Quality assurance (QA) plays a critical role in software development, ensuring delivered products meet requirements. However, traditional manual testing has many downsides:
- Extremely time consuming, taking 50-70% of total dev time
- Repeating rote tests delays releases
- Humans make mistakes running tedious test cases
- Limited coverage across platforms and devices
- Hard to scale with faster delivery speeds
These inefficiencies result in slower feedback cycles, reduced team productivity, and prevent companies from releasing software as fast as they‘d like.
Artificial intelligence now enables automating these repetitive testing tasks to accelerate QA processes. This guide outlines how AI is transforming QA and key factors to consider when implementing AI for test automation.
The high costs of manual testing
Manual testing has been the primary method of QA for decades, but isn‘t sustainable with today‘s pace of development. Studies indicate:
- Engineers spend 50-70% of total dev time on manual testing activities.
- 69% of QA test cycles are composed of repetitive regression testing.
- Average time to design and document test cases is 5-7 days.
- Manual testing costs account for 30-50% of total QA budgets at many firms.
These statistics illustrate how manual testing significantly slows down development velocity, delays feedback, and diverts QA resources from more valuable test coverage.
ML automates repetitive aspects of testing
Recent advances in artificial intelligence and machine learning enable automating many rote QA processes to reduce these inefficiencies:
Test case prioritization
Machine learning algorithms analyze historical test data to determine which test cases have uncovered the most defects in previous builds. The system learns to predict which test scenarios have the highest likelihood of revealing issues earlier.
At Microsoft, this ML approach reduced test cycles by 25-50% across products like Windows, Office, and Visual Studio.
Automated test design
Natural language processing (NLP) techniques can digest requirements documents and user stories to auto-generate relevant test cases. This saves significant time spent manually designing tests.
Infosys Pandit leverages NLP to create test scenarios from specs in minutes rather than days. One telecom client achieved 40% faster test creation.
Computer vision for GUI testing
Computer vision has advanced tremendously thanks to deep learning. CV models can map components on GUIs, extract properties like position and contrast, and validate consistency across platforms.
Researchers at eBay built a deep learning system that reduced GUI test time from hours to minutes by autonomously detecting defects.
Analyzing test reports with NLP
Reviewing test results to identify key information is time-consuming. NLP techniques can rapidly parse test report data and surface relevant insights 10x faster than human analysis.
Testim‘s ML assistant for test report analysis improved efficiency for a major test automation team by 120+ hours per month.
Reduced test cycles with AI automation (source: Tricentis)
Benefits of AI-driven testing
Implementing AI for test automation provides several key benefits:
- Faster feedback loops – Prioritizing test cases surfaces defects sooner rather than blindly running all tests.
- Higher test coverage – AI can far exceed human tester bandwidth across platforms.
- Improved efficiency – Automating rote activities enables QA staff to focus on exploratory testing.
- Reduced costs – Less time spent executing repetitive scripts cuts overall QA costs.
- Enhanced velocity – Accelerated testing means faster delivery of high-quality software.
According to recent research, teams using AI for test automation achieved:
- 30-70% reduction in test cycles
- 50-60% improvement in test efficiency
- 20-30% faster time to market for releases
Challenges with implementing AI testing
While promising, AI also poses some challenges:
- Lack of in-house expertise – Data science skills needed to build custom ML tools are scarce among QA teams.
- Data readiness – Algorithms require sufficient volume of high-quality training data which many teams lack.
- Toolchain integration – Incorporating AI automation into current workflows can take effort.
- Auditability – Blackbox AI recommendations must be explainable for QA staff to trust them.
- Measuring ROI – Quantifying productivity gains and cost savings from AI automation is difficult.
Early AI testing tools also faced issues like brittleness in new environments and lower defect detection rates for edge cases. But capabilities continue to improve with more data.
Keys for successful AI automation
Here are best practices when implementing AI to advance test automation:
- Start with a pilot – Run a controlled pilot project first before expanding to all QA.
- Clean up data – Deduplicate, structure, and enrich historical test data to train ML models.
- Ensure transparency – Use interpretable models and explainable AI techniques so QA engineers understand and trust recommendations.
- Retrain frequently – Continuously feed new test data to improve algorithm accuracy over time.
- Keep humans in the loop – Leverage AI for rote repetitive testing but have QA staff handle complex scenarios.
The future of AI-powered QA
As artificial intelligence and machine learning continue rapid advancement, they will transform quality assurance. Tedious repetitive testing work will be taken over by AI, enabling QA professionals to focus on more complex validation requiring human intuition and domain expertise.
This new paradigm will empower faster delivery of higher quality software. Development teams will be unconstrained by the delays of manual testing, able to release features as soon as they‘re ready. And by leveraging AI to amplify their skills, forward-thinking QA teams will elevate their strategic importance.
