2026-10-06T05:02:32Z https://api.figshare.com/v2/oai

oai:figshare.com:article/34040505 2026-10-01T06:21:03Z category_29203 item_type_3 month_year_10_2026

BinFuncGt N A (25137249) Software engineering not elsewhere classified Code and Source BinFuncGTBinFuncGT is a research framework for binary software composition analysis (BSCA). It provides function-level ground truth, static feature extraction, candidate retrieval, call-graph-aware structural matching, and evaluation of component and version identification.The project, benchmark, and construction utilities are named BinFuncGT. Python modules and filesystem paths use the lowercase spelling <code>binfuncgt</code>.<h2 dir="ltr">Features</h2><ul><li>Binary analysis: Parse ELF metadata, symbol tables, and DWARF information, and recover functions and call graphs with angr CFGFast.</li><li>Function representation: Extract instruction counts, basic-block counts, control-flow edges, call sites, immediate-constant statistics, cyclomatic complexity, and mnemonic histograms.</li><li>Candidate retrieval: Generate candidate function pairs using cosine similarity, a top-r selection rule, and a similarity threshold.</li><li>Structural matching: Combine local similarity, preserved call relationships, and one-to-one matching constraints in a unified optimization objective.</li><li>Reduction and classical solvers: Apply safe reductions and interaction-graph decomposition, with greedy, Hungarian, CP-SAT, simulated annealing, and small-instance exhaustive solvers.</li><li>Function-level evaluation: Measure precision, recall, and F1 against raw and strict ground-truth correspondences.</li><li>Version identification: Explore constant features, string features, differential weighting, and version-discriminative string scoring.</li><li>Statistical analysis: Provide paired comparisons, ranking diagnostics, and Benjamini–Hochberg false discovery rate correction.</li></ul><h2 dir="ltr">Repository Structure</h2>The main directories and modules are organized as follows:<pre><pre>. ├── configs/ # Benchmark and experiment configurations ├── data/ # Binary data, intermediate features, and instances ├── experiments/ │ ├── pilot/ # Matching, baseline, ablation, and ranking results │ │ └── score_cache_v2/ # Per-instance scoring caches │ ├── case_study/ # Debian case-study results and reports │ └── versiongrid_v3/ # Extended version-grid design and build records ├── results/ # Aggregate results, statistics, and logs ├── scripts/ # Experiment and report-generation entry points ├── src/binfuncgt/ │ ├── binary/ # ELF parsing, call graphs, and ground truth │ ├── data/ # BinFuncGT schemas and instance I/O │ ├── features/ # Static features and vectorization │ ├── retrieval/ # Candidate function retrieval │ ├── matching/ # Matching instances and objectives │ ├── reduction/ # Safe and heuristic reductions │ ├── solvers/ # Classical optimization methods │ ├── scoring/ # Downstream structural scores │ ├── evaluation/ # Evaluation metrics │ └── feasibility/ # Classical matching and solver statistics └── requirements.txt # Pinned Python dependencies </pre></pre><h2 dir="ltr">Configuration</h2>The principal benchmark configurations are:<table><tr><th>Configuration</th><th>Experiment setting</th></tr><tr><td><code>configs/binkit_x86_v2.yaml</code></td><td>Same-architecture matching across build variants</td></tr><tr><td><code>configs/binkit_xarch_v2.yaml</code></td><td>Cross-architecture matching with the same compiler and optimization level</td></tr><tr><td><code>configs/pilot_versiongrid_v2.yaml</code></td><td>Component and version comparisons on VersionGrid v2</td></tr><tr><td><code>configs/pilot_versiongrid_v3.yaml</code></td><td>Extended version-grid comparisons</td></tr></table>The <code>x86</code> configuration name is historical: its pairing rule is <code>arch_relation: same</code>, covering matching architectures represented in the input corpus.Common settings include:<table><tr><th>Parameter</th><th>Main protocol setting</th></tr><tr><td>Candidate count per target function</td><td><code>r = 5</code></td></tr><tr><td>Cosine-similarity threshold</td><td><code>0.5</code></td></tr><tr><td>Same-architecture feature set</td><td><code>full</code></td></tr><tr><td>Cross-architecture feature set</td><td><code>structural</code></td></tr><tr><td>Matching objective weights</td><td><code>alpha = beta = 1</code></td></tr><tr><td>CP-SAT time budget</td><td>60 seconds by default in the main scoring runners</td></tr><tr><td>Structural score weights</td><td><code>(0.5, 0.3, 0.2)</code></td></tr></table><h2 dir="ltr">Experiment Scripts</h2><table><tr><th>Script</th><th>Purpose</th></tr><tr><td><code>scripts/rq1_function_gt.py</code></td><td>Function-level matching evaluation</td></tr><tr><td><code>scripts/rq1_function_gt_report.py</code></td><td>Function-level result summaries and paired comparisons</td></tr><tr><td><code>scripts/run_feasibility.py</code></td><td>Structural matching and per-instance feasibility statistics</td></tr><tr><td><code>scripts/analyze_results.py</code></td><td>Pilot result aggregation</td></tr><tr><td><code>scripts/score_v2.py</code></td><td>Multi-method benchmark scoring</td></tr><tr><td><code>scripts/report_v2.py</code></td><td>Scoring-cache summaries and statistical comparisons</td></tr><tr><td><code>scripts/run_baselines.py</code></td><td>Classical baselines through <code>run</code> and <code>summarize</code> subcommands</td></tr><tr><td><code>scripts/rq5_feature_attribution.py</code></td><td>Feature attribution analysis</td></tr><tr><td><code>scripts/rq6_stripped.py</code></td><td>Evaluation with stripped target binaries</td></tr><tr><td><code>scripts/rq7_diff_weighted.py</code></td><td>Differential feature-weighting experiments</td></tr><tr><td><code>scripts/rq7_tie_audit.py</code></td><td>Tie and candidate-set diagnostics</td></tr><tr><td><code>scripts/rq7_str_scorer.py</code></td><td>Version-discriminative string scoring</td></tr><tr><td><code>scripts/rq7_stats.py</code></td><td>Statistical comparisons of string-based methods</td></tr><tr><td><code>scripts/fdr_correction.py</code></td><td>Benjamini–Hochberg FDR correction</td></tr><tr><td><code>scripts/debian_case_study.py</code></td><td>Debian case-study evaluation</td></tr><tr><td><code>scripts/debian_str_idf.py</code></td><td>String-IDF evaluation on Debian binaries</td></tr></table> 2026-10-01T06:21:03Z Dataset Dataset 10.6084/m9.figshare.34040505.v1 https://figshare.com/articles/dataset/BinFuncGt/34040505 CC BY 4.0