{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:ONRDOB345KFXIPZDCKNAK3YQJK","short_pith_number":"pith:ONRDOB34","canonical_record":{"source":{"id":"2409.02977","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.SE","submitted_at":"2024-09-04T15:59:41Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"810cc8f9984bd735b2c41c993d66cced9a41c4e023da8047ac40bb7015218286","abstract_canon_sha256":"5587aa4f1c01310a73ea5f4115aa53ba7ae2409d631937337c93ca25217bf94a"},"schema_version":"1.0"},"canonical_sha256":"736237077cea8b743f23129a056f104a9f2e003b65d320fa47932fcb3a40c620","source":{"kind":"arxiv","id":"2409.02977","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2409.02977","created_at":"2026-05-17T23:38:14Z"},{"alias_kind":"arxiv_version","alias_value":"2409.02977v2","created_at":"2026-05-17T23:38:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.02977","created_at":"2026-05-17T23:38:14Z"},{"alias_kind":"pith_short_12","alias_value":"ONRDOB345KFX","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"ONRDOB345KFXIPZD","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"ONRDOB34","created_at":"2026-05-18T12:33:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:ONRDOB345KFXIPZDCKNAK3YQJK","target":"record","payload":{"canonical_record":{"source":{"id":"2409.02977","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.SE","submitted_at":"2024-09-04T15:59:41Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"810cc8f9984bd735b2c41c993d66cced9a41c4e023da8047ac40bb7015218286","abstract_canon_sha256":"5587aa4f1c01310a73ea5f4115aa53ba7ae2409d631937337c93ca25217bf94a"},"schema_version":"1.0"},"canonical_sha256":"736237077cea8b743f23129a056f104a9f2e003b65d320fa47932fcb3a40c620","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:38:14.097799Z","signature_b64":"4d1J3OSDNB4naHqKe4UiRxfBvPBAVA4KAKFDakXUNfioNtZkuAL6mVa+8FRDE/YCD6Jk8neaj+1DPQaKXnBGDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"736237077cea8b743f23129a056f104a9f2e003b65d320fa47932fcb3a40c620","last_reissued_at":"2026-05-17T23:38:14.096973Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:38:14.096973Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2409.02977","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:38:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"IOep46nXwCoD6nTuajFhUBD14nvlormJOO3y0GntfMklYtvK9VSIOH6nVrKyr0QWCWJsV7xpm6TW3lMVRFvyCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-18T17:40:01.362604Z"},"content_sha256":"f046eb53f6170394284da96a98a76ccccd067f916949cbfdd075a6be321623cc","schema_version":"1.0","event_id":"sha256:f046eb53f6170394284da96a98a76ccccd067f916949cbfdd075a6be321623cc"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:ONRDOB345KFXIPZDCKNAK3YQJK","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Large Language Model-Based Agents for Software Engineering: A Survey","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"This survey gathers 124 papers on LLM-based agents for software engineering and sorts them by software engineering tasks and agent structures.","cross_cats":["cs.AI"],"primary_cat":"cs.SE","authors_text":"Junwei Liu, Kaixin Wang, Lingming Zhang, Xin Peng, Yiling Lou, Yixuan Chen, Zhenpeng Chen","submitted_at":"2024-09-04T15:59:41Z","abstract_excerpt":"The recent advance in Large Language Models (LLMs) has shaped a new paradigm of AI agents, i.e., LLM-based agents. Compared to standalone LLMs, LLM-based agents substantially extend the versatility and expertise of LLMs by enhancing LLMs with the capabilities of perceiving and utilizing external resources and tools. To date, LLM-based agents have been applied and shown remarkable effectiveness in Software Engineering (SE). The synergy between multiple agents and human interaction brings further promise in tackling complex real-world SE problems. In this work, we present a comprehensive and sys"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"In this work, we present a comprehensive and systematic survey on LLM-based agents for SE. We collect 124 papers and categorize them from two perspectives, i.e., the SE and agent perspectives.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That the 124 collected papers form a representative sample of the field and that the chosen two-perspective categorization sufficiently captures the essential distinctions without significant omissions or overlaps.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A literature survey that collects and categorizes 124 papers on LLM-based agents for software engineering from SE and agent perspectives.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"This survey gathers 124 papers on LLM-based agents for software engineering and sorts them by software engineering tasks and agent structures.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"53b3f8c5623cfae7b6476b2b92b5d18189f3939779fd8dfca349682d05e7c3cf"},"source":{"id":"2409.02977","kind":"arxiv","version":2},"verdict":{"id":"79d7de66-cada-4743-8a8e-010bef72ded0","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-17T12:29:16.894990Z","strongest_claim":"In this work, we present a comprehensive and systematic survey on LLM-based agents for SE. We collect 124 papers and categorize them from two perspectives, i.e., the SE and agent perspectives.","one_line_summary":"A literature survey that collects and categorizes 124 papers on LLM-based agents for software engineering from SE and agent perspectives.