grammar-inference-engine/experiments/EXPERIMENT_LOG.md

Experiment Log — Grammar Inference Pipeline

Track what we tried, what worked, what failed, and what's next. Each experiment includes: hypothesis, method, result, verdict.

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Round 1: Context Strategies (commit bbdfe93)

Hypothesis: The calling context (prefix before the method body) determines which methods share a convention. Better context grouping → better grammars.

Method: Tested 4 strategies on RAGSAK (Kotlin) and Flask (Python):

• Baseline: Group by package directory
• Option A: Group by last k components of file path (file_path_k{k})
• Option B: Group by first k symbols of call sequence (first_k_sym_{k})
• Option C: Two-dimensional: (path_k, first_k_symbols)

Result:

Strategy	RAGSAK patterns	RAGSAK coverage	Flask patterns	Flask coverage
Package baseline	73	12.0%	21	10.7%
File path k=1	73	12.0%	21	10.7%
First k=1	20	4.6%	2	1.4%
First k=3	47	12.0%	21	10.7%

Verdict: Package grouping and first_k_sym_3 produce similar results. Cross-package grouping by first symbol is too sparse — most groups are either too large (skipped) or too diverse (skipped). The useful patterns are package-specific, not cross-package.

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Round 2: Reduce Algorithm (commit b516b29)

Hypothesis: Reduce (Algorithm 4, TODS 2010) merges structurally similar contexts, revealing cross-package patterns by unifying equivalent states.

Method: Implemented faithful Reduce with support-weighted SOA edit distance, adjunction, iterative merging, and minimize. Tested at ε=0.05 to 0.4.

Result:

• RAGSAK at ε=0.3: 1 merge (JobStatus.every.getJobStatus ↔ JobStatus.now.minusMinutes)
• Flask at ε=0.3: 1 merge (def.boolean ↔ def.is_boolean)
• Coverage improvement: negligible (< 1%)

Verdict: Reduce doesn't help. The contexts we produce are already too specific (unique per package) for the distance metric to find meaningful merges. Reduce works when you have a large SOA with many equivalent states — we have one SOA per package with few states. Wrong abstraction level.

Why it failed: Reduce merges states in a single automaton. We're producing one automaton per package group. There's nothing to merge across packages because each package gets its own inference run. Reduce would need to operate on a cross-package SOA, which we don't build.

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Round 3: Language Size Scoring (commit dfb56a0)

Hypothesis: Bex et al.'s Language Size measure (arXiv:1004.2372, Section 4.3.1) is a better scoring function than MDL for our use case.

Method: Implemented lang_size_score() as default scoring method. Added diversity threshold: skip groups with unique_ratio > 0.9 or methods < 5.

Result: 39 new tests. Pipeline runs correctly with new scoring. Coverage numbers similar to before — scoring method doesn't change which patterns are found, just which grammar is selected per group.

Verdict: Scoring method is not the bottleneck. The problem is upstream (pattern extraction), not downstream (pattern selection).

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Round 4: GBNF Output (commit 011df39)

Hypothesis: SORE → GBNF conversion enables constrained LLM generation. SORE operators map directly to GBNF syntax.

Method: Implemented recursive descent parser for SORE, AST intermediate representation, and GBNF renderer. 15 tests.

Result: All tests pass. to_gbnf('raise.(ValueError)+') → "raise" "ValueError"+. Correct mapping of +, ?, *, |, ., parens.

Verdict: Implementation works. But the input SOREs are too specific to individual packages to be useful for constrained generation. The converter is correct; the patterns it converts are the problem.

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Round 5: Cross-Package Exact Matches

Hypothesis: Some call sequences appear verbatim in multiple packages. These are the real cross-package conventions.

Method: Grouped all sequences by exact tuple match across packages.

Result: 38 exact cross-package sequences in RAGSAK. Most are trivial:

• ('clearAllMocks',) — 4 packages (test teardown)
• ('Builder',) — 4 packages (builder pattern)
• ('Any',) — 4 packages (Kotlin type)
• ('get',) — 4 packages (getter)

Interesting ones:

• ('assumeTrue', 'isDockerAvailable', 'start', 'pullAndWarmup') — 4 packages (Docker test setup)
• ('isNullOrBlank', 'error', 'error') — 3 packages (null check → error)
• ('sortedBy', 'map', 'toDescriptor') — 3 packages (data pipeline)
• ('ObjectMapper', 'findAndRegisterModules') — 2 packages (Jackson config)

Verdict: Exact matches are too rare and mostly trivial. The real cross-package patterns are structural, not textual — "null check → error" appears with different method names in different packages.

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Round 6: Structural Coarsening (Experiment coarsen_eval.py)

Hypothesis: Coarsening structural tokens (keywords, types) while keeping function names raw reveals cross-package patterns that pure-text misses.

Method: coarsen_token() maps tree-sitter capture names to categories. Function calls kept as raw text (they ARE the content). Only structural tokens coarsened: RETURN, RAISE, IF, LOOP, EXCEPTION, KW, TYPE, FUN, ATTR.

