Executable Code Knowledge: Code as a Native, Validation-Carrying Knowledge Representation for AI Coding Agents
2026-08-17 • Computation and Language
Computation and Language
AI summaryⓘ
The authors propose a new way to help AI coding assistants better understand and validate code by embedding detailed, executable knowledge directly within code pieces, called Executable Code Knowledge Units (ECKUs). Their Python prototype can check if the code is fresh, linked to tests, and show exactly which parts changes affect. In experiments, using ECK helped recover precise test coverage and change impacts better than other methods like static rules or hiding evidence. The authors suggest combining retrieval methods for broad coverage with ECK for accurate source validation and projections for presenting information to AI agents.
Executable Code KnowledgeAI coding agentscode validationtest coveragechange impact analysisPython prototypesource provenancestatic rulesAST fingerprintsprojections
Authors
Xueping Gao
Abstract
AI coding agents need more than relevant snippets: they need business semantics, validation evidence, relations, and assurance that their context is current. Existing systems usually infer or externalize this knowledge through retrieval, summaries, graphs, rules, or reverse specifications. We investigate a complementary representation in which selected code units directly carry agent-usable knowledge. We introduce Executable Code Knowledge (ECK) and define an Executable Code Knowledge Unit (ECKU) as a source-bound object combining stable identity, semantics, executable behavior, contracts, evidence, relations, provenance, validation state, and a query interface. Our Python prototype supports code-local authoring, manifest export, evidence execution, exact changed-line impact, freshness checking, and agent-facing projections. Across three real Python repositories and 26 controlled patch tasks, direct ECK provides executable test coverage for 11/11 evidence-bearing tasks and exact selectors for 9/11; hiding declared evidence reduces exact recovery to 1/11 (paired exact McNemar p=0.0078). ECK-derived rules recover 11/11 exact selectors, showing that rules are effective delivery artifacts while ECK supplies source binding, validation state, impact, and freshness. Exact changed-line impact matches independently authored labels on all 26 patches (12 unit links; precision, recall, and F1 all 1.000). AST-bounded fingerprints classify 50 positive changes and 17 unrelated same-file controls correctly, whereas static rules snapshots detect none of the 50 stale cases. Model-backed patch-review and cross-layer studies measure projection fidelity rather than independent impact discovery. These results support a hybrid architecture: retrieval for coverage, ECK for source and evidence governance, and projections for delivery.