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That the 124 collected papers form a representative sample of the field and that the chosen two-perspective categorization sufficiently captures the essential distinctions without significant omissions or overlaps.","pith_extraction_headline":"This survey gathers 124 papers on LLM-based agents for software engineering and sorts them by software engineering tasks and agent structures."},"references":{"count":290,"sample":[{"doi":"","year":2023,"title":"A Survey of Large Language Models","work_id":"de1b42b5-4a0a-4b1f-8c78-1f7fe21be6c9","ref_index":1,"cited_arxiv_id":"2303.18223","is_internal_anchor":true},{"doi":"","year":2024,"title":"Large language models for software engineering: A systematic literature review.ACM Trans","work_id":"fb5a7f01-49be-49cd-8c74-aed637f9fd21","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2023,"title":"Angela Fan, Beliz Gokkaya, Mark Harman, Mitya Lyubarskiy, Shubho Sengupta, Shin Yoo, and Jie M. Zhang. Large language models for software engineering: Survey and open problems. In IEEE/ACM Internation","work_id":"6885f599-0a75-4049-8519-2a71e1a0cb62","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2024,"title":"Self-collaboration code generation via chatgpt.ACM Trans","work_id":"0a065ebc-e2ce-4a79-b357-213ab84e149d","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2023,"title":"Evaluating the code quality of AI-assisted code generation tools: An empirical study on GitHub Copilot, Amazon Code- Whisperer, and ChatGPT","work_id":"fc34d942-a4d6-4670-b39a-c6c5537410cb","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":290,"snapshot_sha256":"d4a5dc8db59d6151a753622e7347dbbced6388b63627dfe8c9fecc24a8c34274","internal_anchors":13},"formal_canon":{"evidence_count":2,"snapshot_sha256":"66813ca420250f7051eec0b020f42d8005effaf39afdcd91828fa364dd848f2a"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"79d7de66-cada-4743-8a8e-010bef72ded0"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:38:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5cy/s9UjTAGwQsJR5ziPjlG8czyFa+MYr1JxHccUl0d+C83sfj9O289LDwdba5eztsQ5DiMbQEY1ErmZ4AYpCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-18T17:40:01.363572Z"},"content_sha256":"c5173a38003587b5aa20cec5b9b8982f472c794e9a7f8a087bbd25bd6fcba41f","schema_version":"1.0","event_id":"sha256:c5173a38003587b5aa20cec5b9b8982f472c794e9a7f8a087bbd25bd6fcba41f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ONRDOB345KFXIPZDCKNAK3YQJK/bundle.json","state_url":"https://pith.science/pith/ONRDOB345KFXIPZDCKNAK3YQJK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ONRDOB345KFXIPZDCKNAK3YQJK/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-18T17:40:01Z","links":{"resolver":"https://pith.science/pith/ONRDOB345KFXIPZDCKNAK3YQJK","bundle":"https://pith.science/pith/ONRDOB345KFXIPZDCKNAK3YQJK/bundle.json","state":"https://pith.science/pith/ONRDOB345KFXIPZDCKNAK3YQJK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ONRDOB345KFXIPZDCKNAK3YQJK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:ONRDOB345KFXIPZDCKNAK3YQJK","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"5587aa4f1c01310a73ea5f4115aa53ba7ae2409d631937337c93ca25217bf94a","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.SE","submitted_at":"2024-09-04T15:59:41Z","title_canon_sha256":"810cc8f9984bd735b2c41c993d66cced9a41c4e023da8047ac40bb7015218286"},"schema_version":"1.0","source":{"id":"2409.02977","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2409.02977","created_at":"2026-05-17T23:38:14Z"},{"alias_kind":"arxiv_version","alias_value":"2409.02977v2","created_at":"2026-05-17T23:38:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.02977","created_at":"2026-05-17T23:38:14Z"},{"alias_kind":"pith_short_12","alias_value":"ONRDOB345KFX","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"ONRDOB345KFXIPZD","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"ONRDOB34","created_at":"2026-05-18T12:33:37Z"}],"graph_snapshots":[{"event_id":"sha256:c5173a38003587b5aa20cec5b9b8982f472c794e9a7f8a087bbd25bd6fcba41f","target":"graph","created_at":"2026-05-17T23:38:14Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"In this work, we present a comprehensive and systematic survey on LLM-based agents for SE. We collect 124 papers and categorize them from two perspectives, i.e., the SE and agent perspectives."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That the 124 collected papers form a representative sample of the field and that the chosen two-perspective categorization sufficiently captures the essential distinctions without significant omissions or overlaps."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"A literature survey that collects and categorizes 124 papers on LLM-based agents for software engineering from SE and agent perspectives."