Project 8: Interactive BASIC Environment (REPL)

Create a robust REPL with immediate mode, stored program mode, and script playback.

Quick Reference

Attribute Value
Difficulty Level 4 - Hard
Time Estimate 16-24 hours
Main Programming Language Host language implementation
Alternative Programming Languages Rust, Go, TypeScript, C
Coolness Level Level 5 - Legendary
Business Potential 2 - Niche but Monetizable
Prerequisites state machines, prompt UX, session scripting
Key Topics input classification, program buffer editing, prompt recovery

1. Learning Objectives

By completing this project, you will:

  1. Build a concrete artifact with deterministic success and failure evidence.
  2. Convert the core question into explicit engineering decisions.
  3. Use fixture-driven validation to protect behavior from semantic drift.
  4. Document trade-offs so another learner can continue your work.
  5. Produce interview-ready reasoning for design and debugging choices.

2. All Theory Needed (Per-Concept Breakdown)

REPL State Machines

Fundamentals

REPL State Machines is the operational backbone for this project. You should treat it as a contract that defines what can happen, what must never happen, and how to detect violations quickly. In this context, the concept is not abstract background knowledge; it controls real user outcomes. Each action in your implementation should have a clear precondition, a deterministic transition, and a measurable postcondition. If these are missing, the system might appear to work for simple demos but will fail under edge cases and portability checks. The practical mindset is to make behavior inspectable: each stage should expose enough structure to support debugging without overwhelming logs.

Deep Dive into the concept

A strong implementation starts by identifying the uncertainty boundaries that REPL State Machines must control. Common uncertainty sources include malformed input, ambiguous interpretation rules, hidden defaults, and environment-dependent behavior. Your first task is to reduce these to explicit rules. Define canonical representations, transition order, and error taxonomy before broad feature work. This makes later extension work safer and prevents cascading failures.

In this project, apply the concept as a series of bounded transitions. Each transition should consume one validated input shape, modify state in one intentional way, and produce structured output. That makes defects local: if tests fail, you can trace the exact transition that violated invariants. Build one success fixture and one failure fixture per major feature. Success fixtures confirm happy-path behavior; failure fixtures prove diagnostics and recovery quality. Both are mandatory for trustworthy tooling.

The next layer is observability. Implement a concise trace mode that records phase, state identifier, status, and error class. Avoid verbose dumps by default. Observability should answer “what changed and why” rather than print every internal detail. This keeps logs useful as project complexity grows.

Finally, connect the concept to maintainability. Implementation details may change over time, but stable contracts keep external behavior predictable. If you redesign internals later, contract tests ensure users do not experience silent regressions. That is why REPL State Machines is central to this project: it turns complexity into accountable engineering decisions.

How this fit on projects

  • Primary usage: this project’s core implementation path.
  • Secondary usage: portability checks, diagnostics, and release confidence gates.

Definitions & key terms

  • Invariant: Condition that must remain true through all transitions.
  • Canonical representation: Normalized internal shape used by all downstream stages.
  • Deterministic output: Same input and environment produce identical output.
  • Recovery path: Documented behavior after an expected failure.

Mental model diagram

[Input] -> [Validate] -> [Canonicalize] -> [Transition] -> [Output]
   |           |               |               |            |
   +--> [Typed Error]          +--> [Trace Event]          +--> [Fixture Assertion]

How it works (step-by-step, invariants and failure modes)

  1. Validate input against strict schema.
  2. Normalize into canonical internal form.
  3. Execute one deterministic state transition.
  4. Emit output and trace metadata.
  5. Assert against golden fixture.

Invariants: strict schema, stable ordering, typed error classes. Failure modes: hidden defaults, ambiguous ordering, under-specified diagnostics.

Minimal concrete example

SUCCESS CASE: status=OK, output_hash=abc123, exit_code=0
FAILURE CASE: status=ERROR, class=ValidationError, exit_code=2

Common misconceptions

  • “Passing one demo means behavior is solid.” Reliable behavior requires fixtures across edge cases.
  • “Determinism only matters in CI.” Determinism is critical for debugging and collaboration.
  • “Readable error text is enough.” Structured error categories are needed for tooling.

Check-your-understanding questions

  1. Which invariant is most likely to fail first in your design?
  2. How will you prove behavior is deterministic across reruns?
  3. What information must every failure response include?

Check-your-understanding answers

  1. The boundary between validation and canonicalization is usually highest risk.
  2. Re-run fixed fixtures in a clean environment and diff outputs/exit codes.
  3. Error class, location/context, and actionable remediation hint.

Real-world applications

  • Language tooling reliability.
  • Runtime debugging and observability.
  • Migration pipelines requiring stable behavior.

