Objentis home

Using AI in Software Testing Effectively

Artificial intelligence can speed up numerous tasks in software testing. It can analyze requirements, suggest test ideas, generate scripts, interpret screen content, and summarize test results.

However, using an AI tool is no substitute for a well-thought-out testing strategy. Only once the goals, risks, and quality requirements are clear can one assess which tasks can be effectively supported by AI.

Contact
Stylized blue AI chip labeled “AI,” surrounded by transparent layers.

Where AI Can Support Testing

AI can support the testing process in several areas:

Analyze Requirements

AI can structure requirements documents, identify ambiguities, and derive initial test conditions or test ideas.

Create Test Cases

Test case drafts can be generated from requirements, use cases, and existing documentation. These must then be reviewed for business relevance, completeness, and risks.

Support test automation

AI can generate scripts or visually recognize user interfaces. This makes it possible to create automation solutions that are less tightly coupled with the technical implementation.

Analyze test results

Large volumes of logs, screenshots, error messages, and test results can be summarized and analyzed for anomalies.

Create Reports

AI can organize test results and present them in a way that is tailored to the target audience. However, the expert evaluation and approval remain the responsibility of humans.

AI tools and testing expertise go hand in hand

AI can generate drafts and suggestions. Whether these are useful depends on the context. Therefore, we do not use AI simply because a task can be automated from a technical standpoint, but because its use makes a demonstrable contribution to quality, speed, or cost-effectiveness.

Our Principles for AI-Powered Testing

  • AI results are not accepted without verification.
  • Test cases are derived from a coordinated test strategy.
  • Critical risks take priority over maximizing the number of automated tests.
  • The specific benefits of an AI tool are reviewed on a regular basis.
  • Results must be understandable to the people in charge.
  • Responsibility lies with people, not with the tool.
Testing AI Systems

Greater Efficiency Without Compromising Test Quality

For more than 25 years, Objentis has been exploring how to achieve high-quality testing in a cost-effective manner. We apply this principle of Economic Testing to AI-supported testing methods.

The goal is not maximum automation at any cost, but rather a sensible division of labor:

🤖 AI is particularly well-suited for:

  • Organizing Large Amounts of Information
  • Generating Initial Test Ideas
  • Repeatable Test Activities
  • Pattern and Image Recognition
  • Preparation of Drafts

🧠 People remain crucial for

  • Business Risk Assessment
  • Selection of Relevant Tests
  • Exploratory and Creative Testing
  • Interpreting Ambiguous Results
  • Approval and Assumption of Responsibility

You might also be interested in:

Would you like to test AI-based software or integrate AI effectively into your quality assurance process?

Contact
AI Overview