 |
During the early days of software development, testing was taken care of by the development team itself. There was no separate QA team for testing in those days. But a lot has changed since then. QA has grown exponentially, and currently it is one of the crucial teams in the software industry, and its importance is increasing day by day.
If we look into automation, AI is one of the leading technologies, and integrating AI with test automation tools opens up a world of advanced enhancements to software testing. Many features get unleashed through AI like auto-generating test scripts, self-healing of XPaths, etc. So when we say the future of testing, we may need to consider three different periods: In the near future, in the next 10 years, and the far future after 30 years.
In this discussion, we’ll explore what’s coming in these time frames.
In Near Future
Software testing has advanced significantly with the integration of artificial intelligence, and many test automation tools are currently developing or have started incorporating AI into their systems. The fusion of artificial intelligence improves both the performance and utility of these technologies. It also has a tonne of improved features.
In this sense, testRigor, an AI-powered test automation tool, is especially helpful because of its sophisticated integrations, which set it apart from similar solutions. testRigor can do the following tasks and has built-in artificial intelligence assistance.
- Generate test steps to an extent, based on the test case description using generative AI
- Execute plain English commands directly using NLP
- Figure out new test cases for your application
In the near future, we expect many advancements in the areas mentioned above. Let’s see what changes the near future will bring.
Automatic Generation of Test Cases
Test case auto-generation is one of the crucial features supported by AI. So, AI algorithms generate test cases without any human intervention. As input the testers must provide the test case title or description alone. The test case generation is mainly based on specific criteria like the structure of application behavior, and it aims to improve the test coverage, efficiency, and quality of the application. This is achieved by using the techniques mentioned below.
- Predictive Analysis: With the help of AI you can analyze the historical test data that will help to identify the vulnerable areas of the application that are prone to defects. By understanding these patterns, AI can suggest critical test cases that must be handled.
- User Behavior Modeling: Using AI you can understand how users interact and act in the application. AI can create new test cases based on those interactions, which will be more realistic than traditional scenarios created using the tester’s perception.
- Adaptability: When there is a change in any features of the application, AI can automatically update the test scripts based on the functionality change without any manual intervention.
Generating Tests Based on Test Case Descriptions
Generating test scripts based on test descriptions is done with the help of generative AI. This is most notably represented by models like GPT-4 and other advanced neural networks, which can be leveraged to enhance and automate the test generation process. testRigor uses generative AI to create test scripts just by providing the description alone. Here is an article about applications of generative AI in software testing.
But let’s see what new changes are expected in the near future.
Advanced Natural Language Processing (NLP)
- Enhanced Comprehension: Generative AI models, with their improved NLP capabilities, will better understand intricate test case descriptions, capturing nuances and complexities that earlier models might have missed.
- Ambiguity Resolution: AI models will proactively seek clarifications or provide suggestions when faced with ambiguous test descriptions, ensuring that generated tests are as accurate as possible.
Richer Test Case Generation
- Diverse Scenarios: AI can produce multiple valid test scenarios from a single description, allowing testers to choose the most suitable ones or to test various permutations.
- Data-Driven Testing: Generative AI will not just produce test scripts but also generate relevant test data or inputs based on the description, amplifying the depth and breadth of testing.
Integration with Development Environments
- In-IDE Suggestions: Developers might receive real-time test generation suggestions as they write or modify code thanks to integrated AI-driven plugins in their development environments.
- Continuous Testing: As code evolves generative AI will continually refine and regenerate tests, ensuring that testing is always aligned with the latest version of the software.
Learning from Historical Data
- Feedback Loop: AI models will learn from past test results, incorporating insights from previous test executions to improve the quality of future test case generations.
- Predictive Analysis: Based on historical defect patterns and test outcomes AI might predict areas of the application more prone to failures and generate more focused test cases for those areas.
Seamless Collaboration
- Collaborative Test Design: Multiple team members can collaboratively provide input or descriptions, with the AI consolidating these inputs to generate comprehensive test cases.
- Real-time Revisions: As requirements change, stakeholders can modify test descriptions, and the AI will adapt and regenerate tests in real-time.
Reduced Test Maintenance Overhead
- Self-healing Tests: Generative AI will identify when tests become obsolete due to application changes and regenerate or update them accordingly through self-healing, reducing the maintenance overhead.
- Optimization: Over time the AI will recognize and eliminate redundant or overlapping tests, ensuring that test suites are optimized for maximum coverage with minimum execution time.
Execution of Tests in Spoken Language
- Voice Recognition: Advanced voice recognition systems can take spoken test cases, convert them to text, and then process the information.
- Direct Execution: Once the spoken language is processed, the AI system can directly execute the test without needing it to be converted into a traditional scripting language. For instance, a tester might say, “Open the application and check if the login button is active.” The AI would understand this instruction and carry out the test accordingly.
Simplification of Moving Manual Tests to Execution
- Minimized Modification: With the advancements in AI, the bridge between manual testing and automated execution will be significantly reduced. AI can interpret human-designed test steps and translate them into executable actions without significant modifications.