Where you’ll apply it

  • Implementation phase and test harness design for this project.

References

  • “Crafting Interpreters” by Robert Nystrom.
  • “Programming Language Pragmatics” by Michael Scott.
  • “Working Effectively with Legacy Code” by Michael Feathers.

Key insights

Behavior becomes trustworthy when transitions are explicit, testable, and observable.

Summary

REPL State Machines keeps this project deterministic and debuggable as complexity grows.

Homework/Exercises to practice the concept

  1. Define three invariants and map each to one fixture.
  2. Add one deterministic failure transcript.
  3. Design a trace event schema with four required fields.

Solutions to the homework/exercises

  1. Include invariant id, fixture id, and pass/fail criterion.
  2. Include stable error class and exit code.
  3. Minimum fields: phase, state id, status, message.

Session Isolation and Recovery

Fundamentals

Session Isolation and Recovery controls how you handle variability across dialects, environments, and feature sets without losing confidence. Instead of assuming behavior is portable, you classify it and attach evidence. This concept is about explicit boundaries: what behavior is guaranteed, what behavior can vary, and what mitigation exists when variation appears. For this project, portability is not a bonus feature; it is a quality property. If your compatibility assumptions are undocumented, users will discover breakage before your tests do.

Deep Dive into the concept

Treat compatibility work as a matrix problem. Each row represents a behavior or feature; columns capture expected behavior, observed behavior, status, evidence, and mitigation. This structure prevents vague claims and turns migration risk into manageable tasks. Start with the smallest meaningful matrix and expand as you add features.

Use deterministic fixtures as evidence anchors. Every matrix claim should reference at least one fixture. If no fixture exists, mark confidence as provisional and avoid shipping strong claims. This discipline prevents documentation drift.

Status normalization is crucial. Use explicit labels (PASS, PARTIAL, UNSUPPORTED, FAIL) and define each label in the report header. Clear statuses improve communication across technical and non-technical stakeholders.

When drift appears, choose one of three paths: adapter, rewrite, or defer. Adapter fits when behavior can be transformed safely. Rewrite fits when semantics differ too much. Defer is valid only with explicit risk note and trigger conditions for re-evaluation. These decisions should be visible in your report so future maintainers understand rationale.

Over time, this concept makes the project resilient. Even as targets change, the matrix and fixtures provide continuity. Teams can onboard faster, regressions are caught earlier, and migration discussions become evidence-based.

How this fit on projects

  • Core for compatibility and migration reporting.
  • Also useful for release notes and test prioritization.

Definitions & key terms

  • Compatibility surface: Behavior promised to remain stable.
  • Variance surface: Behavior allowed to differ by target or mode.
  • Adapter rule: Transformation preserving intent across targets.
  • Semantic drift: Observable behavior divergence over time/targets.

Mental model diagram

[Feature] -> [Classify] -> [Evidence Fixture] -> [Status] -> [Mitigation]

How it works (step-by-step, invariants and failure modes)

  1. Enumerate target behaviors.
  2. Run fixture evidence across targets.
  3. Classify status using normalized rules.
  4. Document mitigation for non-pass cases.
  5. Publish reproducible report.

Invariants: every claim has evidence, statuses are defined, mitigations are actionable. Failure modes: stale matrices, undocumented assumptions, ambiguous status labels.

Minimal concrete example

Feature: structured branch
Target A: PASS
Target B: PARTIAL (adapter required)
Target C: UNSUPPORTED (rewrite path documented)
Evidence: fixture F12

Common misconceptions

  • “Compatibility is compile success.” Behavior equivalence is required.
  • “One good demo proves portability.” Matrix evidence across fixtures is necessary.
  • “Documentation can come later.” Early matrices prevent design drift.

Check-your-understanding questions

  1. What belongs in compatibility surface vs variance surface?
  2. When is adapter preferable to rewrite?
  3. Why normalize status labels?

Check-your-understanding answers

  1. Compatibility surface contains guaranteed behavior; variance surface contains controlled differences.
  2. Adapter is preferable when behavior can be preserved with bounded translation.
  3. Normalized labels support consistent decisions and automated checks.

Real-world applications

  • Enterprise modernization planning.
  • Multi-runtime language tooling.
  • Cross-platform release governance.

Where you’ll apply it

  • Report generation, migration recommendations, and readiness scoring in this project.

References

Key insights

Compatibility quality is measured through evidence, not assumptions.

Summary

Session Isolation and Recovery turns portability from folklore into a repeatable engineering workflow.

Homework/Exercises to practice the concept

  1. Build a 10-row compatibility matrix for this project.
  2. Add one adapter rule with before/after evidence.
  3. Define readiness score inputs and weights.

