codegraph-qa

name codegraph-qa description Use CodeScope to analyze any indexed codebase via its graph database (neug) and vector index (zvec). Supports Python, JavaScript/TypeScript, C, and Java (including Hadoop-scale repositories). Covers call graphs, dependency analysis, dead code detection, hotspots, module coupling, architectural layering, commit history, change attribution, semantic code search, impact analysis, full architecture reports, and bug root cause analysis from GitHub issues. Use this skill whenever the user asks about code structure, code dependencies, who calls what, why something changed, finding similar functions, generating architecture reports, understanding module boundaries, analyzing GitHub issues/bugs, finding bug root causes, understanding why a project has many bugs, tracing bugs to code, indexing Java projects, or any question that benefits from a code knowledge graph — even if they don't mention "CodeScope" by name. If a `.codegraph` or similar index directory exists in the workspace, this skill applies. CodeScope Q&A CodeScope indexes source code into a two-layer knowledge graph — structure (functions, calls, imports, classes, modules) and evolution (commits, file changes, function modifications) — plus semantic embeddings for every function. Supports Python, JavaScript/TypeScript, C, and Java (including Hadoop-scale repositories with 8K+ files). This combination enables analyses that grep, LSP, or pure vector search cannot do alone. It can also fetch GitHub issues and trace bugs to code — mapping bug reports to root cause candidates using the graph + vector infrastructure. When to Use This Skill User asks about call chains, callers, callees, or dependencies User wants to find dead code, hotspots, or architectural layers User asks about code history, who changed what, or why something was modified User wants to find semantically similar functions across a codebase User wants a full architecture analysis or report User asks about module coupling, circular dependencies, or bridge functions User wants to index or analyze a Java project (Maven, Gradle, plain Java) User wants to analyze GitHub issues or bug reports to find root causes User asks "why does this project have so many bugs" or "what code is most buggy" User wants to trace a bug report to the most relevant code locations A .codegraph directory (or similar index) exists in the workspace Getting Started Installation pip install codegraph-ai Environment Variables (optional) # Create Python virtural environment python -m venv .venv source .venv/bin/activate # Point to a pre-built database (skip indexing) export CODESCOPE_DB_DIR= "/path/to/.linux_db" # Offline mode for HuggingFace models export HF_HUB_OFFLINE= "1" Check Index Status codegraph status --db $CODESCOPE_DB_DIR If no index exists, create one: codegraph init --repo . --lang auto --commits 500 Supported languages: python , c , javascript , typescript , java , or auto (auto-detects from file extensions). The --commits flag ingests git history (for evolution queries). Without it, only structural analysis is available. Add --backfill-limit 200 to also compute function-level MODIFIES edges (slower but enables change_attribution and co_change ). Two Interfaces: CLI vs Python Use the CLI for status and reports: codegraph status --db $CODESCOPE_DB_DIR codegraph analyze --db $CODESCOPE_DB_DIR --output report.md Use the Python API for queries and custom analyses: import os os.environ[ 'HF_HUB_OFFLINE' ] = '1' # required from codegraph.core import CodeScope cs = CodeScope(os.environ[ 'CODESCOPE_DB_DIR' ]) # Cypher query rows = list (cs.conn.execute( ''' MATCH (caller:Function)-[:CALLS]->(f:Function {name: "free_irq"}) RETURN caller.name, caller.file_path LIMIT 10 ''' )) for r in rows: print (r) cs.close() # always close when done The Python API is more powerful — it gives you raw Cypher access and lets you chain queries. Core Python API Raw Queries These are the building blocks for any custom analysis: Method What it does cs.conn.execute(cypher) Run any Cypher query against the graph — returns list of tuples cs.vector_only_search(query, topk=10) Semantic search over all function embeddings — returns [{id, score}] cs.summary() Print a human-readable overview of the indexed codebase Structural Analysis Method What it does cs.impact(func_name, change_desc, max_hops=3) Find callers up to N hops, ranked by semantic relevance to the change cs.hotspots(topk=10) Rank functions by structural risk (fan-in × fan-out) cs.dead_code() Find functions with zero callers (excluding entry points) cs.circular_deps() Detect circular import chains at file level cs.module_coupling(topk=10) Find cross-module coupling pairs with call counts cs.bridge_functions(topk=30) Find functions called from the most distinct modules cs.layer_discovery(topk=30) Auto-discover infrastructure / mid / consumer layers cs.stability_analysis(topk=50) Correlate fan-in with modification frequency cs.class_hierarchy(class_name=None) Return inheritance tree for a class (or all classes) Semantic Search Method What it does cs.similar(function, scope, topk=10) Find functions similar to a given function within a module scope cs.cross_locate(query, topk=10) Find semantically related functions, then reveal call-chain connections cs.semantic_cross_pollination(query, topk=15) Find similar functions across distant subsystems Evolution (requires --commits during init) Method What it does cs.change_attribution(func_name, file_path=None, limit=20) Which commits modified a function? (requires backfill) cs.co_change(func_name, file_path=None, min_commits=2, topk=10) Functions that are always modified together cs.intent_search(query, topk=10) Find commits matching a natural-language intent cs.commit_modularity(topk=20) Score commits by how many modules they touch cs.hot_cold_map(topk=30) Module modification density Report Generation from codegraph.analyzer import generate_report report = generate_report(cs) # full architecture analysis as markdown Or via CLI: codegraph analyze --output reports/analysis.md The report covers: overview stats, subsystem distribution, top modules, architectural layers (with Mermaid diagrams), bridge functions, fan-in/fan-out hotspots, cross-module coupling, evolution hotspots, and dead code density. Java Support CodeScope includes a full Java adapter that handles enterprise-scale repositories like Apache Hadoop (~8K files, ~97K functions indexed in ~3.5 minutes). What Gets Indexed Element Graph Node/Edge Notes Classes Class node Includes generics, annotations Interfaces Class node extends → INHERITS edge Enums Class node Enum methods extracted Methods Function node Full generic signatures, JavaDoc Constructors Function node (name= <init> ) Including super() calls Method calls CALLS edge Receiver context preserved ( obj.method() ) new expressions CALLS edge to ClassName.