{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:G45TWNPQYZLL6AAGJZ3W3YW3D7","short_pith_number":"pith:G45TWNPQ","schema_version":"1.0","canonical_sha256":"373b3b35f0c656bf00064e776de2db1fee48e1f20689f440b069f3fcdb00f957","source":{"kind":"arxiv","id":"2310.08837","version":1},"attestation_state":"computed","paper":{"title":"Static Code Analysis in the AI Era: An In-depth Exploration of the Concept, Function, and Potential of Intelligent Code Analysis Agents","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"Gang Fan, Peng Di, Xiaoheng Xie, Xunjin Zheng, Yinan Liang","submitted_at":"2023-10-13T03:16:58Z","abstract_excerpt":"The escalating complexity of software systems and accelerating development cycles pose a significant challenge in managing code errors and implementing business logic. Traditional techniques, while cornerstone for software quality assurance, exhibit limitations in handling intricate business logic and extensive codebases. To address these challenges, we introduce the Intelligent Code Analysis Agent (ICAA), a novel concept combining AI models, engineering process designs, and traditional non-AI components. The ICAA employs the capabilities of large language models (LLMs) such as GPT-3 or GPT-4 "},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2310.08837","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.SE","submitted_at":"2023-10-13T03:16:58Z","cross_cats_sorted":[],"title_canon_sha256":"a3bd05876548d3f54bb42db1100be366eb9877e7b2dbaf45496d1b0a660766cf","abstract_canon_sha256":"f54391a8ae3e592313e12bd1703a7342087a0a5b56a9008a1aff2398dba2a5e5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:00:35.590156Z","signature_b64":"Ya9Li+oZlFtLKETnd1O7BdYlWBsbwQx3yXCmjjy4YBdJHHqGhHl0T675OkshnOxzPSI75I37B0Ug9Xl1Xu4wCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"373b3b35f0c656bf00064e776de2db1fee48e1f20689f440b069f3fcdb00f957","last_reissued_at":"2026-07-05T07:00:35.589727Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:00:35.589727Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Static Code Analysis in the AI Era: An In-depth Exploration of the Concept, Function, and Potential of Intelligent Code Analysis Agents","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"Gang Fan, Peng Di, Xiaoheng Xie, Xunjin Zheng, Yinan Liang","submitted_at":"2023-10-13T03:16:58Z","abstract_excerpt":"The escalating complexity of software systems and accelerating development cycles pose a significant challenge in managing code errors and implementing business logic. Traditional techniques, while cornerstone for software quality assurance, exhibit limitations in handling intricate business logic and extensive codebases. To address these challenges, we introduce the Intelligent Code Analysis Agent (ICAA), a novel concept combining AI models, engineering process designs, and traditional non-AI components. The ICAA employs the capabilities of large language models (LLMs) such as GPT-3 or GPT-4 "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.08837","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2310.08837/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2310.08837","created_at":"2026-07-05T07:00:35.589784+00:00"},{"alias_kind":"arxiv_version","alias_value":"2310.08837v1","created_at":"2026-07-05T07:00:35.589784+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.08837","created_at":"2026-07-05T07:00:35.589784+00:00"},{"alias_kind":"pith_short_12","alias_value":"G45TWNPQYZLL","created_at":"2026-07-05T07:00:35.589784+00:00"},{"alias_kind":"pith_short_16","alias_value":"G45TWNPQYZLL6AAG","created_at":"2026-07-05T07:00:35.589784+00:00"},{"alias_kind":"pith_short_8","alias_value":"G45TWNPQ","created_at":"2026-07-05T07:00:35.589784+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2409.02977","citing_title":"Large Language Model-Based Agents for Software Engineering: A Survey","ref_index":137,"is_internal_anchor":false},{"citing_arxiv_id":"2604.15390","citing_title":"Analyzing Chain of Thought (CoT) Approaches in Control Flow Code Deobfuscation Tasks","ref_index":13,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/G45TWNPQYZLL6AAGJZ3W3YW3D7","json":"https://pith.science/pith/G45TWNPQYZLL6AAGJZ3W3YW3D7.json","graph_json":"https://pith.science/api/pith-number/G45TWNPQYZLL6AAGJZ3W3YW3D7/graph.json","events_json":"https://pith.science/api/pith-number/G45TWNPQYZLL6AAGJZ3W3YW3D7/events.json","paper":"https://pith.science/paper/G45TWNPQ"},"agent_actions":{"view_html":"https://pith.science/pith/G45TWNPQYZLL6AAGJZ3W3YW3D7","download_json":"https://pith.science/pith/G45TWNPQYZLL6AAGJZ3W3YW3D7.json","view_paper":"https://pith.science/paper/G45TWNPQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2310.08837&json=true","fetch_graph":"https://pith.science/api/pith-number/G45TWNPQYZLL6AAGJZ3W3YW3D7/graph.json","fetch_events":"https://pith.science/api/pith-number/G45TWNPQYZLL6AAGJZ3W3YW3D7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/G45TWNPQYZLL6AAGJZ3W3YW3D7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/G45TWNPQYZLL6AAGJZ3W3YW3D7/action/storage_attestation","attest_author":"https://pith.science/pith/G45TWNPQYZLL6AAGJZ3W3YW3D7/action/author_attestation","sign_citation":"https://pith.science/pith/G45TWNPQYZLL6AAGJZ3W3YW3D7/action/citation_signature","submit_replication":"https://pith.science/pith/G45TWNPQYZLL6AAGJZ3W3YW3D7/action/replication_record"}},"created_at":"2026-07-05T07:00:35.589784+00:00","updated_at":"2026-07-05T07:00:35.589784+00:00"}