Software testing, a pivotal stage in software development, has rapidly evolved with the emergence of new technologies. The advent of AI has significantly expanded opportunities for testers. Previously considered a distant dream, the idea of an automated tool that determines test coverage has become a reality.
It’s crucial to note that, in this context, we will focus on AI beyond familiar linguistic models like ChatGPT and Bard AI. While these tools are undoubtedly valuable for testers, we will highlight new products and technologies that leverage machine training and neural networks to elevate testing efficiency to a new level.
The Role of AI in Software Testing
Let’s start by exploring the concept of a neural network: an artificial intelligence technique that teaches computers to mimic the human brain. This form of deep learning employs interconnected nodes or neurons in a layered structure that resembles the mind. Neural networks create an adaptive system where computers learn from mistakes, self-improve, and make data-driven decisions. They are a fundamental component of AI that enables the modeling of complex relationships and facilitates data-driven decision-making.
AI solutions built on artificial neural networks aim to address complex testing problems, including:
- Automatic determination of test coverage and testing of web applications with minimal tester involvement.
- Automatic creation of code structure and complete autotests from textual descriptions of steps and web applications.
- Automatic smoke/regression testing with auto-generation of test coverage based on system changes, as well as searching and detecting defects.
- Automatic analysis of found defects and testing results (root cause analysis) to identify the causes of defects and correct them automatically.
The term “automatic” is omnipresent in this description, underscoring the shift in the tester’s role to monitoring AI’s work and signifying a new stage in the evolution of software testing within IT. While many companies are still in the early stages of training their models, the tester’s work remains significant. However, over time, we anticipate a gradual reduction in the need for a large number of QA specialists. In the next 10-20 years, we may find ourselves overseeing AI solutions as they perform the majority of the work, driven by the industry’s pursuit of faster releases and resource efficiency.
Although the future promises AI’s capability to handle this task, current algorithms and learning models are not perfect. It will take time, along with successful test completions, for neural networks to learn to identify errors traditionally found by testers. The quality of AI testing will ultimately determine our confidence in the results obtained.
Exploring the Implementation of AI Solutions in Companies
Despite the prevalent discourse around AI, the integration of these technologies into company processes, aside from linguistic AI models, remains at a nascent stage. Firstly, there is a prevailing lack of trust in existing neural networks, both in terms of quality and security, particularly when employing the technologies and solutions available in the market. Secondly, the cost of creating and training proprietary neural networks currently outweighs the future advantages. However, we believe this is merely a matter of time.
Major corporations are recruiting machine learning specialists into their testing and development teams to craft bespoke neural networks. These networks hold the potential to adeptly and securely address diverse challenges throughout the software development and testing phases. Looking forward, we are confident that investing in proprietary neural networks, particularly for large enterprises, will lead to a substantial enhancement in the overall efficiency of the testing process.
Conversely, for entities unable to invest in developing their neural networks, various testing products that leverage AI technologies are emerging in the market, facilitating testing tasks for smaller companies. Examples include:
- Appvance: This system automates test generation and execution for web applications while creating comprehensive test coverage models.
- Eggplant AI: This solution enables test coverage generation using AI based on the constructed application model, employing a Model-Based testing approach.
- TestCraft: An AI solution that allows users to interact with a web form to determine necessary test checks, automatically generating JS scripts for Cypress or Playwright.
Furthermore, prominent test management systems like qase.io, testrail, and others are actively incorporating AI features into their frameworks. While these solutions currently rely heavily on ChatGPT rather than proprietary neural networks, they already enable the automatic creation of autotest structures.
Finally, AI has profoundly transformed our capacity to develop solutions that hold the potential to revolutionize the role of a conventional tester and the entire testing industry. Through the integration of neural networks and machine learning, organizations can expedite testing processes, enhance software quality, and accelerate product launches. Recognizing and harnessing this advantage now can confer a significant competitive edge in the future, both in terms of testing efficiency and the overall performance of the IT team. If you’re considering creating your own AI solutions for the testing process or integrating existing solutions, our team of QA experts is always ready to assist.
Delivery QA Director at First Line Software
Alexander Meshkov is QA Delivery Director at FLS. Alexander has over 10 years of experience in software testing, organization of the testing process, and test management. A frequent attendee and speaker of diverse testing conferences, actively engages in discussions and keeps up-to-date with the latest trends and advancements in the field.