Solutions to the homework/exercises

  1. Include status, evidence id, confidence, and mitigation columns.
  2. Document residual risk and fallback behavior.
  3. Score should weight blockers, fixture pass ratio, and confidence.

3. Project Specification

3.1 What You Will Build

You will build a complete artifact for “Interactive BASIC Environment (REPL)” with deterministic run instructions, golden and failure scenarios, and compatibility-aware documentation. In scope: core behavior, fixture evidence, diagnostics, and report output. Out of scope: unrelated optimization work and unverified feature claims.

3.2 Functional Requirements

  1. One deterministic golden-path scenario with exit code 0.
  2. One deterministic failure-path scenario with non-zero exit code.
  3. Structured status and diagnostic output.
  4. Fixture suite protecting key invariants.
  5. Documentation sufficient for third-party reproduction.

3.3 Non-Functional Requirements

  • Performance: Fast enough for iterative development.
  • Reliability: Repeatable behavior under fixed inputs and environment.
  • Usability: Clear commands and actionable diagnostics.
  • Maintainability: Decision log and compatibility notes included.

3.4 Example Usage / Output

COMMAND: run fixture set
OUTPUT: deterministic transcript + summary + exit code

3.5 Data Formats / Schemas / Protocols

  • Fixtures: plain text with stable ordering.
  • Outputs: line-oriented transcript format.
  • Reports: markdown tables with status and mitigation fields.

3.6 Edge Cases

  • Missing fixture files.
  • Unsupported feature in strict mode.
  • Invalid state transition request.
  • Conflicting flags that break determinism.

3.7 Real World Outcome

3.7.1 How to Run (Copy/Paste)

# setup environment
# run success fixture
# run failure fixture
# compare with golden output

3.7.2 Golden Path Demo (Deterministic)

STATUS: READY
RUNNING: golden fixture
RESULT: PASS
EXIT CODE: 0

3.7.3 If CLI: exact transcript style

$ ./project-08 --fixture fixtures/golden.txt
PASS: golden scenario
REPORT: outputs/project-08-golden.md

$ ./project-08 --fixture fixtures/failure.txt
ERROR: ValidationError code=E_INPUT
EXIT CODE: 2

3.7.4 Failure Demo

Input: malformed fixture
Output: typed error + remediation hint + non-zero exit code

4. Solution Architecture

4.1 High-Level Design

[Input] -> [Normalization] -> [Core Engine] -> [Output/Report]
                  +------------------------------+
                  |  Diagnostics + Trace + Codes |
                  +------------------------------+

4.2 Key Components

Component Responsibility Key Decisions
Input Loader Validate and normalize fixtures Strict schema and deterministic ordering
Core Engine Execute project-specific behavior Explicit transitions and typed outcomes
Diagnostics Emit actionable errors Stable class-based schema
Reporter Summarize evidence Diff-friendly markdown output

4.3 Data Structures (No Full Code)

ProjectState { mode, phase, canonical_input, status }
ResultRecord { status, output_hash, exit_code, diagnostics[] }

4.4 Algorithm Overview

  1. Load and validate fixture.
  2. Canonicalize input.
  3. Execute deterministic transitions.
  4. Emit outputs and diagnostics.
  5. Compare with golden expectations.

Complexity:

  • Time O(n) on fixture size.
  • Space O(n) for canonical state and reporting.

5. Implementation Guide

5.1 Development Environment Setup

# install runtime and dependencies
# pin versions
# run smoke check

5.2 Project Structure

project-08/
  fixtures/
  outputs/
  docs/
  src/
  tests/

5.3 The Core Question You’re Answering

“How do you keep a prompt-driven language environment stable under user mistakes?”

5.4 Concepts You Must Understand First

  1. REPL State Machines
    • Which invariants does it require?
    • How will violations be detected?
  2. Session Isolation and Recovery
    • Which behavior is portable vs target-specific?
    • What mitigation exists when behavior diverges?
  3. Deterministic verification
    • How will outputs be normalized and frozen?

5.5 Questions to Guide Your Design

  1. Which transition is most failure-prone?
  2. What evidence is required for compatibility claims?
  3. Which errors are recoverable vs fatal?

5.6 Thinking Exercise

Draw a full success path and failure path, annotating expected state at each step.

5.7 The Interview Questions They’ll Ask

  1. What invariant protects your core behavior?
  2. How do you prove deterministic behavior?
  3. Where can semantic drift appear?
  4. How does your diagnostics model help debugging?
  5. Which design trade-off did you intentionally make?

5.8 Hints in Layers

Hint 1: Add observability before full feature coverage.

