Codebase Comprehension Algorithms

Community Codebase Comprehension And Domain Mapping Algorithms Best Practices

A practitioner-oriented reference of the algorithms that work for mapping a codebase into understandable feature/business domains. Most of these techniques live in the Software Architecture Recovery and Mining Software Repositories literatures and are invisible to working engineers — yet they're the right tools for the job a coding agent is asked to do every day: "what does this codebase do, and where?"

The 47 rules are organized by execution-lifecycle impact: a wrong decision early in the pipeline (which graph to build, which identifiers to keep) propagates through everything downstream. The three CRITICAL categories (graph-, clust-, valid-) are the ones a wrong call cannot be recovered from later. Read them first.

Scope: proven algorithms with peer-reviewed citations or canonical books — Newman Networks, Leskovec-Rajaraman-Ullman Mining of Massive Datasets, Ganter-Wille Formal Concept Analysis, plus 40+ ICSE / FSE / TSE / PNAS / JMLR papers. No tutorial sites, no Stack Overflow, no marketing posts. Deliberately deferred to a future version: GNN/CodeBERT/code2vec (not "proven over decades" yet) and refactoring-recipe stuff (covered by sibling skills like react-refactor and typescript-refactor).

When to Apply

Use these rules when:

• Onboarding an agent into an unfamiliar codebase: "explain what this codebase does, by domain"
• Producing an architecture map: "what are the main subsystems and how do they connect?"
• Locating a feature: "which files implement payments / authentication / search?"
• Reviewing a refactor: "did this change respect the architectural boundaries?"
• Detecting architectural debt: "what files have surprising coupling?"
• Validating an existing decomposition: "does the README's architecture match the code?"
• Picking algorithms for any of the above — the user wants something that's proven, not vibes

Rule Categories By Priority

Quick Reference

1. Graph Construction & Edge Weighting (CRITICAL)

• graph-filter-omnipresent-utilities-before-clustering — Drop the loggers and base classes BEFORE clustering (20-40 MoJoFM points)
• graph-pick-edge-type-by-question-asked — Call, import, co-change, bipartite — the question determines the graph
• graph-collapse-sccs-before-clustering — Tarjan SCC condensation makes cycles explicit and stabilises every algorithm
• graph-weight-edges-by-information-content — IDF / PMI / Jaccard on edges suppresses noise (2-5× MoJoFM)
• graph-bipartite-file-term-for-joint-structure — When DI / dynamic dispatch hides the call graph
• graph-combine-signals-in-multilayer-graphs — Mucha 2010 multilayer modularity over normalised α-weighted layers

2. Community Detection & Clustering (CRITICAL)

• clust-leiden-not-louvain — Louvain produces disconnected communities on 5-25% of nodes (Traag 2019)
• clust-infomap-mdl-on-random-walks — MDL on random walks; the right tool for flow-meaningful graphs
• clust-stochastic-block-model — Bayesian, hierarchical, learns K from data; handles non-assortative structure
• clust-mcl-markov-clustering — Flow simulation; dominant in bioinformatics; robust to noise
• clust-walktrap-short-random-walks — Random-walk distance + hierarchical agglomerative
• clust-spectral-laplacian-fiedler — Optimal k-way normalised cut via Laplacian eigenvectors
• clust-hdbscan-density-based — When clustering on file embeddings, not graphs

3. Validation & Quality Metrics (CRITICAL)

• valid-mojofm-as-software-clustering-distance — The SAR gold-standard distance metric (Wen-Tzerpos 2004)
• valid-adjusted-rand-index-and-nmi — Chance-corrected cross-algorithm comparison
• valid-be-aware-of-resolution-limit — Modularity can't see clusters smaller than √(2m) (Fortunato-Barthélemy PNAS 2007)
• valid-consensus-clustering-for-stability — A single-run answer is unreliable; consensus across runs is the right answer
• valid-cochange-prediction-as-ground-truth-proxy — Temporal held-out co-change replaces missing ground truth
• valid-ablate-each-input-signal — Leave-one-out; reveals which input actually drives the result

4. Identifier & Lexical Preprocessing (HIGH)

• lex-split-identifiers-with-samurai — 87% precision vs 60% for naive camelCase (Enslen MSR 2009)
• lex-build-programming-language-stop-words — Three-layer: keywords + generic + IDF-driven
• lex-expand-abbreviations-with-context — usr → user, ctx → context (Lawrie GenTest 2011)
• lex-tf-idf-and-bm25-on-identifiers — Raw counts are dominated by common terms; TF-IDF / BM25 fix it
• lex-stem-versus-subword-tokenization — Porter stemmer for clustering, BPE for embeddings
• lex-extract-verb-object-pattern-from-method-names — getUserById → (verb=get, object=user); compound concept signal

