Artificial intelligence (AI) has transformed many industries, and software testing is next. AI testing tools are automating time-consuming manual processes while also uncovering issues that humans cannot detect. As AI capabilities continue advancing, smart test automation will become indispensable for delivering flawless digital experiences. This article explores the current and future impact of AI on software testing.
Introduction: Overburdened by Manual Testing
As a seasoned QA professional, you likely grapple daily with the limitations of manual testing. Executing test plans across every desktop and mobile browser is exhausting work, and maintaining updated regression tests is never-ending. Despite your best efforts, some defects inevitably reach production. You likely often feel that testing remains an underappreciated bottleneck holding back releases.
The root issue is that applications have become complex beasts with countless permutations. Testing every possible input manually is just not scalable anymore. Hello AI tools that can replicate human judgment at machine scale! AI promises to eliminate the drudgery of repetitive testing work so you can focus on high-value test innovation.
The Promise and Current Use of AI in Testing
Gartner predicts over 50% of large enterprises will adopt AI for test automation by 2025. Multiple studies also confirm around 30% improvement in quality because of AI testing.
Many reputed brands have already witnessed stellar outcomes. For example, Microsoft cut testing time by 33%, UPS shortened test cycles by 80%, and Huawei achieved 30-50% test efficiency gains.
Specifically, AI augments testing in five key ways:
1. Faster Testing Cycles
AI algorithms can analyze code much quicker to determine optimal test priority based on risk – allowing 40-60% faster feedback. For example, Tricentis AI scans code history to identify high impact areas needing more testing due to frequent defects.
AI test generators also auto-author test cases 2-3x faster by leveraging machine learning. Testim recorded 60% faster test creation compared to manual planning.
2. Smart Test Automation
Executing test automation at scale remains challenging due to script maintenance overheads. AI self-healing frameworks auto-update suites, learning from code changes. For example, Autonomic from Tricentis auto-generates and heals scripts, enabling 80% test automation.
3. Improved Test Coverage
Humans cannot realistically test beyond a couple of parameters manually. AI combinatorial testing smartly combines variables identifying edge defects, improving coverage significantly. For example, Leapwork saw 31% more test coverage.
4. Earlier Defect Detection
Bugs remaining in production drastically increase rework costs. AI algorithms accurately detect over 50% defects pre-production across code, logs, UX and APIs. For example, Functionize uses computer vision to catch visual bugs early for 40% cost savings.
5. Analyzing Requirements
Interpreting needs into test cases is challenging. AI tools applying NLP techniques directly extract test cases from user stories with over 85% accuracy. For example, WalmartLabs saves QA teams hours of requirements planning effort using such algorithms.
The Future: Towards Fully Autonomous Testing
As AI for testing matures, the next frontier is achieving full autonomous testing without any manual effort. Self-driving test frameworks will become a reality with AI managing end-to-end test design, environment setup, test creation, execution, analysis and reporting automatically.
For example, Tricentis is developing the fully autonomous ‘Continuous Testing Cloud‘ by integrating their AI tools like qTest, Tosca and Flood into a smart engine. It will not only drive tests automatically but also self-learn and self-heal all aspects of testing.
Conclusion: AI is the Next Big Leap
There is little doubt AI will drive the next evolution in software testing, bringing scale, speed and quality improvements. Forrester notes modern AI systems are flexible enough to complement human testers rather than fully replace them. The future is about AI-assisted testing achieving beyond human capabilities. By embracing AI testing tools as a trusted partner, QA teams can find the best balance of automation and creativity that accelerates releases without compromising quality.
