Testing has developed over the years as software production and deployment have been leveled up to match industry expectations. The entry of automation through AI and ML and policies of DevOps, such as AI-driven quality engineering services has transformed the landscape of testing into a more planned and well-defined strategy than impromptu involvements.
This is exactly why there is a lot of talk around how automation will transform the future of testing, as experts form their reports sets those expectations. The upcoming Test Fest USA, 2020 event, signifies an important discussion on such worldwide views on testing advancements.
As we are expecting to see how the event will unfold and what opinions will encourage new trends over the testing world, here are some thoughts on how we see the future of testing with AI, ML, and IoT devices.
DevOps, SecOps, and now TestOps?
Previously, testing was performed only at the end of everything. This has begun to change in a lot of companies with a few yet hesitating on change. Automation scripts are inbuilt into every element of the development that can perform routine and patch tests without unnecessary expenditure of the tester’s time. Testers are also designing tests ahead of the development cycle, including themselves even at the nascent stages.
As we know, DevOps has taken on the new culture of integrating operations during the development phase, making a Shift Left strategy. The whole cycle of development with regular testing, maintenance, and feature releases are consistently achieved in concurrence with the dev and ops teams, reviewing for security alongside. In fact, the lines of differentiation among departments are becoming thinner and thinner in terms of the timelines of involvement. Hence, we introduce terms like SecOps and TestOps, to highlight the importance of new parameters to product management.
TestOps is the newest emergent idea of testing along with development. Though it seems complex, TestOps tools for automation can explain much of the confusion around it as organizations proceed with implementation and finally deliver consistency in their processes.
TestOps recognizes with the same patterns of DevOps, in looking at the ‘people, tools and processes:
Significant modifications to the development methodology must closely include the teams working at it. Following a bottom-up strategy, the teams are aligned with the expectations on the quality, structural modifications, and training information. They get familiarized with the new tools and the processes included. The valuable inputs from these teams can append to the organizational approach as coming from a functional point of view, helping in making informed choices.
As unavoidable elements of the DevOps and testing procedure, the tools are chosen to get the projects a success. In selecting the tools, again the respective teams and their adaptability, room for flexibility, and skill set require to be considered complete. Apart from matching with the budget, the tools, and frameworks required to give the maximum value for your appropriate product and use case. It’s not a ‘one size fits all’.
It’s wise, hence, to not choose the popular but to do enough scrums before picking a special tool or framework.
Befitting the agile approach, any strategy that includes operational changes requires moving little by little. It’s not the ideal mindset to strive for the ultimate processes that someone else has and fumble through implementation. Processes follow through with each small piece of implementation and it gets established throughout the life cycle of development.
That being said, automation is surely the future of Quality Engineering (QE) and testing.
AI-infused QE / Automation at Every Step
It is hard to switch over to automation for each manual task out there, simply because it is not sustainable nor has AI and ML arrived at a point where it is made possible. But we can safely perform automation at every significant juncture that will append to the derivation of insights from the testers themselves. Automation is also an ongoing method that is not a set-and-done process.
Gartner’s Strategic Planning Assumption report says, “By 2024, three-quarters of large companies will be using AI-enabled test automation tools that encourage continuous testing across the various stages of the DevOps life cycle”.
Many organizations still restrict QE testing to just the front-end of development and the UI. This is quite ineffective, like just touching the tip of the iceberg. AI-driven test automation scripts can now be employed for tools validation, version-control, load, performance, and of course, API testing. There are also some areas where you require to tread thoroughly when considering automation, like with regression testing.
While traversing the automation tools view, there may be plenty of open-source tools out there that can either be combined into your go-to application or used widely as separate entities. This saves the investment into resources through the procurement phase. A lot of open-source platforms work as substitutes for several testing practices.
The goal of automation should be quality, and hence the speed should complement the quality of testing. The test automation scripts are also undergoing tests for this purpose.
In summary, test automation set these advantages:
Like a smooth-running machine, all DevOps efforts that instill automation into the system, we strive to deliver continuous delivery through continuous integration and testing. Continuous testing can find out bugs in the development with pre-defined scripts that are supplied into the system. These scripts are working scripts that have experienced rigorous analyses.
Continuous testing guarantees tests at the initial phases of development, and it’s run for each change that occurs during the software development life cycle (SLDC).
Script maintenance becomes easier
Building scripts upon scripts that can adapt to changes make code maintenance a cakewalk. Newer tools in testing that are driven by AI can do specifically that, by innovating on the spot for every new application interface modification, or environmental change. It may be hard to accomplish this as a novice to TestOps, so it is better to rely on the fast-advancing tools in the market.
Test engineers must not run to the rescue each time a known bug occurs in the product’s life cycle. Self-healing systems learn from the huge number of data gathered that can train such models. This can repair broken ends in the front-end, or update dependencies, etc.
Reports and insights generation
Possibly one of the most useful sides to automated systems is the insights they can give to the testers about what is running and what isn’t. The ability of the algorithms to categorize different data and discover correlations and causations can produce breakthroughs in building test automation scripts for a long time.
Internet of Things (IoT) and Challenges
The major challenges for IoT testing and quality assuare rance due to the technical and sociological features such as architecture and connection parts, and security and data privacy. Balancing fast and simple connectivity with the security of data is what QE suggests for the extremely heterogeneous hardware and software components of IoT. Service availability can be confused with even a small malfunction.
The major factors that are regarded for the IoT application testing are:
- Communication and technologies rules at the application level
- Sensors/actuators to test for trivial variations in external environmental variations like temperature, humidity, pressure, etc.
- Backend simulations to manage object behavior
- Communication among various interactable devices
- Volatile and heterogeneous data in large volume
- Cloud computing and migrations for service extensibility
IoT mechanisms involve data acquisition and processing, execution, and a feedback mechanism in controlled atmospheres for IoT assurance. AI-driven software testing will consider all these circumstances and build automation software for every IoT component. It requires rigorous reviews and comparisons with the desired results.
Going the route of service delivery to evade risky integrations and budget obstacles will be the best choice for any IoT-related projects.
Will automation put testers out of work?
A most frequented question as this is, it has also shifted the most valid question. Are testers the easiest replacements? Pretty contrarily, automation can make the life of testers easier by driving away the most mundane and repetitive duties.
Yet, the most daunting reality is that testers require to adapt to the changing climate with new tools and processes. The arrival of automated processes such as Robotic Process Automation (RPA) and testing has decreased the dependency on humans while keeping them away from taxing and risky conditions. This will gradually increase the quality of a tester’s work, necessarily pushing them to change.
Automation has needed testing engineers to gain new skills that expand their workability and adaptation to the future, as with all other industry. Top-of-the-game testers are already concentrating on learning new toolsets, freeing themselves of too much maintenance. The mantra is now, ‘less maintenance, higher innovation’.
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