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testland/ai-test-generator

Use when tests should be generated from acceptance criteria, user stories or requirements with an LLM. Scores confidence per test case (self-assessment plus assertion, naming and completeness checks), batches uncertain cases for human review and emits tests in the team's existing framework.

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name:: ai-test-generator
description:: Use when tests should be generated from acceptance criteria, user stories or requirements with an LLM. Scores confidence per test case (self-assessment plus assertion, naming and completeness checks), batches uncertain cases for human review and emits tests in the team's existing framework.

ai-test-generator

Overview

This skill provides an augmentation framework for converting acceptance criteria (ACs) into test code: AI generates, the team curates. Generated tests are scored for confidence and reviewed adversarially before merge.

Step 1 - Define the input

# input/cart-promo.yaml
spec_source: "stories/LIN-1234.md"
acceptance_criteria:
  - id: AC-1.1
    description: "Valid promo 'WELCOME10' reduces subtotal by 10%"
    inputs:
      cart_total: 24.99
      promo_code: "WELCOME10"
    expected:
      subtotal_after: 22.49
      message: "Code applied"

  - id: AC-1.2
    description: "Expired promo shows error 'This code has expired'"
    inputs:
      cart_total: 24.99
      promo_code: "EXPIRED50"
    expected:
      subtotal_after: 24.99
      error: "This code has expired"

Step 2 - Run the generator

Generate one test per AC. Core loop below; full script with helpers and the project-conventions injection is in references/generation-and-scoring.md.

# scripts/ai-gen.py
import openai

system = ("Generate one test per AC in {framework} using the project's test "
          "conventions (inject docs/test-code-conventions.md). Specific "
          "assertions only - no .toBeTruthy() / .toBeDefined(). If an AC can't "
          "be satisfied with the given inputs, mark CONFIDENCE: low and say why.")

for ac in input_yaml['acceptance_criteria']:
    response = openai.chat.completions.create(
        model='gpt-4',
        messages=[
            {'role': 'system', 'content': system.format(framework='jest')},
            {'role': 'user', 'content': f"{ac['id']}: {ac['description']} | "
                                        f"inputs {ac['inputs']} -> {ac['expected']}"},
        ],
    )
    open(f"tests/generated/{ac['id']}.test.js", 'w').write(
        response.choices[0].message.content)

Step 3 - Validate before scoring

Before scoring, verify that generated test files parse and compile. Failing tests caught here should be flagged as CONFIDENCE: low automatically.

# For TypeScript projects
npx tsc --noEmit

# For Python projects
python -m py_compile path/to/generated_test.py

# For JavaScript projects (syntax check)
node --check path/to/generated_test.js

Any file that fails compilation is immediately downgraded: subtract 50 from its score before applying the heuristics in Step 4.

Step 4 - Confidence scoring

Per generated test, compute a confidence score. The rubric is the core of the skill; its parsing helpers (extract_imports, module_exists, extract_test_name) are in references/generation-and-scoring.md.

def score(test_code, ac):
    score = 100
    if 'CONFIDENCE: low' in test_code:                 # LLM's own confidence
        score -= 40
    weak = ['.toBeTruthy()', '.toBeDefined()', '.toBeFalsy()', '.toContain(']
    score -= sum(20 for m in weak if m in test_code)   # vague matchers
    for imp in extract_imports(test_code):             # hallucinated imports
        if not module_exists(imp):
            score -= 30
    name = extract_test_name(test_code).lower()        # generic naming
    if any(g in name for g in ['works', 'should', 'test 1', 'placeholder']):
        score -= 15
    return max(0, score)

Score	Action
80-100	High-confidence - review can be quick.
50-79	Medium - careful review required.
<50	Low - likely needs rewrite or rejection.

Step 5 - Output structure

## AI-generated tests - `<spec>`

**Generated:** N tests
**High-confidence:** M (review: spot-check 2-3)
**Medium-confidence:** K (review each)
**Low-confidence:** L (likely rewrite)

### High-confidence (4)

(test code blocks with confidence scores)

### Medium-confidence (3)

(blocks with confidence scores + flagged issues)

### Low-confidence (2)

(blocks with confidence scores + recommend manual rewrite)

### Hand-off

Review each generated test for:
- Hallucinated APIs / functions / constants
- Weak assertions (assert on specific expected values, not just truthiness)
- Missing setup / teardown
- Redundancy with existing tests

After curation: merge.

Step 6 - Iteration loop

Spec → Generate → Validate → Score → Review → (rewrite | merge | reject)
                                                    ↓
                                        Lessons fed back into prompt

The team's prompt evolves: when the LLM keeps producing .toBeTruthy(), add an explicit prohibition. When it hallucinates an API, add an example of the real API.

Step 7 - Cost + rate management

LLM calls have cost and rate limits. Pattern:

Generate per-PR: only new ACs.
Cache prior generations: if AC unchanged, reuse the previous test.
Batch: generate 5-10 ACs per LLM call to amortize overhead.
Budget: cap monthly generation cost; track per-team.

Anti-patterns

Treating AI output as production-ready -> always curate (Step 5 hand-off).
Vague spec input ("Apply a promo") -> structured inputs/expected (Step 1).
Skipping compilation validation -> run the compile check (Step 3) before scoring.
Skipping confidence scoring -> tier by confidence (Step 4).
Generic LLM without project context -> inject conventions into the prompt (Step 2).
One-shot generation -> keep the lessons-feedback loop (Step 6).

Limitations

Hallucinated APIs are a constant risk. Even with examples, the LLM may invent cart.applyDiscount() when the real method is cart.applyPromo(). Review catches this.
Cost. Per-AC generation costs add up at scale.
Confidence scoring is heuristic. A high-confidence test can still be wrong; never skip review.

References

ai-spec-coverage-mapper - sister skill: maps existing tests to spec sections.
gherkin-from-stories (in the qa-bdd plugin, ATDD mode) - non-AI alternative for AC-to-test conversion.

.tessl-plugin

references

SKILL.md

tile.json