Diagnosing App Crashes: A Mini-Course Using Process Roulette Examples

Stop guessing — learn to reproduce, diagnose, and fix crashes with controlled process-killing labs

If you struggle to turn flaky crash reports into fixable bugs, this compact, studio-style mini-course is designed for you. In 2026, hiring managers expect engineers and data scientists to demonstrate reproducible crash debugging, strong logging hygiene, and safe fault-injection skills. This course uses controlled process-killing tools (process roulette), chaos frameworks, and simple pkill scenarios as realistic failure cases so students learn to reproduce, trace, and resolve crashes — safely and ethically.

Who this mini-course is for

• Students and junior engineers wanting portfolio projects that prove debugging chops
• Teachers and bootcamps building hands-on modules on reliability and observability
• Lifelong learners transitioning to SRE, backend, or ML ops roles

Why process-killing labs matter in 2026

Modern systems are distributed, asynchronous, and increasingly AI-infused. Crash surface area has grown: GPU drivers, inference workers, model serving containers, background data pipelines. Employers now value candidates who can reliably reproduce intermittent crashes and provide a path to remediation: logs, crash dumps, telemetry, and a test demonstrating the fix.

Recent trends (late 2025 — early 2026) reinforce this course design:

• OpenTelemetry 1.x adoption standardizes telemetry and makes structured logs easy to integrate into labs.
• AI-assisted triage tools (LLM-based stack-trace summarizers and root-cause suggestions) accelerate diagnosis but require curated inputs — students must craft high-quality, structured traces to leverage them.
• Chaos engineering frameworks (Chaos Mesh, Gremlin, LitmusChaos) have matured; educational sandboxes are now common in cloud lab offerings.

Course overview — compact micro-credential (6 modules, ~24–30 hours)

This short course is a skills-first micro-credential aimed at portfolio-ready evidence. Each module includes a theory brief (15–30 minutes), a 60–120 minute hands-on lab, and a graded assignment.

Module 0 — Prerequisites & safe lab environment (2 hours)

• Goal: Set up isolated VMs/containers and get toolchain ready.
• Tools: Docker, Podman, VirtualBox/VMware, Git, VS Code, bash, basic Python/Node app templates.
• Safety checklist: never run fault injection on production, secure scopes and permissions, obtain written consent for multi-tenant environments.
• Assignment: Provision a VM or container and submit a screenshot of your isolated environment with OS, kernel, and user account details.

Module 1 — Fundamentals of crash reproduction (4 hours)

Core concepts: deterministic vs nondeterministic crashes, minimal repro, bisecting changes, test harness design.

• Lab: Take a small HTTP worker app (Node or Python) and create a minimal failing test that reproduces a crashed worker. Use deterministic inputs and seed values.
• Tools/Techniques: rr (record-and-replay), deterministic random seeds, dependency isolation.
• Assignment: Deliver a one-page reproduction report with exact steps and a minimal test. Rubric: clarity (40%), reproducibility (40%), brevity (20%).

Module 2 — Logging and observability for crash triage (4–5 hours)

Logging is the primary evidence you’ll use to train AI triage tools and to answer “what happened?”

• Best practices: structured JSON logs, correlation IDs, log levels, avoiding PII, graceful shutdown logs.
• Lab: Instrument the app with OpenTelemetry and a JSON logger, add tracing spans across request lifecycle, export to a local collector.
• Assignment: Submit a set of logs that show pre-crash and crash context. Bonus: provide an OpenTelemetry sample that links traces to logs.

Module 3 — Controlled process killing: process-roulette and safety (4 hours)

Use process-killing as a fault-injection primitive. Historically, hobby tools like “Process Roulette” randomly killed processes as a prank; in an educational context, we turn that concept into a repeatable lab.

• Tools: pkill, kill -9, a small process-roulette test harness (scripted to kill specific PIDs on timers), and chaos frameworks for containerized labs.
• Lab 1 (simple): Run the app under a supervisor (systemd or supervisord) and script periodic kills of the worker process. Observe restart behavior and logs.
• Lab 2 (advanced): Use a chaos framework (LitmusChaos or Gremlin in a sandbox) to terminate a pod in Kubernetes and trace sidecar logs and service-level metrics. For infrastructure-level resilience and orchestration tips, consider micro-DC power and orchestration notes like Micro‑DC PDU & UPS Orchestration.
• Commands (example):

  pkill -f worker.py  # kills by command name
  kill -9 <PID>       # immediate termination (use only in isolated lab)

• Assignment: Produce a reproducible experiment that demonstrates a crash triggered by a targeted kill and show how to reproduce it deterministically.

Module 4 — Crash dump analysis and symbolication (4–5 hours)

Crash dumps and stack traces are the raw evidence for deep bugs (use-after-free, null-deref, race conditions).

• Tools: core dumps, gdb/lldb, addr2line, ASan/UBSan, Valgrind, and rr for deterministic replay.
• Lab: Generate a core dump from a killed process, load into gdb, and map addresses to symbols. Then reproduce using AddressSanitizer to find memory errors.
• Assignment: Submit an annotated gdb session that identifies the faulty frame and a short patch suggestion.

Module 5 — Root cause analysis, fix verification, and reporting (4–5 hours)

Conclude with triage, fix, and verification strategies so students can show the full lifecycle of a reliable fix.

