My answers to pre-interview

These were interesting questions to answer. I have limited knowledge in this field, and I tried my best.

Hello xxxxx,

Thank you for the opportunity. Below are my answers.

Screening and Evaluation Questions

How do you differentiate unit testing from integration testing? Give examples of when each is most appropriate in a micro-services or modular codebase.

• Unit testing is precise and focuses on individual components in complete isolation. Integration testing. Integration testing tests multiple components together under real conditions.
• Example: At Globalnet, I built scalable automation tools using Python, Flask, and REST APIs.
  • Unit tests verified input/ output for isolated functions like device configuration parsing, customer data retrieval logic from upstream systems, and API request payload validation.
  • Integration tests used to verify the main API endpoint flow. Ensuring the Flask API route correctly handled an HTTP requests, persisted the new customer record from/ to the database, and successfully called Powercode APIs to create customer account, ticket, equipment, etc.

When setting a target test-coverage percentage, do you ever aim for 100%? Why or why not? What trade-offs do you consider?

• I don’t. It will require way more time to cover 100% and it’s not worth effort to write complex tests for simple code. Then main trade-offs I consider: Time vs value; maintainability

Describe your experience with tools like semantic-release or Google’s release-please. How would you extend or replace their heuristics with ML to highlight changelog entries based on actual code diffs?

• I have experience with CI/CD tooling like Github Actions and Jenkins for configuration management. I haven’t used semantic-release directly, but my experience with automation pipelines provides the basis for understanding automated versioning and release logic.
• Instead of just using commits messages, I would include the actual code change with git diff, and what files were changed.

How would you design a release pipeline that enforces minimum coverage thresholds (pulled from services like Codecov or Coveralls) before publishing a new version?

• I would use Github Actions as required step before the deployment:
  • Run tests and get coverage report, upload report and calculate the coverage delta for the current branch
  • Then pipeline would use service integration to fetch the current coverage percentage
  • Add conditional step
  • If the condition fails, the job fails, and pipeline is blocked from proceeding to the publish stage.

Suppose you need to map a git diff origin/main…origin/develop patch to affected API docs. What techniques or ML models would you use to identify which sections of the docs must be updated?

• I would try to use hybrid approach, static analysis and text embedding models.
• I’d extract the code symbols from the diff using an AST parser. Then, I’d embed both code snippets and doc sections using a some model and compute cosine similarity

How would you integrate auto-generated doc changes into a Git-based review process (e.g. opening PRs/MRs) to ensure human oversight?

• When dev creates pull request with code changes, Github Action runs the documentation generation tool like Sphinx for Python
• The tool compares the new generated documentation against the documentation in target branch, and if a diff is found, the job fails with a warning about the change
• In mature setup, the CI job would automatically commit generated doc to developer branch and push the commit back to pull request
• Then the manual review.

Have you used AI or property-based testing to auto-generate unit or integration tests? Describe the setup, how you prioritized test cases, and what success metrics you tracked.

• I’ve used AI-assisted code generation to bootstrap initial unit tests — primarily with Python-based APIs. Using pytest and hypothesis.

How would you train a model (e.g., using historical CI logs) to flag tests that are likely to be flaky? What data would you collect, and how would you act on the predictions?

• That’s tougher I haven’t done this yet. But I would collect test names/ module, run timestamp, duration of test, pass/ fail result, environment metadata.
• I would use other model with all the collected info to train current model
• And then implement logic to tag if test stable or flaky.

In a large monorepo, how would you configure CI (e.g. GitLab CI, GitHub Actions) so that only tests impacting changed files are run, without sacrificing overall confidence?

• I’d use a dependency graph that maps source files to test suites.
• When a commit lands, a script inspects the git diff and looks up affected test groups.
• Only those run in CI.

You’re building a Dart-based CLI assistant that suggests code fixes in the terminal. Which Dart or ANSI-terminal packages would you choose for syntax highlighting, interactive menus, and progress bars?

• My dart/ flutter experience is only based on my Expenses tracker app that I recently started developing. From quick searching, I would use ansicolor, dart-cli and cli_progress_bar.

How would you detect the user’s current file or project context in a CLI and surface targeted refactoring recommendations?

• Check working directory with pwd to infer the root project directory by searching for configuration file.
• Once CLI identifies project scope, it could extract metadata, use embeddings, parse active files via LSP.

For a Dart/Flutter monorepo, how would you ensure coordinated version bumps and dependency updates across multiple packages—similar to Melos?

• I’d do something like this:
  • Parse all pubspec.yaml files in the workspace.
  • Build a dependency graph
  • When one package updates, propagate compatible version updates to dependent packages automatically.
  • Enforce version consistency checks during
  • Integrate a changelog generator tied to Git commit messages

Describe how you’d let users define pre-release hooks or bespoke versioning strategies in a hosted package-management service.

• I’d allow users to define pre-release hooks in a config file

Beyond dart test and Mockito, what Dart testing or mocking libraries have you used (e.g. test_cov, mocktail, dart_fzzt)? How do they compare in ergonomics and feature set?

• I have not used Dart-specific testing libraries, as my experience is primarily in Python, Go, and network engineering. In Python, I rely on tools like pytest for unit testing and requests-mock or unittest.mock for mocking services.

Show a snippet or outline of how you’d test a Stream-emitting service in Dart, including error and completion cases.