Hint 2: Freeze one golden fixture early.

Hint 3: Add one failure fixture per major feature.

Hint 4: Keep core logic separate from reporting.

5.9 Books That Will Help

Topic Book Chapter
Interpreter architecture “Crafting Interpreters” Architecture chapters
Language behavior “Programming Language Pragmatics” Semantics and evolution
Safe change “Working Effectively with Legacy Code” Characterization and seams
Representation model “Code” by Charles Petzold Representation/execution chapters

5.10 Implementation Phases

Phase 1: Foundation (16-24 hours)

  • Define fixtures and output schema.
  • Implement baseline validation and diagnostics.

Checkpoint: one golden fixture passes.

Phase 2: Core Functionality (16-24 hours)

  • Implement primary behavior with invariant checks.
  • Add deterministic trace and failure paths.

Checkpoint: success and failure fixtures both stable.

Phase 3: Polish & Edge Cases (16-24 hours)

  • Expand edge-case coverage.
  • Finalize compatibility report and docs.

Checkpoint: full suite and docs reviewed.

5.11 Key Implementation Decisions

Decision Options Recommendation Rationale
Error model plain text or typed classes typed classes better automation and debugging
Validation point early or late early prevents cascading failures
Compatibility method implicit or matrix-driven matrix-driven explicit migration risk control

6. Testing Strategy

6.1 Test Categories

Category Purpose Examples
Unit checks Validate isolated transitions parser/token/state step
Integration checks Validate full flow fixture to report pipeline
Edge checks Validate failure behavior malformed input, unsupported feature

6.2 Critical Test Cases

  1. Golden fixture: expected output + exit code 0.
  2. Failure fixture: typed error + non-zero exit code.
  3. Compatibility fixture: stable status classification.

6.3 Test Data

fixtures/golden.txt
fixtures/failure.txt
fixtures/compatibility.txt

7. Common Pitfalls & Debugging

7.1 Frequent Mistakes

Pitfall Symptom Solution
Hidden assumptions Flaky tests Make assumptions explicit in fixtures
Weak diagnostics Slow debugging Add typed errors with concise hints
Unscoped portability claims Unexpected migration failures Use evidence-backed matrix rows

7.2 Debugging Strategies

  • Diff current output against golden transcript.
  • Enable trace mode around failing transition boundaries.
  • Re-run in clean environment to confirm determinism.

7.3 Performance Traps

  • Excessive report recomputation without incremental caching.
  • Verbose default traces that hide critical signal.

8. Extensions & Challenges

8.1 Beginner Extensions

  • Add a second golden fixture with alternate path.
  • Add clearer remediation hints for top three errors.

8.2 Intermediate Extensions

  • Add compatibility scoring.
  • Add report diff mode across versions.

8.3 Advanced Extensions

  • Add adapter suggestion generation.
  • Add multi-target regression run summaries.

9. Real-World Connections

9.1 Industry Applications

  • Legacy modernization with strict behavior preservation.
  • Multi-runtime toolchains and migration planning.

9.3 Interview Relevance

This project trains concrete answers for deterministic system behavior, compatibility trade-offs, and migration safety.

10. Resources

10.1 Essential Reading

  • “Crafting Interpreters” by Robert Nystrom.
  • “Programming Language Pragmatics” by Michael Scott.
  • “Working Effectively with Legacy Code” by Michael Feathers.

10.2 Video Resources

  • Talks on interpreter design and language migration.

10.3 Tools & Documentation

11. Self-Assessment Checklist

11.1 Understanding

  • I can explain the core invariants.
  • I can classify portability risks with evidence.
  • I can justify my mitigation strategy.

11.2 Implementation

  • Functional requirements are complete.
  • Success and failure transcripts are deterministic.
  • Documentation is reproducible by another learner.

11.3 Growth

  • I can explain this project in interview format.
  • I documented at least one trade-off and one improvement.
  • I can connect this project to adjacent projects.

12. Submission / Completion Criteria

Minimum Viable Completion

  • deterministic success transcript,
  • deterministic failure transcript,
  • setup/run documentation.

Full Completion

  • all minimum criteria plus compatibility matrix with evidence.

Excellence

  • multi-target regression evidence and adapter recommendations.

13. Additional Content Rules (Hard Requirements)

13.1 Determinism

Freeze random/time/environment variables or document why they do not affect outputs.

13.2 Outcome Completeness

  • include one success golden demo,
  • include one failure demo,
  • document exit codes and error classes.

13.3 Cross-Linking

Reference sections in this file and related projects for transfer learning.

13.4 No Placeholder Text

This file is ready to execute as a learning plan.