How do we foresee the future of Test Automation?

Test Automation is like a self-healing process for Software Testing and Quality Assurance. When software glitches keep mushrooming constantly, business risks grow and testing teams face major challenges. Automation frameworks enable testing and development teams to enhance the testing activities and accelerate the development tasks. According to a report released by SBWire, ‘The global test automation market will likely expand at a robust CAGR of 15.4% from 2017 to 2025 to become worth US$ 109.69 bn by 2025 from US$ 30.45 bn in 2016.’

With Artificial Intelligence and Machine Learning getting implemented across applications and products, testing needs to get much more result-oriented. Automation frameworks help teams to customize their testing activities as per the requirements of the project. Most importantly, the tools and platforms help to backtrack the activities and accordingly take decisions for implementing automation and building test cases as well.

As the development scene evolves and gets more complicated, test automation and automation platforms will have to twist their ways and ensure competency. It will be interesting to know the kind of future that test automation can expect in the light of the changing development scenarios.

Increasing relevance and application of AI and ML

The challenges surrounding testing and development are expected to only increase over a period of time. This implies that testing and development teams will have to induce more measurability and traceability within the QA processes. Hence, testing will have to leverage ML and AI to bring in more efficiency than an automation framework or a manual testing process.

Additionally, the testing methodologies will have to build predictable models to ensure seamless Continuous Integration and Continuous Deployment through the development cycle. Eventually, the focus will be on delivering experiences as against mere outcomes. That’s where the role of Testing and QA will transform substantially. AI and ML will become a part of the solution and not just a tool to support the process.

Manage large amount of data

Data forms a significant component in the software development and testing process. This data can take multiple formats and ultimately the type of data will more or less determine how the testing cycle will proceed and contribute to the overall application development. The kind of data collected will also help to improve testing and bring in added efficiency.

With methodologies such as DevOps and Agile getting implemented, data becomes highly critical for teams to build strong test cases that can contribute even in the longer run. Hence, there will be a growing need for testing to process large chunks of data and incorporate it within the testing gamut. This will also make the testing process more interactive and responsive for constant evolution.

Incorporating Smart Analysis and Smart Reporting structures

As we have discussed, data is critical for every testing process, as it helps to bring in effective analysis and highlight any potential historical vulnerabilities. For instance, during a security testing scenario, any testing group will need past analysis that will enable them to take smart decisions for better outcome. In this way, the developers will be equipped with accurate vulnerability data almost in real-time. This means that testing teams will have to incorporate structures and platforms that contribute towards smarter analysis and effective reporting of data.

Implementing AI for better Code quality

What you need ultimately is a code that ensures a quality application and is free from vulnerabilities. This is possible by implementing AI-enabled platforms that ensure effective validation across various parameters. Aspects such as smart reporting and smart analysis can be achieved by incorporating AI within the automation test results. This will also help to spot the bugs better and much early in the testing and development cycle.

The best option would be to bring in a hybrid model that incorporates both automated, manual, or AI-enabled testing platforms that are applicable for the concerned requirements. AI and ML can come in wherever needed, rather than just being applied for the sake of it. Automation can work better with smart data that gets reported effectively and analyzed smartly. It will be help to assure code quality even in the longer run by ensuring cost effectiveness as well.

Business models are being transformed as the world is riding the wave of digital. As enterprises look to be digital, they need best of the breed Quality Engineering services. Powered by intelligence and automation, architected with customer experience at its center, and built for the Agile and DevOps environment, these state-of-the-art Quality Engineering services leverage deep domain expertise to customize solutions according to the industry and are focused towards achieving Quality@Speed.

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