• Activities: write a regression test, add logs/metrics to prevent recurrence, perform canary deployment, and run chaos tests.
• Tools: CI pipelines, GitHub Actions or GitLab CI, Sentry/Bugsnag for crash alerting, Prometheus/Grafana for metrics.
• Final Project: A portfolio-ready case study: reproduce crash, analyze, patch, add tests and observability, then validate with a chaos run. Deliverables: git repo, reproduction script, annotated logs, and a 5–7 minute screencast showing the debugging process.

Assignments: realistic scenarios using process-killing tools

Assignments are short, repeatable, and graded on reproducibility, analysis depth, and remediation quality.

Assignment A — Supervisor failure

1. Run a supervised worker process that writes a heartbeat every second to a log file.
2. Script a scheduled kill (pkill -f worker) every 45 seconds and show how the supervisor restarts the worker.
3. Deliverable: proof of restart, plus an improvement plan (graceful shutdown hooks, atomic flush, state checkpoints).

Assignment B — Race condition surfaced by random kill

1. Create a producer-consumer pair where consumer crashes on unexpected shutdown of producer (simulate by killing producer at a specific step).
2. Use rr to record and replay the crash and demonstrate a deterministic repro.
3. Deliverable: minimal failing test, gdb backtrace, and patch to harden the consumer.

Assignment C — Distributed chaos canary

1. Deploy a two-service setup (API + worker) in a local k8s cluster.
2. Use LitmusChaos to kill the worker pod and validate API-level degradation metrics.
3. Deliverable: Prometheus dashboards screenshot, incident report, and rollback strategy.

Grading rubrics & micro-credential badge

Each assignment is graded on three axes: Reproducibility (40%), Analysis (40%), Remediation & Tests (20%). Students completing all modules and the final project receive a micro-credential badge and a portfolio-ready case study. Suggested time: 24–30 hours total.

Safety, ethics, and legal guidance

Fault injection can cause data loss and service outages. Emphasize these rules:

• Never run process-killing or chaos tests on production systems without explicit authorization and runbooks.
• Use isolated environments (VMs, disposable cloud projects, namespaces) and ensure backups where applicable. For guidance on migrating and isolating cloud workloads, see EU sovereign cloud migration playbooks.
• Mask or avoid logging PII and sensitive model weights; follow your institution’s data policy and legal counsel guidance. See ethical data pipelines for best practices.

Safety-first principle: the goal is reliable debugging skills, not breaking live systems.

Tooling cheat sheet (practical commands and integrations)

• Core process-killing and inspection:

  ps aux | grep worker
  pkill -f worker
  kill -9 <PID>

• Recording and replay:

  rr record ./worker && rr replay

• Crash dumps and symbolication:

  ulimit -c unlimited
  # After crash: gdb mybinary core
  bt full

• ASan/UBSan (compile-time):

  gcc -fsanitize=address,undefined -g -O1 -o myapp myapp.c

• Observability: Use OpenTelemetry SDKs and export to a local collector, then visualize with Grafana/Tempo. See dashboard design guidance at Designing Resilient Operational Dashboards.
• Chaos frameworks: LitmusChaos (K8s), Gremlin (commercial), Chaos Mesh (open-source).

Case study (instructor example from a 2025 lab)

In late 2025, one of our bootcamp cohorts hit an intermittent crash in a model-serving worker. Behavior: worker crashed every ~3 hours under load. Steps followed in lab:

1. Reproduced locally by increasing load and using a scripted process-kill to mimic the worker restart timing. This revealed a race where the worker assumed an initialization flag was set by a short-lived supervisor process.
2. Used rr to get a deterministic replay of the failing run, then ASan to find a use-after-free in the model loader.
3. Patch: added explicit reference counting and a graceful shutdown sequence; added structured logs and a trace that linked request IDs across retries.
4. Result: Canaries showed zero crash reoccurrences in 48-hour chaos runs; the cohort student used the project in interviews and got positive feedback for the end-to-end evidence.

Advanced strategies & future-proof skills (2026 and beyond)

As observability and AI tooling mature, the highest-value skills are:

• Crafting high-quality inputs for AI triage tools: structured logs, correlated traces, and minimal repros. Learn more about AI-assisted triage approaches.
• Automating repro pipelines: CI jobs that run rr-based replays and chaos acceptance tests on PRs.
• Cross-team communication: packaging your case study into an incident report with RCA, impact analysis, and a postmortem action plan.

Practical takeaways — what you can do this week

• Set up a disposable VM and run a supervised worker. Script a pkill and collect logs before and after the kill.
• Instrument that worker with structured JSON logs and a correlation ID to link requests to crashes.
• Use rr to record a crash run and practice loading a core dump in gdb. Write a one-paragraph remediation plan.

Resources & further reading

• OpenTelemetry docs (2026 updates)
• Chaos engineering: LitmusChaos, Gremlin tutorials
• rr (record & replay) project and best practices
• ASan/UBSan and Valgrind guides for memory debugging
• Sentry/Bugsnag crash-reporting integration examples

Final project rubric & portfolio guidance

For a resume-ready micro-credential, students should include:

• Git repo with reproducible test and a clearly documented reproduction script.
• Annotated logs and a screencast showing the debugging workflow.
• Patch and tests in a branch; CI that verifies the fix under chaos runs.
• One-page incident report summarizing impact, root cause, fix, and follow-ups.

Call to action

If you want a ready-to-run syllabus, graded assignment templates, and containerized lab images for this mini-course, download the free instructor kit or sign up for the next cohort-led run. Build the debugging case studies hiring managers ask for — safely, effectively, and with clear evidence. Ready to diagnose your first crash? Get the syllabus, start the labs, and add a real incident to your portfolio.

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