Test Log Analyzer

Finds similar past test failures and anomalous logs.

Industry	Level	Difficulty	Recommended mode
Electrical & electronics	3 of 5	Advanced	flat

Coming soon

A complete, runnable Test Log Analyzer reference implementation is in progress. The architecture and core code below already work with SrvDB 0.3.

The problem

• Test logs pile up unsearchable.
• Failures recur across boards.
• Root causes are re-investigated.

How SrvDB powers Test Log Analyzer

1. Log excerpts are embedded.
2. Failures retrieve similar past failures and fixes.
3. Logs far from normal are flagged.
4. Batch processing for CI.

What changes

Today	With Test Log Analyzer on SrvDB
Unsearchable logs	Searchable history
Repeated debugging	Past fixes found
Silent anomalies	Flagged

How it works

1. Collect logs.
2. Embed.
3. Match failures.
4. Flag anomalies.
5. Record fixes.

SrvDB features it uses

• search_batch: runs many queries at once, in parallel inside Rust.
• True cosine scores: scores in [-1, 1] mean the same in every mode, so thresholds are stable.
• upsert: inserts new items and replaces changed ones, so the knowledge base stays current.
• Crash-safe persist(): after it returns, data survives a crash or power loss and reopens exactly as committed.

Use cases

• Board bring-up
• EMC test failures
• Production test
• Burn-in
• Firmware regressions
• Field returns

Core code

embed() is your embedding model (for example sentence-transformers, or a function that turns your records into feature vectors).

import json
import srvdb

db = srvdb.Database("./test-log-analyzer-db", dimension=384)

def ingest(items):
    """items: list of (id, text, metadata dict)"""
    db.upsert(
        ids=[i for i, _, _ in items],
        embeddings=[embed(t) for _, t, _ in items],
        metadatas=[json.dumps(m) for _, _, m in items],
    )
    db.persist()                      # crash-safe checkpoint

def similar_failures(log_excerpt, k=5):
    hits = db.search(embed(log_excerpt), k)
    return [(json.loads(db.get(i)), round(score, 3)) for i, score in hits]

Limits and honest notes

• Log format consistency helps.

Build roadmap

1. Prototype: load a sample of real data with flat mode and check that searches return what an expert expects.
2. Measure: if you move to an approximate mode, compare its results with flat on the same queries (recall).
3. Pipeline: add continuous upsert and decide when to call persist().
4. Harden: test restarts and crashes, add logging, review every output path with domain experts.
5. Deploy: ship the database directory with the application or service.

All Electrical & electronics projects

Level	Project	What it does
1 · Beginner	Datasheet Q&A	Ask questions of your component datasheets and get page citations.
2 · Intermediate	Second-source Finder	Suggests substitute components by specification similarity.
3 · Advanced	Test Log Analyzer (this page)	Finds similar past test failures and anomalous logs.
4 · Expert	Hardware Lab Agent	An agent that remembers bench sessions, scope captures and decisions.
5 · Frontier	RF Fingerprint Lab	Similarity search over RF spectrum fingerprints (research).

See also: Use cases · Live examples (coming soon)