> ## Documentation Index
> Fetch the complete documentation index at: https://lab.pollack.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Code Coverage v1 — Knowledge Injection Baseline

> 9 variants testing progressive knowledge injection on Spring Boot test generation

<div style={{display: 'flex', gap: '0.5rem', marginBottom: '1rem'}}>
  <span style={{background: '#22c55e', color: 'white', padding: '0.15rem 0.5rem', borderRadius: '4px', fontSize: '0.75rem', fontWeight: 'bold'}}>COMPLETE</span>
  <span style={{background: '#334155', color: '#94a3b8', padding: '0.15rem 0.5rem', borderRadius: '4px', fontSize: '0.75rem'}}>Feb 2026</span>
</div>

## Hypothesis

Progressive knowledge injection — giving agents increasingly structured domain knowledge — improves JUnit test generation quality on Spring Boot projects more than model upgrades.

## Setup

| Parameter        | Value                                                    |
| ---------------- | -------------------------------------------------------- |
| **Target**       | Spring Boot projects (gs-rest-service, spring-petclinic) |
| **Variants**     | 9 (baseline → full forge with SAE)                       |
| **Evaluation**   | Four-tier jury (T0-T3)                                   |
| **Build tool**   | Maven                                                    |
| **Agent engine** | [Agent Workflow](/projects/agent-workflow)               |

## The 9 Variants

| # | Variant               | Knowledge Level                        |
| - | --------------------- | -------------------------------------- |
| 1 | Simple prompt         | None                                   |
| 2 | + System prompt       | Minimal guidance                       |
| 3 | + Flat knowledge base | File-based domain knowledge            |
| 4 | + Skills (SkillsJars) | Structured, agent-accessible knowledge |
| 5 | + Skills + SAE        | Skills + Structured Agent Execution    |
| 6 | + Hardened prompt     | Defensive instructions                 |
| 7 | + Hardened + KB       | Hardened + flat knowledge              |
| 8 | + Hardened + Skills   | Hardened + structured knowledge        |
| 9 | + Forge (full stack)  | Complete knowledge-directed execution  |

## Key Findings

1. **Two independent axes discovered** — Knowledge injection and prompt hardening improve quality independently
2. **Model floor exists** — PetClinic achieves 92-94% coverage across all variants (the model already knows PetClinic)
3. **SAE is most efficient** — 70 expected steps, \$2.84 per run
4. **Partial knowledge paradox** — Some knowledge without structure can *decrease* performance
5. **First Markov fingerprints** — Tool-call traces reveal distinct behavioral signatures per variant

## Markov Analysis

Agent behavior varies dramatically across variants even when final outcomes are similar. The [Markov fingerprint](/methodology/markov-fingerprinting) analysis revealed:

* **JAR cluster patterns** — How much time agents spend in dependency inspection
* **Thrashing loops** — BUILD→TEST→EDIT cycles that indicate the agent is stuck
* **Loop amplification** — Quantified via transition probability engineering (TPE)

## Resources

<CardGroup cols={2}>
  <Card title="Experiment Repo" icon="github" href="https://github.com/markpollack/experiment-code-coverage-v1">
    Full traces, Markov analysis scripts, raw data
  </Card>

  <Card title="Blog: Agent Fingerprint" icon="newspaper" href="https://blog.pollack.ai/i-read-my-agents-diary/">
    Narrative walkthrough of the Markov findings
  </Card>
</CardGroup>