5. Software-Specific Architecture Recovery (HIGH)

• arch-bunch-with-mq-fitness — MQ fitness function + search; better than Q-maximization on code
• arch-acdc-subgraph-patterns — Subsystem and skeleton patterns; matches architect intuition
• arch-limbo-information-bottleneck — Tishby's IB applied to software (Andritsos-Tzerpos 2005)
• arch-reflexion-model — Compare hypothesized vs actual; the underused gem from Murphy-Notkin 1995
• arch-dsm-partitioning — Design Structure Matrix; 60-year-old engineering technique

6. Topic Modelling on Source Code (HIGH)

• topic-lda-on-source-code — Probabilistic per-file topic distributions over identifier+comment text
• topic-lsi-svd-on-term-document — Deterministic SVD-based semantic embeddings (Maletic-Marcus 2001)
• topic-nmf-non-negative-factorization — Parts-based additive topics, fully reproducible
• topic-hdp-for-nonparametric-topic-count — Hierarchical Dirichlet Process — learns K from data
• topic-pick-topic-count-by-coherence-not-perplexity — Perplexity is anti-correlated with human topic quality

7. Evolutionary Coupling & Co-Change Mining (HIGH)

• evol-mine-cochange-with-lift-and-confidence — Lift > 2 is the cutoff; raw co-change count is noise
• evol-filter-large-commits — A 200-file commit produces 20K spurious pair-counts; filter aggressively
• evol-temporal-decay-on-edge-weights — Exponential decay with 6-month half-life
• evol-logical-coupling-as-architectural-signal — 30-50% of strongest coupling is invisible to static analysis (Gall 1998)

8. Information-Theoretic Methods (MEDIUM-HIGH)

• info-normalized-compression-distance — Cluster without feature engineering; gzip-based universal similarity
• info-mutual-information-as-coupling — Catches non-linear / conditional coupling that lift misses
• info-mdl-for-model-selection — Principled K selection; Occam's razor as a code length
• info-naturalness-of-code-as-quality-signal — Hindle 2012 — code is 30-50% more predictable than English; bugs spike entropy

9. Centrality, Hierarchy & Labelling (MEDIUM)

• rank-pagerank-for-module-importance — Architectural spine via PageRank on the reversed dependency graph
• rank-hits-hubs-and-authorities — Orchestrators vs implementations (Kleinberg 1999)
• rank-betweenness-centrality-for-bottlenecks — Bridges between domains; god-class detection
• rank-textrank-for-cluster-labels — Multi-word keyphrases as cluster labels (Mihalcea-Tarau 2004, YAKE 2020)

How to Use

Start with the question the agent is trying to answer:

• "What are the main domains in this codebase?" → graph- (pick a graph) → clust- (Leiden / Infomap / SBM) → topic- (label them) → valid- (sanity-check stability and ablate)
• "Which files implement feature X?" → topic-lda-on-source-code for theme location; rank-pagerank-for-module-importance with X's files as seed for personalized PageRank
• "Where is the architectural spine?" → rank-pagerank-for-module-importance + rank-hits-hubs-and-authorities on the dependency graph
• "Does the README's architecture match the code?" → arch-reflexion-model is purpose-built for this
• "What's the real coupling here (beyond static dependencies)?" → evol-logical-coupling-as-architectural-signal and evol-mine-cochange-with-lift-and-confidence
• "How do I cluster without designing features?" → info-normalized-compression-distance
• "How big are the clusters supposed to be?" → valid-be-aware-of-resolution-limit and topic-hdp-for-nonparametric-topic-count
• "How do I know my decomposition is right?" → the entire valid- category; multi-proxy evaluation is mandatory

The skill's worldview: build the right graph first (and filter omnipresent files), pick an algorithm matching the graph and the question, use a code-specific preprocessing pipeline (Samurai + stop-words + stemming + TF-IDF) where lexical signals matter, and always validate — MoJoFM if you have expert ground truth, consensus + co-change prediction + ablation if you don't.

Code examples are in Python because the reference implementations (networkx, igraph, leidenalg, scikit-learn, gensim, graph-tool, hdbscan) all live there. The reasoning generalises to any language.

Reference Files

Related Skills

• computer-science-algorithms — Algorithm-and-data-structure reference (this skill cross-references it for MinHash/LSH, Aho-Corasick, etc.)
• complexity-optimizer — Static analysis for hot paths the rules here identify
• design-to-react-algorithms — Companion skill for design-to-code structural recovery