- Seamless Integration: AI-driven testing tools will integrate seamlessly with development and deployment pipelines, ensuring that tests are always relevant and up-to-date with the application’s latest version.
- Reduction in Redundant Steps: AI can identify and eliminate redundant steps in manual test cases, optimizing the testing process and ensuring only meaningful tests are executed.
Next 10 years
The potential shift towards AI-dominant testing methodologies in the next decade reflects the broader move across various industries to harness AI’s capabilities. Let’s delve deeper into this futuristic perspective on testing in the next 10 years.
Evolution of AI-Driven Testing
- Adaptability: AI will swiftly adapt to software changes ensuring tests remain relevant without constant human intervention. This adaptability will reduce the maintenance overhead typically associated with changing test environments.
- Intelligent Test Generation: Generative AI, as previously discussed will craft test cases based on high-level requirements, user behaviors, and predictive analytics. This will enhance test coverage and efficiency.
- Continuous Learning: AI-driven testing tools will learn from past defects, test results, and evolving software behaviors, continuously refining and improving test scenarios.
Diminishing Reliance on Traditional Frameworks
- Redundancy of Non-AI Frameworks: Given AI’s capacity to adapt, learn, and predict, non-AI-based testing frameworks will be dead, such as cucumber. They may become redundant due to their static nature and inability to cope with rapid software evolution.
- Cost and Efficiency: Maintaining and updating traditional frameworks might become costlier and less efficient than adopting advanced AI-driven solutions, leading to their eventual phasing out. Here is an article on how to save budget on QA.
Role of Human Testers
- Supervision: Even though AI will handle most testing tasks, human expertise will be crucial to supervise, guide, and provide context to AI systems, ensuring they align with the business’s goals and values.
- Complex Scenarios: Certain intricate testing scenarios that require deep domain knowledge, intuition, or an understanding of user psychology may still necessitate human intervention.
- Quality Assurance Strategy: Humans will focus more on defining testing strategies, quality goals, risk management, and ensuring that AI tools meet organizational objectives.
- Ethical and Bias Checking: AI models can inherit biases in their training data. Human testers will be essential in checking and ensuring that AI-driven tests do not propagate or act upon such inclinations.
Here is an informative article on how to start as a QA tester.
Industry-Wide Implications
- Job Transformations: While specific manual testing roles may diminish there will be a rise in roles like AI Test Supervisors, AI Training Specialists, and Ethical Testing Analysts.
- Continuous Testing: With AI’s speed and efficiency, continuous testing will become the norm, integrated seamlessly into the DevOps lifecycle and CI/CD pipelines. Here are two good articles about DevTestOps and TestOps.
- Enhanced Quality: AI-driven tests, with their predictive capabilities can potentially result in software with fewer defects, improving user satisfaction and trust.
Far future after 30 years
Predicting the distant future, especially in the fast-evolving realm of technology can be challenging. Let’s delve deeper into the speculation that, in the distant future, both engineering and Quality Assurance (QA) tasks might be entirely handled by Artificial General Intelligence (AGI).
The Evolution of AI to AGI
- What is AGI? Artificial General Intelligence refers to machines that can understand, learn, and apply knowledge in various tasks, much like humans. This contrasts with the current state of AI, which is specialized and designed for specific tasks.
- Current Trajectory: With advances in deep learning, neural networks, and computational power, we’re witnessing rapid progress in AI capabilities. If this trajectory continues, the evolution from narrow AI to AGI seems like a plausible next step.
Engineering Functions and AGI
- Design and Problem Solving: Engineering often involves intricate problem-solving and design choices. With its vast computational capacity and ability to learn from many sources, an AGI could handle complex design problems efficiently.
- Code Generation: Instead of just aiding developers with suggestions, as current tools do, AGI might autonomously write, optimize, and refactor entire codebases, adapting them based on changing requirements.
- Continuous Improvement: An AGI could continually refine and optimize software or hardware designs based on real-world feedback without waiting for human intervention.
Quality Assurance (QA) and AGI
- Comprehensive Test Generation: AGI could generate exhaustive test cases considering all possible scenarios, ensuring thorough software testing. The current trend of automatic test generation could be a precursor to this.
- Real-time Quality Checks: With AGI’s potential omnipresence across the development lifecycle, real-time quality checks might become the norm, ensuring errors are caught and rectified instantaneously.
- Predictive Analysis: Harnessing vast amounts of data, AGI could predict potential failure points and address them proactively, further elevating software quality.
Human Role in an AGI-Dominant Landscape
- Oversight and Direction: While AGI might handle tasks autonomously, humans could still play a vital role in providing direction, ethical guidelines, and overarching objectives.
- Creativity and Intuition: While AGI would be exceptionally logical and data-driven humans might still be the primary source of creativity, intuition, and emotional intelligence.
Futuristic Testing with testRigor
testRigor consistently outperforms other automation tools by staying at the forefront of technological advancements. It already offers integrated AI capabilities, including various features we have discussed like generative AI and the ability to create test scripts in plain English. As a result, we can expect testRigor to keep advancing and introducing even more AI-powered features in the coming years.
