Autonomous testing vs. Traditional testing: Which will win?

The future has never been so vague (there should be a drum roll going on here). But seriously, what to say about the future of software testing? Efficient and reliable testing methods have become essential to tackling modern cybercriminals and glitches.

MarketsandMarkets states that the global automation testing market is expected to grow from USD 12.6 billion in 2019 to USD 55.2 billion by 2028 — a remarkable reflection of the software industry's focus.

For instance, a fresh-backed startup, Distributional, recently raised USD 19 million to automate AI model and application testing. Indeed, autonomous software testing has emerged as a modern alternative to traditional testing methods.

Artificial intelligence and machine learning, beyond all the buzz, are the salt of autonomous testing tools aiming to reduce manual effort, increase test coverage, and adapt to changes in the application. And we are faced with a question no less important than the one Shakespeare once faced: Which approach will dominate the future of software testing — autonomous testing or traditional testing?

Further, you will find a software testing comparison between two main methods, their strengths and weaknesses. It explores their relevance in today's software development landscape and offers insights into the future of software testing.

Understanding traditional testing

Traditional software quality assurance primarily involves manual testing and early forms of test automation. Former means that human testers execute test cases without the assistance of automated tools, relying on their understanding (and sometimes intuition) to spot bugs.

Conventional test automation doesn’t rule out scripting and tools to automate repetitive test cases, but it still requires significant human intervention for script creation and maintenance.

Brief history background. Traditional testing sprang up in the headwaters of software development. Of course, those applications were much simpler and from a modern perspective even banal.

Yet, software began to evolve, and testing started gaining momentum. The 1960s and 1970s saw the introduction of structured programming methodologies, which necessitated systematic testing approaches.

The Waterfall model presented sequential phases, making thorough testing a cornerstone of the development process. Since that time, traditional testing has been a fundamental practice.

Several reasons for the rapid evolution of testing:

• Early software development practices: Applications were less complex, and release cycles were longer. Manual testing was sufficient to validate functionality.

• Lack of advanced tools: Simply: OpenAI, Microsoft developments, and other “frontline companies” in the AI world have not emerged yet in their modern appearance.

• Standards: Transition to the best practices (e.g., IEEE 829 — test documentation standard; ISTQB certification — structured approaches to testing).

• Human judgment: The reliance on human intuition allowed testers to understand user behaviors and identify issues beyond what automated scripts could capture.

Key features and advantages

Human expertise

This is a double-talk matter, because, on the one hand, human testers bring intuition, experience, and critical thinking to the process. On the other hand, their intuition often isn’t backed up by anything tangible. However, old-hand testers can:

> Identify complex issues including hidden relationships

> Understand various user perspectives from their own experience

> Adapt to changes

Flexibility

• Adaptive testing scenarios: Testers can create and modify test cases on the fly to explore new functionalities or edge cases.

• Exploratory testing: Allows testers to navigate the application freely without predefined scripts, uncovering unexpected issues.

• Immediate response: Testers can investigate anomalies in real time, providing immediate feedback to developers.

Ease access to best practices

• Proven methodologies: Effortless access to techniques like black-box testing, white-box testing, and regression testing.

• Reliable tools: Traditional testing has probably the widest net of mature tools and frameworks.

• Community support: Another advantage — the testing community has been there a long time before novel tech branches.

This is not to say about:

• Contextual understanding: Human testers can understand the broader context of the application, considering business logic and user expectations.

• Usability assessments: Ability to evaluate the look and feel of the application.

Limitations of traditional software testing paradigm

1. Time-consuming

• Manual execution: Advantage and burden at the same time. Anyway, this is time-consuming for large test suites.

• Script maintenance: Have a frequently changing app? The traditional testing approach may load you with additional troubles.

• Delayed feedback: Longer testing cycles can delay the identification of defects, which slows down the development process.

2. Scalability issues

• Resource limitations: Increasing the number of test cases requires more testers or more time, which may not be feasible.

• Complex applications: As applications grow in complexity, maintaining comprehensive test coverage becomes difficult.

• Rapid changes: In agile environments with frequent updates, keeping test cases up-to-date is a constant challenge.

