Software products have been evolving rapidly for the past few decades. From single-functionality dedicated software to complex features that encompass a multitude of functionalities, the growth of software applications has been anything but typical. Customer expectations keep on rising as they get used to the product, and the companies keep upgrading their software to meet them. One of the most widely used methodologies in software engineering is the agile model. While following this model, companies are expected to upgrade their products under several iterations with a faster release cycle. To achieve this goal, companies not only rely on faster development processes but also on modern testing mechanisms. Faster release cannot be achieved at the cost of quality. Software testing has been evolving to keep pace with the modern requirements, and hence, the advent of AI-powered testing is leaping. Human testing still has its strengths in certain areas, but the AI testing is slowly evolving to cover them all.
This article explores the strengths and weaknesses of AI testing, compares it with human testing, and finally concludes with the best testing approach modern software companies can adopt.
The evolution of software testing so far has seen three phases: manual, automation, and AI testing.
Software testing has come a long way in the past few years. From manual testing to automation and AI testing, the adoption curve has been quite steep. Traditional testing had its benefits, like creativity and flexibility when testers used to run test cases manually, log defects, and work with the development team to fix bugs. This was the simplest method of testing, as the testers could directly test what they wanted to validate, find bugs manually, and follow the process thereafter. But it had its own limits, which we will discuss in further sections.
The next phase of development in software testing came with automation. It allowed testers to automate frequently used test cases that can be run any number of times without consuming much time. This approach was especially useful in regression testing, where old test cases could be reused multiple times, with each iteration of the development, to validate existing functionality. But this required heavy scripting knowledge as all the test cases were required to be programmed in a coding language. Automation surely eased out the testing process, but created many technical barriers .
The latest development in software testing came with the usage of AI-powered testing tools. These tools not only automated the testing process but also allowed the testers to generate test cases through simple commands, thereby removing the technical barrier. The tools can learn and adapt to the growing complexity of the product and even predict potential defects much prior to running the test cases.
With the rapid adoption of AI testing currently, the question remains - how much of the testing process should be delegated to AI tools vs human testers?
An AI-based software testing approach uses AI elements like machine learning, self-healing, GenAI, and predictive analytics to leverage all the powers of AI. AI testing tools can learn about the product and generate the test cases on their own. The self-healing feature allows the test cases to re-adjust themselves according to the minor changes in the code without any manual intervention needed. Predictive analysis can foresee the potential defects that are most likely to occur based on a deeper analysis of the historical patterns. The AI-powered testing is not just about automation but about transforming the whole testing process into a smarter process.
One of the most important benefits of Artificial Intelligence in software testing is speed. When compared to creating test cases in an automation tool that requires heavy scripting, an AI tool can generate test cases instantly based on simple inputs. And it's not just the creation of test cases but also their execution that happens instantly. Additionally, many test cases don’t even require user input to generate, but the tool itself can do it based on its understanding of the product.
Another major advantage is scalability. Most of the modern software products often go through several upgrades. Whether that includes new API connections, micro-services, or new platform environments. An AI-powered testing tool adapts to these upgrades quite easily without any human intervention needed. Its self-healing capability allows it to keep updating its test cases according to the recent changes in the code that may otherwise cause the test script to break.
One of the biggest challenges with AI tools is training their AI model . Training requires extensive and reliable data that allows it to identify patterns and thereby provide solutions. If the data is inaccurate, it may cause skewed results, which can be detached from real-life scenarios.
AI testing also needs a highly skilled staff to be able to implement and maintain it. The solution may be expensive for the small-scale companies.
One primary benefit that human testing brings over AI is contextual understanding and empathy. A human tester considers all factors, like business goals, user needs, and cultural contexts that may go beyond the training data of an AI system. Manual testing can also be done, keeping user experience in mind, such as validating whether the application’s workflow makes sense or not. This is something an AI system cannot detect by itself.
When it comes to running repetitive test cases, human testing falls too short because of time consumption and human fatigue. Most of the test cases need to be executed several times when a product goes through multiple iterations. In that case, an automated testing framework fares much better than manual testing.
Scalability is another drawback of human testing. With every upgrade in the product, a dedicated time must be allotted to upgrade test cases. Also, humans can never compete with AI’s capability of running thousands of test cases in seconds.
Contrary to the popular notion of AI replacing human testers, the future of testing would be a hybrid model of AI collaborating with humans. AI can perform repetitive and scalable tasks, while humans, when freed from those, can focus more on creative and exploratory testing. Since the typical testing processes can now be easily handled by AI systems, new roles for human testers are evolving, that are testing strategists, quality coaches, and AI testing supervisors. They are becoming capable of leveraging AI’s strength towards higher-quality goals for building a more robust product. Testers need to continuously evolve themselves to keep up with the latest AI tools and data analytics to stay ahead of the competition.
Looking forward, AI can never replace the need for human hands in testing, but it can surely amplify the capabilities of human testers. The future belongs to those companies that can harness the best of both worlds.
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