Loom Debugging

Debugging

Overview

Find the true cause of a defect and prevent its recurrence — across app code, tests, data pipelines, ML, and infra. The failure mode to avoid is symptom-patching: changing code until the symptom disappears without understanding why, which moves the bug rather than fixing it.

The Root-Cause Loop

Run this loop; don't skip steps. Most wasted time comes from hypothesizing before reproducing, or fixing before localizing.

1. Reproduce — deterministically. If you can't reproduce it, you can't verify a fix. Capture exact inputs, env, versions, and the full error/stack. For intermittent bugs, first make it more frequent (loop it, add load, shrink timeouts) before anything else.
2. Minimize — shrink to the smallest input/code that still fails. Delete half, re-run, repeat (delta-debugging). A 5-line repro localizes faster than a 5000-line one and often reveals the cause outright.
3. Localize — bound where it happens before asking why. Bisect in space (comment out / binary-search modules) and in time (git bisect). Read the stack trace top frame first, then the first frame in your code.
4. Hypothesize — state a specific, falsifiable cause ("X is null because Y returns None when Z"). Vague hypotheses ("something with async") aren't testable.
5. Test the hypothesis — one variable at a time; change something that should confirm/refute it. If the experiment can't distinguish two causes, design a better one.
6. Fix — the root cause, minimally. Verify the repro now passes AND that you understand why the fix works (else you may have masked it).
7. Prevent — add a regression test that fails without the fix. No regression test = the bug is not done. Then generalize: are there sibling instances of the same class elsewhere?

Core Discipline

• Reproduce before fixing. Never "fix" what you can't observe failing.
• Read the error fully — message, type, and every stack frame. The answer is often literally in it (wrong frame, unexpected value, swallowed cause).
• Check recent changes. Most new failures are recently introduced → git log, git bisect.
• Question your assumptions. The bug lives in what you're sure is correct. Verify it (print it, assert it) rather than believing it.
• Preserve evidence. Save the failing input, logs, core dump, and seed before you start mutating code.

Bisection Discipline

git bisect run automates finding the introducing commit — far faster than manual marking.

git bisect start
git bisect bad                 # current commit fails
git bisect good v1.2.0         # last known-good ref
git bisect run ./repro.sh      # script: exit 0 = good, 1..124 = bad, 125 = skip (can't test)
# ...git converges to "abc123 is the first bad commit"
git bisect reset

• The repro script must be reliable — a flaky script sends bisect down the wrong branch. Make the repro deterministic first (see flaky section), or bisect will lie.
• Exit codes: 0 good, 1–124 bad, 125 = untestable (skip — e.g. commit doesn't build), >127 aborts.
• Bisect the behavior, not the test file — if the test itself changed, git bisect run a script that applies today's test to the old code, or pin the test.
• Works on any monotonic property: performance regressions (exit 1 if bench > threshold), binary-size, output diffs — not just pass/fail.

Differential Debugging

When it works here but fails there (or worked yesterday), diff the two worlds instead of reading code blind. Enumerate and compare, one axis at a time:

• Code/version: git diff good..bad, dependency lockfile diff (transitive upgrades!), runtime/compiler version, OS/arch.
• Input/data: exact request, dataset, encoding, ordering, size, null/edge presence.
• Environment: env vars, config files, feature flags, secrets, locale/timezone, PATH, working dir.
• State: DB contents, cache, filesystem, clock, warm vs cold, concurrency level.

The first axis that, when copied from the working world to the broken one, flips the result is your root cause. diff <(working_env) <(broken_env) beats speculation.

"Passes locally, fails in CI" playbook

Almost always an environment or isolation difference, not logic. Check in order:

• Test ordering / parallelism: CI runs a different order or in parallel → shared-state leak. Repro locally: run the full suite (not just the one test), randomize order, and force the CI thread count (cargo test -- --test-threads=N, pytest -p xdist -n N, jest --runInBand).
• Ambient config leakage: an env var / config file / global you have set locally but CI doesn't (or vice versa). Diff env. Your local DB has seed data CI lacks.
• Wall-clock / timezone / locale: CI in UTC exposes tests that assume your local TZ; date formatting, LANG/LC_*, number/decimal separators.
• Filesystem: case-sensitivity (macOS/Windows case-insensitive → Linux CI case-sensitive: import Foo vs foo), path separators, missing fixture files not committed, permissions, /tmp cleanup.
• Resources/timing: CI is slower/loaded → tight timeouts and sleep-based waits fail; less memory → OOM.
• Hidden network: a test hits a real service that's reachable locally but blocked/rate-limited in CI.
• Uncommitted state: the fix works locally because of an uncommitted file or a dirty DB. Reproduce in a clean checkout / fresh container.

