Manual testing is time-consuming and repetitive. Writing automated test scripts offers speed but requires specialized coding skills.
Natural language test scripting aims to deliver the best of both worlds – automation without code.
Let us explore how conversational AI is transforming test automation and making it accessible to non-programmers.
The Promise of Natural Language Testing
Natural language test scripting allows anyone to describe test scenarios and test cases in simple words.
AI assistants then automatically generate executable test scripts. Key benefits include:
- Faster test creation without coding. Tests can be written 3-4x faster than traditional scripting.
- Access for non-coders. Subject matter experts can create tests using their domain knowledge.
- Fewer script maintenance costs. Tests resist breaking when the application changes.
- Insight through conversation. Discussing tests drives clarification between team members.
- Platform flexibility. Tests integrate across different application technologies.
How AI Assistants Understand Test Instructions
Natural language test tools use a combination of techniques:
- Intent recognition: AI models classify test descriptions into intents like “login user”, “check balance”, and “reset password”.
- Entity extraction: Key entities like usernames, account numbers, and passwords are extracted from test steps.
- Dialogue managers: Conversations collect clarifying details like test data and expected outcomes.
- Code generators: Test scripts are auto-generated for target test frameworks based on the collected test details.
Together these AI capabilities allow testers to describe tests conversationally while assistants handle the scripting automatically.
However, scaling this technology required advances in machine learning.
Scaling AI Assistants with Machine Learning
Early natural language test tools had limited scope, unable to handle complex scenarios.
Machine learning changed the game by enabling test assistants to handle ambiguous instructions, understand context, and seek clarification. Key techniques include:
- Recurrent neural networks: RNNs develop an understanding of test step sequences and their logical relationships.
- Object recognition: Computer vision models identify on-screen components and actions to incorporate into tests.
- Active learning: Assistants learn to dynamically prompt for missing test details during script creation.
- Reinforcement learning: Test failures provide feedback to improve assistants’ test writing skills.
With ML, test assistants can have free-form, productive conversations about test scenarios while generating reliable scripts.
However, designing truly intelligent assistants requires rigorous training.
The Importance of Conversational Training Data
Like any machine learning application, natural language test assistants are only as smart as their training data.
Assistants need diverse datasets of real-world test conversations including:
Written dialogue transcripts spanning many test scenarios.
Recordings of verbal test instructions with multiple speakers.
Annotated intents, entities, and contextual details within exchanges.
Generated test scripts aligned to conversations.
With comprehensive training data, assistants learn to parse instructions, handle digressions, and request clarification when needed.
Ongoing training must cover new test cases and conversational patterns.
Optimizing the Human-AI Partnership
While AI assists with script creation, human insight is still essential for effective test design.
Some tips for productive human-AI collaboration:
Let testers focus on high-level validations while the assistant handles scripting details.
Have testers review auto-generated scripts to validate test correctness and provide feedback.
Use human domain expertise to identify edge cases overlooked by AI testers.
Provide assistants access to application requirements and design documents to build contextual understanding.
With transparent human oversight and guidance, AI test automation can exceed human-only efforts.
Democratizing Test Automation
A key promise of natural language testing is expanding automation beyond coding experts. Some tips:
Train non-coder subject matter experts to document test cases conversationally.
Have developers review SME tests to translate requirements into scripts.
Produce analytics for all testers on areas covered to focus on new scenarios.
Enable non-coders to refine auto-generated scripts with tools that abstract away code complexity.
With the barrier to entry lowered, more of your team can contribute to test automation at their level of comfort.
Maintaining Tests as Applications Evolve
Persistent pain with test automation scripts breaking when applications change.
Natural language tests offer resilience by focusing on intents over rigid procedures. Some maintenance tips:
Use descriptive element names like “login button” vs. brittle CSS selectors.
Have QA update named entities and flows when functionality moves or gets redesigned.
Continuously train assistants on new test cases to expand domain understanding.
Monitor test failures to identify where application deviations confused the assistant.
By keeping tests abstract and assistants updated, you limit brittleness as applications undergo inevitable changes.
Looking ahead, we can expect AI test automation to become:
- Accessible: Integrated into development workflows via code tooling and available in natural language.
- Comprehensive: Able to author a wide range of test types like security, performance, and accessibility.
- Self-healing: Monitors tests and auto-fixes scripts to match application changes.
- Proactive: Identifies untested areas and proposes new test cases for coverage.
- Collaborative: Seamlessly hands-off tests between humans and AI to combine strengths.
With AI delivering speed, scale, and flexibility, test automation will evolve from a technical bottleneck to an enabler of rapid releases.
Transforming Testing Culture
To fully realize the potential, the focus must extend beyond technology:
- Incentivize contributions to central test repositories from all team members.
- Structure test-a-thons to quickly build suite breadth.
- Celebrate bug reports that came from test failures.
- Continuously evaluate test strategy against business risks.
- Make test reviews and maintenance a shared team responsibility.
Natural language lowers the barriers to collaboration on quality.
A culture that prioritizes continuous testing will see compound gains.
Monitoring and Improving AI Test Assistants
Like any AI system, natural language test automation requires ongoing maintenance and tuning:
- Log transcripts of human-assistant test discussions to expand training data.
- Regularly test assistants with edge cases to expose weaknesses.
- Measure metrics like script creation time, tester satisfaction, and test coverage.
- Survey testers on pain points and desired capabilities to guide enhancement priorities.
- Budget time for retraining models on new test cases and application changes.
With the right Software Testing Services Provider, you can streamline your testing process by conversing with your tests through natural language test scripting.
The Risks of Over Automation
While powerful, relying too much on AI test automation carries risks that teams should be aware of:
Loss of Technical Knowledge
If testers come to depend heavily on AI assistants for script creation, they may lose an understanding of how the application technically functions under the hood.
When tests break, they will lack the skills to debug issues and edit scripts manually. Coding fluency enables deeper investigation into failures.
Narrowing of Test Thinking
AI assistants use algorithms to programmatically identify areas in need of test coverage. But algorithms have blind spots.
Without human judgment guiding test strategy, important edge cases can get overlooked.
Creative thinking about misuse cases is harder to automate.
Complacency around Manual Testing
The speed and simplicity of conversational test scripts can breed over-confidence in automation.
Teams may cut back on thorough manual testing and code reviews if they perceive the AI assistant has testing covered.
But when the unexpected occurs, manual skills are essential.
Limited Troubleshooting Ability
When reviewing auto-generated test scripts, humans tend to skim since they didn’t write them.
This limits insight into script logic and variables when tests eventually fail.
Lacking a full understanding of the code, testers will struggle to pinpoint and correct issues.
No training dataset fully prepares an AI assistant for every possible test scenario.
Conversational ambiguity and complex phrasing around new test cases increase the chance of scripts being misinterpreted or mishandled.
Preparing Teams for Natural Language Testing
Shifting to conversational test automation requires cultural readiness:
Phase in gradually while maintaining existing scripting methods.
Train testers to effectively instruct assistants and review auto-generated scripts.
Incentivize the contribution of real-world test conversations to improve assistants.
Communicate transparency around current assistant capabilities and limitations.
Encourage pairing between technical and non-technical testers.
With upskilling and open communication, teams can smoothly integrate AI collaboration.
What potential do you see for AI assistants to open test automation to non-coders?
What risks or challenges may arise from increased reliance on AI for scripting? Share your perspectives!