3. Human error

Again, human effort may be an advantage (if you have atypical and highly skilled testers) and a serious roadblock that introduces the potential for mistakes:

• Oversights: Testers may miss defects due to fatigue, oversight, or cognitive biases.

• Inconsistent execution: Different testers may execute test cases differently. And different execution — unlike results.

• Documentation gaps: Do you have a dedicated tech writer who handles tech documentation? Or should your testers also record and process test results? There is a trick: sometimes being a penny pincher could do you a bear favor.

• High labor costs: Manual testing requires a significant human workforce.

• Training requirements: Ongoing training is necessary to keep testers up-to-date with new methodologies.

• Constraints on test cases: Time and resource limitations may prevent exhaustive testing.

ℹ️ Consequences of inadequate testing. 2018, an accident with the Uber self-driving car and Elaine Herzberg. Investigations revealed that the vehicle's software failed to identify the pedestrian correctly due to deficiencies in the testing process. The National Transportation Safety Board (NTSB) reported that the system did not adequately anticipate jaywalking pedestrians. The result — lethal outcome for a pedestrian.

Exploring autonomous testing

It is an advanced approach with artificial intelligence (AI) and machine learning (ML) technologies and the entire testing lifecycle automation. It differs from the traditional approach with its active human participation.

On the contrary, autonomous testing systems independently generate test cases, execute them, analyze results, and adapt to application changes without constant oversight.

Leveraged technologies

• Machine learning algorithms: Supervised learning, unsupervised learning, and reinforcement learning — this way, the system learns from data patterns and makes intelligent decisions about test creation and execution.

• Natural language processing (NLP): Allows the system to understand and interpret human language, convert requirements and user stories into executable test cases.

• Computer vision: This feature creates something like a real vision for the machine to interact with graphical user interfaces (GUIs). This means it can comprehend the app’s virtual elements as a real user.

What made autonomous testing possible

• AI and ML frameworks go beyond: The development of libraries (e.g., TensorFlow, PyTorch, etc.) has made it easier to implement complex neural networks and deep learning models.

• Increased computational power: USA and China breakthroughs in powerful GPUs and cloud computing resources can process large datasets required for training AI models.

• Big data comes closer: We (as a tech field) accumulated enough data for machine learning algorithms to learn effectively.

How autonomous testing tools like OwlityAI operate

1. Collect and analyze data: You have different sources of data (application code, user interaction logs, previous test results, etc.). The tool gathers it and sizes it up.

2. Test case generation: Your code structure and user behavior provide ML algorithms with additional information for generating test cases.

3. Test execution: The tool acts as a real user: creates additional load and challenges the application through computer vision and NLP.

4. Result analysis: AI algorithms analyze test outcomes to identify defects, performance issues, and areas requiring further testing.

5. Adaptive learning: The test outcomes are the perfect source for the model to learn from. Scanning the results, the system refines its models to improve future cycles.

6. Continuous integration: Integrates seamlessly with CI/CD pipelines, triggering tests automatically upon code changes and providing real-time feedback to developers.

Key features and advantages

AI-driven efficiency

• Automated test creation: As we described above, the tool analyzes the app's codebase and user behavior to generate end-to-end test suites without manual scripting.

• Intelligent test prioritization: Based on previous experience and learning data, ML capabilities identify high-risk areas in the code and prioritize important tests.

• Resource optimization: The tool efficiently allocates computational resources, running tests in parallel and reducing overall execution time.

Continuous learning

• Flexible algorithms: The system refines its testing strategies based on historical data, test results, and detected defects.

• More accuracy: The detection of anomalies becomes more precise with each cycle, and false positives decrease.

• Predictive analytics: The tool can predict potential problem areas in new code changes and act proactively.

Adaptability

• Self-healing tests: Automated adjustments after UI or underlying code changes allow you to shorten your staff because it’s simply unnecessary to keep a wide team as the tool tests and adapts to changes itself.

• Dynamic element recognition: Computer vision and advanced pattern recognition identify UI elements and the change in their properties.

• Seamless integration with development workflows: The tool remains aligned with the development pace. It all depends on your initial setup and further control.

