Killing QA in the name of better software

Quality Assurance should not be a human endeavor.

For one, humans are bad at it. Human attention spans waver, so missed bugs and inconsistent results are endemic.

Two, it's bad for humans. It's tedious work. The repetitive nature of QA tasks leads to burnout and job dissatisfaction among talented developers. Moreover, it underutilizes human creativity and problem-solving skills, which could be better applied higher up the engineering value chain.

AI, on the other hand. Our pattern recognition skills pale compared to the relentless accuracy of machines, and so far, they seem OK with the tedious tasks we ask of them.

So, AI is ideal for QA. We want to turn QA entirely over to machines. No more QA engineers, no more QA teams, no more QA—just quality built into the development process.

Here's our plan to replace QA teams.

Cost reduction

Large QA teams represent a significant ongoing operational expense, often consuming a substantial portion of software development budgets. Maintaining and scaling test environments for growing projects incurs additional costs, further straining resources. This financial burden is where AI-driven QA can deliver significantly more than traditional methods.

Now: Reduced operational costs

AI-driven QA systems dramatically slash the need for large human QA teams. A QA team of ten can easily cost $1 million per year. Add to that overhead costs such as infrastructure, training, and management, and it's not surprising companies can spend over 35% of their budget on QA. These intelligent systems can handle the bulk of testing tasks, significantly allowing companies to streamline their QA departments. This reduction in headcount translates to substantial savings in salaries, benefits, and associated overhead costs.

With AI, engineering teams can dynamically scale testing resources based on project needs, eliminating waste and reducing the costs of maintaining idle test environments.

This approach not only cuts direct expenses but also improves overall efficiency. With faster testing cycles and reduced human intervention, projects move quicker from development to production, potentially increasing revenue by bringing products to market faster.

The Future: Zero-cost quality assurance

In the long term, quality assurance ceases to exist as a separate cost center. AI becomes so deeply integrated into the development process that ensuring quality is as natural and cost-free as writing code.

AI working alongside developers in real time eliminates the need for a distinct QA phase and, consequently, a separate QA budget. Quality is inherently built into every line of code, making dedicated QA resources obsolete.

These AI systems optimize the entire development process. They predict and prevent issues before they occur, drastically reducing the costs associated with bug fixes and maintenance. The concept of a QA budget becomes as antiquated as manual bookkeeping in the age of computerized accounting. The result is not just reduced costs, but a fundamental shift in how we perceive and account for quality in software development.

Test coverage and accuracy

Currently, QA relies heavily on manual testing and the creation and maintenance of test scripts. This process is time-consuming, error-prone, and often fails to keep pace with rapid development cycles. QA engineers spend countless hours writing test cases, updating scripts for each new feature or change, and manually executing tests. This approach is not only inefficient but also prone to human error and oversight.

This is the winning position for machines.

Now: Documentation-driven QA

LLMs specialize in language processing. They can parse project documentation, requirement specifications, and user stories to extract key functionalities, expected behaviors, and potential edge cases and automatically generate a comprehensive test suite.

This eliminates human bias in test case creation and ensures thorough coverage based on project requirements. This approach saves time and significantly reduces the risk of misinterpretation when human QA engineers translate documentation into test cases. The AI can adapt to changes in documentation, ensuring that the test suite always reflects the most current project state.

The Future: Human-out-the-loop test generation

Long term, AI evolves beyond merely interpreting documentation to become a proactive and predictive force in quality assurance. Bespoke models, trained on vast datasets of behavior, bug patterns, and user interactions, will generate test cases that explore scenarios far beyond human documentation.

These AI systems will analyze code structure, data flow, and potential execution paths to identify vulnerabilities and edge cases that even the most experienced developers might overlook. This AI-led test generation will not only cover known requirements but will also challenge assumptions and explore the 'unknown unknowns' of software behavior, pushing the boundaries of software reliability and robustness to unprecedented levels.

Speed and efficiency

Testing bottlenecks due to human work hours and fatigue-induced errors significantly hamper development speed. Lengthy feedback loops between QA and development teams further slow down iterations, causing delays in product delivery. This is where machines can dramatically outpace human capabilities.

Now: 24/7 testing cycles

AI can run tests around the clock without breaks or fatigue. AI test suites can be triggered on code commits, run complex integration tests across multiple environments, and provide immediate feedback to developers.

This approach drastically reduces the time between code changes and quality feedback. Developers receive test results and bug reports in near real-time, allowing quick iterations and rapid bug fixes. AI can prioritize tests based on recent code changes, ensuring that the most critical areas are tested first. This 24/7 testing cycle accelerates development speed, improves code quality, and significantly shortens the software development lifecycle.

The Future: Instantaneous quality assurance

Quality assurance becomes an intrinsic part of the development process itself. AI integrates directly into development environments, providing real-time feedback as code is written. These systems leverage predictive models and static code analysis to identify potential issues before the code is even executed.

AI analyzes and compares the code against known bugs, anti-patterns, and best practices. It suggests optimizations, identifies potential security vulnerabilities, and even proposes alternative implementations on the fly. This instantaneous feedback loop eliminates the traditional QA phase, as bugs are caught and fixed at the source.

AI can simulate code execution in real-time, predicting its behavior across various scenarios and environments. This allows for identifying edge cases and potential failures without requiring time-consuming manual testing or compilation.

User experience optimization

Traditional UX testing relies heavily on limited user samples and subjective feedback, often failing to capture the full spectrum of user interactions and preferences. Human testers struggle to simulate the vast array of user behaviors and scenarios real-world applications encounter. This is where AI-driven UX optimization can revolutionize how we design and test user experiences.

Now: Enhanced user simulation

AI systems can excel at creating sophisticated test scenarios and execution strategies, far surpassing the capabilities of human testers. These AI-powered tests interact with the software in ways that mimic real-world usage, uncovering edge cases and potential issues that human testers might overlook.

This approach allows for comprehensive testing across countless scenarios, identifying potential friction points and optimization opportunities. The AI can intelligently locate elements without relying on brittle selectors, craft powerful assertions based on complex logical conditions, and manage test timing.

These AI systems can also adapt their testing strategies based on application changes and new user interactions, refining their approach to reflect evolving software behaviors and user patterns.

The Future: Predictive user experience design

In the long-term vision, AI transforms from a testing tool into a proactive design partner, fundamentally reshaping how we approach user experience. Armed with deep learning capabilities and vast knowledge of human psychology and design principles, AI anticipates user needs and preferences before development even begins.

As development progresses, the AI continuously refines the user experience, making real-time adjustments based on simulated user interactions and predictive models. This eliminates the traditional post-development UX testing phase, as user-centric design is intrinsically woven into every development process step.

In this scenario, UX testing as a separate phase becomes obsolete. Instead, user experience optimization becomes a continuous, AI-driven process that spans the entire product lifecycle, from initial concept to ongoing evolution. The result is not just better user experiences but a shift in how we conceive, design, and deliver digital products that truly resonate with users.

QA is dead! Love live QA!

OK, QA still exists- and will continue to exist for many more years. But we should see QA for what it is-a means to an end. QA aims to build better software, fix bugs, enhance performance, and improve experiences. It is that final experience that matters, not how we get there.

With AI, we can get there faster. QA teams have performed their roles admirably, but if AI can improve user experiences better, faster, and cheaper, AI will win.

We're just in the Now phase now, but the future is coming. If you are interested in seeing how the future of AI in QA could help your team improve your product experience better, faster, and cheaper, then chat with us. If you're interested in building this with us, join our team.