<init> Constructor invocations Imports IMPORTS edge (file→file) Single, wildcard, static Inner classes Class node (name= Outer.Inner ) Prefixed with outer class Inheritance INHERITS edge extends + implements Indexing a Java Project codegraph init --repo /path/to/java-project --lang java --commits 500 Or with auto-detection (auto-detects .java files): codegraph init --repo /path/to/java-project --lang auto Java-Specific Exclusions By default, these directories are excluded when indexing Java projects: target/ , build/ , .gradle/ , .idea/ , .settings/ , bin/ , out/ , test/ , tests/ , src/test/ . Java Query Examples # Find all classes that extend a specific class list (cs.conn.execute( """ MATCH (c:Class)-[:INHERITS]->(p:Class {name: 'FileSystem'}) RETURN c.name, c.file_path """ )) # Find all methods in a specific class list (cs.conn.execute( """ MATCH (c:Class {name: 'DefaultParser'})-[:HAS_METHOD]->(f:Function) RETURN f.name, f.signature """ )) # Find constructor call chains list (cs.conn.execute( """ MATCH (f:Function)-[:CALLS]->(init:Function {name: '<init>'}) WHERE init.class_name = 'Configuration' RETURN f.name, f.file_path LIMIT 10 """ )) Bug Root Cause Analysis CodeScope can fetch GitHub issues and map them to code using the graph + vector infrastructure. This is the core workflow for answering questions like "why does this project have so many bugs?" or "where in the code does this bug come from?" Prerequisites A code graph must already be indexed for the target repository gh CLI must be installed and authenticated ( gh auth login ) Bug Analysis API Single Issue Analysis # Analyze a specific GitHub issue against the indexed code graph result = cs.analyze_issue( "owner" , "repo" , 1234 , topk= 10 ) print (result.format_report()) This: Fetches the issue from GitHub (or loads from cache) Parses file paths, function names, and stack traces from the issue body Matches extracted paths to File nodes in the graph Uses semantic search ( cross_locate ) to find related code Traces callers of mentioned functions via impact() Ranks and returns root cause candidates with explanation Batch Bug Analysis # Analyze top-k bug issues and get aggregated hotspot data results = cs.analyze_top_bugs( "owner" , "repo" , k= 10 , label= "bug" ) for r in results: print ( f"# {r.issue.number} : {r.issue.title} " ) for c in r.candidates[: 3 ]: print ( f" {c.function_name} ( {c.file_path} ) score= {c.score: .2 f} " ) CLI Commands # Fetch and parse a single issue (no graph needed) codegraph fetch-issue owner repo 1234 # Fetch top-k bugs from a repo codegraph fetch-bugs owner repo --top 10 --label bug # Analyze a single bug against the code graph codegraph analyze-bug owner repo 1234 --db .codegraph --topk 10 # Batch analyze top bugs codegraph analyze-bugs owner repo --db .codegraph --top 10 --label bug Lower-Level Components For custom analysis pipelines, the components can be used individually: from codegraph.issue_fetcher import fetch_and_parse_issue from codegraph.bug_locator import ( resolve_paths_to_files, find_semantic_matches, trace_callers, rank_root_causes, analyze_bug, ) # Fetch and parse (with caching) issue = fetch_and_parse_issue( "owner" , "repo" , 1234 ) print (issue.extracted_paths) # file paths found in body print (issue.extracted_funcs) # function names from stack traces print (issue.linked_commits) # merge commit SHAs from linked PRs # Match paths to graph nodes path_matches = resolve_paths_to_files(cs, issue.extracted_paths) # Semantic search using issue description semantic_matches = find_semantic_matches(cs, f" {issue.title} \n {issue.body} " ) # Trace callers of mentioned functions caller_traces = trace_callers(cs, issue.extracted_funcs, max_hops= 2 ) # Combine into ranked candidates candidates = rank_root_causes(path_matches, semantic_matches, caller_traces, issue.extracted_funcs) Scoring System Root cause candidates are scored by combining multiple signals: Signal Score Description Direct mention +1.0 Function name appears in issue body/stack trace File path match +0.8 Function is in a file mentioned in the issue Semantic match +score Raw cosine similarity (0.0-1.0) from cross_locate Caller relationship +0.5/hops Function calls a mentioned function (decays with distance) Issue Cache Parsed issues are cached at ~/.codegraph/issue_cache/{owner}_{repo}_{number}.json . Cache hits skip the GitHub API call entirely (sub-millisecond). To force a refresh, pass use_cache=False or use --no-cache on CLI. from codegraph.issue_cache import clear_cache clear_cache(owner= "openclaw" , repo= "openclaw" ) # clear specific repo clear_cache() # clear all Stack Trace Parsing The parser automatically extracts file paths and function names from stack traces in Python, C/C++, JavaScript/Node.js, Go, and Rust formats. It also extracts func_name() references in backticks and inline code. How to Route Questions The key decision is: does the user want an exact structural answer, a fuzzy semantic one, or a bug-to-code mapping? User asks... Best approach "Who calls free_irq ?" Cypher: MATCH (c:Function)-[:CALLS]->(f:Function {name: 'free_irq'}) RETURN c.name, c.file_path "Find functions related to memory allocation" cs.vector_only_search("memory allocation") or cs.cross_locate("memory allocation")