Limitations of autonomous testing tools

1/ Initial setup and learning curve

• Complex implementation: To set up the system, you should know AI models at a particular level, have previous experience integrating with existing tools, and prepare data for training. Time-consuming, to say the least.

• Skill requirements: Teams may need training to understand and operate AI-driven tools effectively.

2/ Initial cost

• Higher upfront investment: Implementing autonomous software testing usually implies significant initial costs for licensing, infrastructure, and team training.

• Long-term ROI: Despite the initial expenses, companies often find that the long-term benefits – reduced manual effort, faster release cycles, and improved software quality – justify the investment.

Head-to-head comparison

The future of software testing

AI-driven methodologies are changing the status quo in software testing drastically. Of course, there is no fume without flame — the complexity of software applications and the demand for faster releases increased over the past few years.

Adoption of AI in testing: Leading tech companies are integrating AI into their testing workflows, particularly for coding. Deloitte states that 54% of US tech companies use AI coding agents or planning to implement them in 6 months.

Market growth projections: Market analysts second that. The mentioned at the beginning of this article MarketsandMarkets expects the AI market in tech to grow to USD 55+ billion by 2028.

Other opinions: Industry leaders advocate for AI-driven testing. Gartner predicts that by 2025, AI-powered testing will become a standard practice. Another reason is the demand for resilient delivery and flexibility.

Handling complexity: Microservices, cloud computing, and intricate integrations — traditional testing methods struggle to manage this increasing complexity. On the contrary, AI in testing analyzes vast amounts of data and complex interactions.

Improving quality and reliability: AI algorithms enhance and streamline defect detection by more thorough analysis of unwanted patterns.

Hybrid approach: is it a worthy idea?

Autonomous testing makes a difference. Yet, maybe exactly combining the approach is your choice. Here are possible whys.

Human expertise = good; humans+AI = excellent

> Exploratory testing: Successful exploratory testing implies using intuition and experience to discover unexpected issues. Could you complement this with AI power? Excellent.

> Contextual understanding: Testers are humans. They understand the context deeply, even the indirect one. This adds up to business logic and usability aspects that AI may not fully capture.

Automating the repetitive, focusing on the strategic

> Efficiency boosts: Autonomous testing can handle repetitive and time-consuming tasks (e.g., regression testing), meanwhile, testers can suss out strategic issues.

> Gradual implementation: There is no need to automate everything at once. Start by automating specific parts of the testing process while maintaining traditional methods. Smoother = further.

The winning edge

AI is gaining ground. However, traditional testing will continue to have its place, particularly in areas requiring human intuition and creativity. But this doesn’t mean we should give up searching for novelties. Rather keeping up with trends.

Alignment with modern development practices

• Continuous integration and delivery (CI/CD): Autonomous software testing exists in the same space that CI/CD pipelines do. Its strengths are immediate feedback and maintaining high quality.

• Agile methodologies: The adaptability and speed of autonomous testing support the iterative nature of Agile development.

Scalability and adaptability

• Handling growth: Earlier or later, you will grow (we wish so), and autonomous testing can help you — scale your testing efforts without double downing on resources.

• Real-time adaptation: AI-driven testing tools adjust to changes in the codebase automatically, reducing downtime.

Advancements in AI technology

• Continuous improvement: AI and machine learning technologies with us for the long term. We should accept it and adjust, especially because forecasts point out a rapid evolution of these technologies over the next 5 years.

• Competitive advantage: Companies leveraging cutting-edge tools will always do better than obsolete ones.

Bottom line

Autonomous testing leverages artificial intelligence and machine learning to make software development great again: faster release cycles, complex applications, and continuous delivery. Without manual intervention, it reduces the potential for human error and scales effortlessly when you grow.

And the shift toward AI-powered testing will continue. Recent accidents with MoneyGram (possible data breach) and Fidelity Investments (stolen data of over 77,000 users) are prime examples of the significance of proper and fast testing.

Traditional testing struggles with scalability, speed, and maintaining consistency. Autonomous testing tools like OwlityAI overcome these limitations and enhance the testing process.

Intelligent test generation, self-healing tests, and seamless integration with development workflows — your software testing won’t be the same. Change the way you test.