Flaky Tests — Root-Cause Classes

Flaky = non-deterministic pass/fail with no code change. Classify the cause, then fix the class (retrying is not a fix — it hides the defect, often a real production race):

# Reproduce flakiness: loop until it fails, capture the run
for i in $(seq 1 200); do cargo test flaky_name -- --nocapture 2>&1 | tee /tmp/run || break; done

cargo test -- --test-threads=1     # if this fixes it → shared-state/ordering isolation bug
RUST_LOG=debug cargo test flaky    # expose timing/ordering
pytest -p no:randomly / -p randomly --randomly-seed=N   # pin/vary order to bisect ordering deps

Heisenbugs — when observation changes the outcome

The bug vanishes under a debugger or with a print added. This is diagnostic, not magic: the observer changed timing or memory layout.

• Timing shift: a print/breakpoint adds latency that hides a race or reorders async events. → The real bug is a race; use logging that doesn't alter timing (ring buffer, perf/tracepoints, post-mortem logs), or ThreadSanitizer/--race.
• Optimization/UB: works in debug, breaks in release (or vice versa) → undefined behavior, uninitialized memory, aliasing, or a compiler optimization exposing a latent bug. Reach for ASan/UBSan/Valgrind, -Werror, MIRI (Rust).
• Memory/aliasing: printing a variable forces it to memory (not a register), masking a corruption. → memory tooling, not more prints.
• Rule: if adding observation fixes it, you have a concurrency/UB bug — attack the timing/memory cause, don't ship the print.

printf vs. debugger — pick deliberately

Structured logging beats scattered prints: include the value's identity (user_id=%s got=%r expected=%r), not just "here". Remove or gate debug logging before committing.

Domain Notes

Data pipelines

Bugs: schema drift between stages, null/missing handling, type coercion (precision loss/overflow), encoding (UTF-8), OOM on large data. Techniques:

sample = df.filter("problematic_condition").limit(1000); sample.write.parquet("dbg.parquet")  # local repro set
assert df.filter(col("user_id").isNull()).count() == 0, "null user_ids"   # assert assumptions at boundaries
df.explain(); df.printSchema()   # execution plan + schema drift

Validate at every stage boundary; log sample rows per transform; test edge data (nulls, empty strings, extremes). Check upstream source for schema changes first.

ML models

Distinguish code bugs from model-behavior issues. Training: loss not decreasing (LR/gradient flow), exploding loss (instability), over/underfit. Inference: distribution shift, train/serve skew.

for n, p in model.named_parameters():             # dead layers / vanishing grads
    print(n, None if p.grad is None else p.grad.norm().item())
torch.autograd.set_detect_anomaly(True)           # locate NaN/Inf source
# Train/serve skew is the #1 silent accuracy killer:
print(train.mean(), train.std(), prod.mean(), prod.std())  # differ ⇒ preprocessing mismatch

⚠ Train/serve skew: most "accuracy dropped in prod" bugs are a preprocessing/scaler mismatch — the scaler/encoder fit at train time wasn't persisted and reused at serving. Save and load transforms with the model. Also check: feature drift, label leakage, and that eval data matches training preprocessing exactly.

Worked Example — the full loop

# SYMPTOM: TypeError: 'NoneType' object has no attribute 'name' at process_user (users.py:45), intermittent.

# 1 Reproduce: which user_ids? Loop the failing endpoint; log inputs → fails only for deleted users.
# 3 Localize: stack points at users.py:45 → user is None.
def process_user(user_id: str) -> dict:
    user = get_user(user_id)          # 4 Hypothesis: get_user returns None for deleted users
    return {"name": user.name}        # crash

# 6 Fix root cause (explicit contract), not `user.name if user else ""` (which hides the deletion)
def process_user(user_id: str) -> dict:
    user = get_user(user_id)
    if user is None:
        raise UserNotFoundError(user_id)   # caller decides 404 vs skip
    return {"name": user.name}

# 7 Prevent: regression test that fails without the guard
def test_process_user_missing():
    with pytest.raises(UserNotFoundError):
        process_user("deleted-id")

Tooling quick reference

# Python
python -m pdb script.py                 # interactive; python -m cProfile -o out.prof for CPU
py-spy top --pid PID                     # sampling profiler, no code change, prod-safe

# Node
node --inspect-brk script.js            # break on first line → chrome://inspect
node --stack-trace-limit=100 ...        # deeper async traces

# Rust
RUST_BACKTRACE=full cargo run           # full trace; RUST_LOG=debug for logs
cargo test -- --test-threads=1          # serialize to expose shared-state flakes

# Systems
strace -f -e trace=file,network CMD     # syscalls (Linux) — find missing files, blocked I/O
gdb -p PID  / lldb -p PID               # attach to a live/hung process; `bt` all threads for deadlocks
tcpdump -i any port 8080                # wire-level; curl -v for HTTP

Verification checklist

• Reproduced the failure deterministically before changing code
• Localized (stack + bisect) — you know the exact line and why, not just where the symptom shows
• Root cause identified and stated; fix addresses it, not the symptom
• Regression test added that fails without the fix and passes with it
• Checked for sibling instances of the same bug class elsewhere
• For flaky/CI-only: class identified (isolation/time/order/async/resource) and fixed — not retried away
• No debug prints / temporary logging / set_detect_anomaly left in the committed code