Result:

Codebase	Structural %	Raw coverage	Coarsened coverage	Δ
RAGSAK	4.7%	18.6%	18.3%	-0.3%
Flask	36.1%	17.5%	28.8%	+11.3%

Key findings:

• RAGSAK: 95.3% function calls → coarsening has nothing to work with
• Flask: 36.1% structural → coarsening significantly improves grouping
• Flask k=3: cross-package contexts increase 40 → 47
• Coarsened cross-package shapes: ('IF', 'KW', 'RETURN') in 4 packages, ('RETURN', 'render_template', 'render_template') in 6 packages

Verdict: Coarsening helps codebases with rich structural tokens (Python: IF, LOOP, EXCEPTION, KW). Doesn't help codebases dominated by function calls (Kotlin: 95% calls). The approach is sound but language-dependent in practice — depends on how rich the highlights.scm is.

What we learned:

• The 5% structural tokens DO carry signal when they exist
• Python highlights.scm is much richer than Kotlin's
• Coarsening is not dead — it's a tool for languages with rich captures
• The real question is whether the coarsened patterns are USEFUL, not just whether they exist

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What We Learned (Summary)

1. Per-package grouping is too sparse. 1-3 sequences per package isn't enough for any inference method to produce general patterns.

2. Cross-package exact matches are rare. Only 38 in RAGSAK, mostly trivial single-call sequences.

3. Reduce doesn't help at our abstraction level. It merges states within one automaton; we need to merge patterns across packages.

4. Scoring/selection isn't the bottleneck. MDL vs Language Size doesn't change what patterns are found.

5. The calling context prefix is the right signal but grouping by it produces groups that are either too large, too diverse, or trivial.

6. GBNF converter works correctly but the input patterns are too specific.

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Next: Structural Coarsening + Cross-Package Detection

Idea

Collapse method names → categories using tree-sitter capture names. This converts textual sequences into structural shapes:

('isNullOrBlank', 'error', 'error')     →  (CALL, ERROR, ERROR)
('raise', 'ValueError', 'ValueError')   →  (CALL, ERROR, ERROR)

Same structural shape, different packages → cross-package convention.

Why This Might Work

• We already extract tree-sitter capture names in code.py:56-69
• We already classify nodes into categories in code.py:72-100 (lit, call, var, lambda, kwarg, expr, template, other)
• The behavioral prefix filter (CALL_PREFIXES) keeps raw text; we need a parallel path that keeps the category instead
• Coarsened sequences have smaller alphabets → more methods per group → better inference
• Patterns like (CALL, ERROR, ERROR) are meaningful conventions that repeat across packages

What We Need

1. Coarsening map: capture_name → category using the existing CALL_PREFIXES, ARG_LITERAL_TYPES, LAMBDA_TYPES classifications plus a new ERROR_TYPES set

2. Coarsened sequence extraction: Same pipeline as now, but output category labels instead of method names

3. Cross-package grouping: Group by coarsened shape (first k categories), find shapes that appear in ≥2 packages

4. SORE inference on coarsened sequences: Smaller alphabet, more examples per group → better patterns

5. Evaluation: Compare coarsened patterns vs raw patterns on:

   • Coverage (% of methods in learned groups)
   • Cross-package reach (# of packages per pattern)
   • Usefulness for constrained generation (GBNF quality)

Open Questions

• Does coarsening lose too much specificity? (CALL, ERROR, ERROR) is less informative than (raise, ValueError, ValueError) — is the tradeoff worth it?
• What categories to use? The existing classifications in code.py are a starting point but may need refinement (e.g., separating ERROR from CALL)
• How to handle the long tail? Most sequences are 1-2 symbols — coarsening doesn't help much for those
• Is the GBNF output useful at all? Maybe the output should be a conditional frequency table instead of a grammar

Experiment Design

Phase 1: Coarsening Proof of Concept

• Implement coarsening map in code.py
• Add --coarsen flag to CLI
• Run on RAGSAK + Flask, compare raw vs coarsened patterns
• Measure: alphabet size reduction, group size increase, pattern count

Phase 2: Cross-Package Detection

• Group coarsened sequences by shape (first k categories)
• Find shapes appearing in ≥2 packages
• For each shape, infer SORE on coarsened sequences
• Measure: cross-package patterns found, coverage improvement

Phase 3: Output Quality

• Convert coarsened SOREs to GBNF
• Evaluate: are the GBNF rules more general/useful than raw SOREs?
• Compare: coarsened GBNF vs raw GBNF vs conditional frequency table

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The "Redacted" Concept

When collapsing method names to categories, we lose the specific method name but gain the structural pattern. This is a form of abstraction — moving from concrete examples to general rules.

The question is whether the abstraction is at the right level:

• Too specific: (raise, ValueError, ValueError) — package-specific noise
• Right level: (CALL, ERROR, ERROR) — cross-package convention
• Too abstract: (X, Y, Y) — trivial, tells the LLM nothing

The middle ground depends on the category vocabulary. We need enough categories to be informative (CALL, ERROR, LIT, VAR, TYPE, etc.) but not so many that every sequence is unique.