will be able to generate automated tests.
  • Key areas for us are all AI-related. I look forward to seeing self-healing tests with a confidence score like root-cause analysis. Maintenance is the first big bottleneck. We’re always looking for ways to optimize and improve. Reduce root cause analysis time. Look for test-case creation as well. Other big areas are set-top box platforms like 10Foot – Netflix, Hulu, and Amazon where experience is interwoven with the functionality of the application. This reminds me of the beginning of smartphones with Android and iOS where the initial experience was very poor. Next is voice automation with cool companies like Bespoken providing voice test automation. Apps today are more short-term with a short lifespan. Testing becomes more recurrent with voice and automation being very valuable to quantify quality. IoT is difficult to test since every device is different. Think about common problems across all of these and what are solutions so we can provide value to customers. It’s still difficult to do, and there is a lot of pain. 
  • Automation is the right thing to do because it’s fast and cost low. Blend the best of both workloads with automation while reducing the pain of creating and maintaining the tests. Leverage AI/ML to help customers create and maintain automated tests. We have humans running test and then using data to inform AI/ML to automate tests going forward. Customers don’t have to choose between manual and automation we write and execute the test through robots with AI and ML. If there’s a problem, humans are deployed to get accurate results in a short period of time. Automation is going in the right direction. There are a lot of solutions that solve the challenges with automation learning how humans implement tests. 
  • There are some challenges within test automation that have been lurking for many years. We’re hearing a lot of ideas about how AI can assist with those challenges. We’re an early provider of AI-driven test automation solutions with visual testing that mimics how the human brain and eyes see and process the visual appears of apps.


  • Automated testing will be used for everything – performance, load, usability, security, all testing will be automated. Security is just part of it. Integrate into Jenkins, JIRA, and in the build pipeline.
  • We will see massive increases in the level of automated testing through model-based testing and linking to business outcomes with automated feedback loops to improve speed and quality.
  • DevOps is a unifier and enabler. Cross-trained teams enable better security, better tooling, and more shared responsibility.
  • The greatest opportunities lie in post-deployment verification activities such as health checks and monitoring given that with distributed systems it is important to be the first one to know when things go wrong in production.
  • I see continuous delivery being the biggest opportunity for test automation. The drive to continuous integration and continuous deployment puts pressure on automated testing. The tests need to be comprehensive, efficient, up to date and running virtually flawless. If the automation breaks, the pipeline breaks.
  • Testing needs to take place throughout the lifecycle. Products will test themselves. Printers go beyond self-monitoring to self-reporting with accelerometers to test while in use. We will get massive amounts of data to inform different test plans.

Here’s who shared their insights:

performance ,automated testing ,machine learning ,test automation ,devops

Opinions expressed by DZone contributors are their own.

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