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Risk

As explained in Software Testing with Generative AI

The possibility that an action, system, or technology will cause harm, loss, failure, or another undesirable outcome.

Risk is the possibility that an action, system, or technology will cause harm, loss, failure, or another undesirable outcome. In testing, it identifies what could undermine product quality and where investigation should focus. section 3.1.2 section 5.1.1

Risk-based testing starts by identifying concerns rather than asking for test cases immediately. This keeps testing directed and avoids forcing every problem into one technique. After risks are understood, the team can select suitable activities, including manual tests, automation, exploratory testing, or other forms of investigation. section 5.1.1 section 5.4

A practical risk-oriented workflow:

  • Identify risks through product context, user expectations, team discussion, testing experience, and critical or lateral thinking. section 3.1.2 section 8.1
  • Use an LLM to expand the team’s ideas and suggest overlooked risks, while retaining human control over testing direction. section 5.1.1 section 8.1.1
  • Match each important risk with an appropriate testing activity instead of accepting generic generated suggestions. section 5.1.1 section 5.2
  • Prioritize exploratory charters by risk, track which risks sessions explore, and judge the value each session provides. section 8.1

From a feature to focused risks

For a file-upload feature, a generic prompt may produce lists about malicious files, injection, denial of service, privacy, compliance, storage, and concurrency. Those suggestions still require contextual judgment. A stronger plan would identify which risks matter for the product, then choose focused tests or other activities to investigate them. section 5.2.1 section 5.1.1

Figure 8.1 A visual model showing the relationship between risks, charters, and exploratory testing

The model connects identified risks to structured exploratory charters and then to testing sessions, showing how concerns become actionable investigation. section 8.1

How Software Testing with Generative AI covers it

How deep it goes
Develops identification, prioritization, mitigation, and LLM augmentation, while stopping short of a fixed procedure for discovering risks.
Examples
Uses fast-food ordering, file upload, booking reports, generated test cases, and exploratory charters to make risk-focused testing concrete.
What it assumes
Assumes testing and product context, while emphasizing that risk discovery depends on human questioning, heuristics, and critical thinking.
What you can do after
Apply it to identify concerns, challenge LLM suggestions, select proportionate testing activities, and prioritize exploratory work.
How it connects
Connects risk with testing, test planning, exploratory testing, test charters, test cases, LLMs, and prompt engineering.

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This explainer was generated by Manning's knowledge graph from the text of Software Testing with Generative AI. The section links point to the parts of the book it draws on.