• Since I do not have specific Dart experience, this is an outline based on general reactive programming principles and the Dart Stream API:

`// Assume a service that returns a Stream of data abstract class DataService { Stream getDataStream(); }

void main() { test(‘emits data, completes, and handles errors’, () { final mockService = MockDataService();

// 1. Define the Stream behavior for the mock
when(() => mockService.getDataStream())
    .thenAnswer((_) => Stream.fromIterable(['data1', 'data2']));

// 2. Use the stream matchers (provided by a library like 'test_api')
expect(
  mockService.getDataStream(),
  // 3. Define the expected sequence of events
  emitsInOrder([
    'data1',
    'data2',
    emitsDone, // Ensures the stream closes cleanly
  ]),
);

// --- Error Case Test ---
test('emits error and stops', () {
  when(() => mockService.getDataStream())
      .thenAnswer((_) => Stream.error(Exception('Network Error')));

  expect(
    mockService.getDataStream(),
    // 4. Expect a specific error to be emitted
    emitsError(isA<Exception>()),
  );
});   }); }`

As a codebase grows, how do you structure a test harness so new modules can plug in without duplicating setup or tear-down logic?

• *I would adopt the Pytest fixture pattern: *
  • Centraziled base test class
  • Module-specific setup
  • Parameterized test data

Have you built end-to-end pipelines (GitHub Actions, GitLab CI, Jenkins) that run tests, collect coverage, and trigger releases? Walk us through a specific example and the biggest challenge you encountered.

• Yes. I built a GitHub Actions pipeline for an internal automation system, but it does not collect coverage and trigger releases.

How would you feed AI-driven test-failure predictions or code-quality scores back into a CI pipeline to block or warn on risky merges?

• I would integrate the AI prediction into the pull request status check flow in GitHub.

Describe how you’ve integrated an LCOV-based coverage tool into CI, and how you made the resulting reports actionable for developers.

• I haven’t directly integrated LCOV yet, but I’ve built CI pipelines that run automated tests, collect results, and gate releases based on pass/fail logic. I understand how LCOV would fit into that flow — generating coverage data, uploading reports in CI, and using the results to highlight weakly tested modules for developers to improve.

What systematic steps do you take to identify, isolate, and fix flaky tests in an automated test suite?

• I rerun failing tests multiple times to confirm non-determinism, then check logs, timing, and dependency states. Once confirmed flaky, I isolate them in a “quarantine” group, review async or network-related code, and add mocks or controlled waits. After fixing, I monitor their stability across several CI runs before re-enabling them.

How do you document a complex testing framework so that new engineers can onboard rapidly? Share a structure or toolchain you’ve used.

• I keep docs lightweight and practical — a single README explaining test structure, setup steps, and examples. For larger systems, I add diagrams and short “how-to” guides in Markdown. The goal is that a new dev can clone the repo, run tests, and understand the flow in under 10 minutes.

When improving or refactoring existing test suites, how do you avoid causing large disruptions to active development cycles?

• I refactor in small, incremental PRs and run both the old and new test harnesses in parallel until stability is confirmed. Communication is key — I give teams a heads-up before changing test behavior and make sure existing pipelines stay green during the transition.

After a merge causes coverage to drop, what’s your investigation workflow? Which logs, diffs, or metrics do you consult, and how do you remediate?

• First, I check the diff for skipped tests or untested branches. Then I review coverage reports and CI logs to pinpoint which files dropped. I prioritize fixing or adding tests for critical logic first, then automate a follow-up check in the next build to confirm recovery.

Tell us about a time you had to overhaul or adapt your testing approach because it didn’t fit with your team’s processes. What compromise or innovation did you introduce?

• When I introduced automated backend testing at GlobalNet, the team was used to manual verification. I adapted by integrating tests into existing deployment scripts instead of forcing a separate workflow. It made testing feel like part of deployment, not an extra step — adoption shot up.

How do you work with developers who deprioritize testing? Give an example of how you’ve influenced better practices or provided tools to make testing less onerous.

• I lead by showing value, not lecturing. I’ve built simple automation that catches config errors before production — once developers saw it save time, they naturally started writing their own tests. Making testing painless is the best persuasion tool.

SPECIFIC PREVIOUS STARTUP EXPERIENCE

1. What is your specific experience with equity/cash split compensation models. Specifically have you participated in this in a startup before?
   • I have not previously participated in an equity/cash split compensation model at a startup. My professional experience has been primarily with established companies, including GlobalNet Inc. and Best Western. I am keen to explore and understand this model as I transition into the startup environment.
2. Which level of startups have you participated in previously (list below)? The more specific the better.

• pre-pre seed (MVP) (Startups generally with 5 - 10K in funding)
• pre seed (first revenue) (Startups with 50K - 250K in funding)
• seed (first outside hire other than founders and core leaders) (250 -1M in funding)
• Bridge Loan (growth expansion without diluting equity) (varies)
• Series A (pre for acquisition) 1-20M in funding
• Series B (pre for acquisition) 20M-100M in funding
• Series C (pre for acquisition) 100+ in funding

• Company more than 10 years old (Legacy companies)

• Based on the list provided, I have not participated in a company at the pre-seed through Series C stages. The companies I have worked for would fall under Company more than 10 years old (Legacy companies), as GlobalNet Inc. is an established provider. However, I bring a startup-like mindset to problem-solving, as evidenced by my work on architecting and deploying scalable backend automation systems.