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"This survey gathers 124 papers on LLM-based agents for software engineering and sorts them by software engineering tasks and agent structures."}],"snapshot_sha256":"53b3f8c5623cfae7b6476b2b92b5d18189f3939779fd8dfca349682d05e7c3cf"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"66813ca420250f7051eec0b020f42d8005effaf39afdcd91828fa364dd848f2a"},"paper":{"abstract_excerpt":"The recent advance in Large Language Models (LLMs) has shaped a new paradigm of AI agents, i.e., LLM-based agents. Compared to standalone LLMs, LLM-based agents substantially extend the versatility and expertise of LLMs by enhancing LLMs with the capabilities of perceiving and utilizing external resources and tools. To date, LLM-based agents have been applied and shown remarkable effectiveness in Software Engineering (SE). The synergy between multiple agents and human interaction brings further promise in tackling complex real-world SE problems. In this work, we present a comprehensive and sys","authors_text":"Junwei Liu, Kaixin Wang, Lingming Zhang, Xin Peng, Yiling Lou, Yixuan Chen, Zhenpeng Chen","cross_cats":["cs.AI"],"headline":"This survey gathers 124 papers on LLM-based agents for software engineering and sorts them by software engineering tasks and agent structures.","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.SE","submitted_at":"2024-09-04T15:59:41Z","title":"Large Language Model-Based Agents for Software Engineering: A Survey"},"references":{"count":290,"internal_anchors":13,"resolved_work":290,"sample":[{"cited_arxiv_id":"2303.18223","doi":"","is_internal_anchor":true,"ref_index":1,"title":"A Survey of Large Language Models","work_id":"de1b42b5-4a0a-4b1f-8c78-1f7fe21be6c9","year":2023},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"Large language models for software engineering: A systematic literature review.ACM Trans","work_id":"fb5a7f01-49be-49cd-8c74-aed637f9fd21","year":2024},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"Angela Fan, Beliz Gokkaya, Mark Harman, Mitya Lyubarskiy, Shubho Sengupta, Shin Yoo, and Jie M. Zhang. Large language models for software engineering: Survey and open problems. In IEEE/ACM Internation","work_id":"6885f599-0a75-4049-8519-2a71e1a0cb62","year":2023},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"Self-collaboration code generation via chatgpt.ACM Trans","work_id":"0a065ebc-e2ce-4a79-b357-213ab84e149d","year":2024},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"Evaluating the code quality of AI-assisted code generation tools: An empirical study on GitHub Copilot, Amazon Code- Whisperer, and ChatGPT","work_id":"fc34d942-a4d6-4670-b39a-c6c5537410cb","year":2023}],"snapshot_sha256":"d4a5dc8db59d6151a753622e7347dbbced6388b63627dfe8c9fecc24a8c34274"},"source":{"id":"2409.02977","kind":"arxiv","version":2},"verdict":{"created_at":"2026-05-17T12:29:16.894990Z","id":"79d7de66-cada-4743-8a8e-010bef72ded0","model_set":{"reader":"grok-4.3"},"one_line_summary":"A literature survey that collects and categorizes 124 papers on LLM-based agents for software engineering from SE and agent perspectives.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"This survey gathers 124 papers on LLM-based agents for software engineering and sorts them by software engineering tasks and agent structures.","strongest_claim":"In this work, we present a comprehensive and systematic survey on LLM-based agents for SE. We collect 124 papers and categorize them from two perspectives, i.e., the SE and agent perspectives.","weakest_assumption":"That the 124 collected papers form a representative sample of the field and that the chosen two-perspective categorization sufficiently captures the essential distinctions without significant omissions or overlaps."}},"verdict_id":"79d7de66-cada-4743-8a8e-010bef72ded0"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:f046eb53f6170394284da96a98a76ccccd067f916949cbfdd075a6be321623cc","target":"record","created_at":"2026-05-17T23:38:14Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"5587aa4f1c01310a73ea5f4115aa53ba7ae2409d631937337c93ca25217bf94a","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.SE","submitted_at":"2024-09-04T15:59:41Z","title_canon_sha256":"810cc8f9984bd735b2c41c993d66cced9a41c4e023da8047ac40bb7015218286"},"schema_version":"1.0","source":{"id":"2409.02977","kind":"arxiv","version":2}},"canonical_sha256":"736237077cea8b743f23129a056f104a9f2e003b65d320fa47932fcb3a40c620","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"736237077cea8b743f23129a056f104a9f2e003b65d320fa47932fcb3a40c620","first_computed_at":"2026-05-17T23:38:14.096973Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:38:14.096973Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"4d1J3OSDNB4naHqKe4UiRxfBvPBAVA4KAKFDakXUNfioNtZkuAL6mVa+8FRDE/YCD6Jk8neaj+1DPQaKXnBGDw==","signature_status":"signed_v1","signed_at":"2026-05-17T23:38:14.097799Z","signed_message":"canonical_sha256_bytes"},"source_id":"2409.02977","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f046eb53f6170394284da96a98a76ccccd067f916949cbfdd075a6be321623cc","sha256:c5173a38003587b5aa20cec5b9b8982f472c794e9a7f8a087bbd25bd6fcba41f"],"state_sha256":"73c6b0c8ed5903665db9b624b5614ef9b1b5781170c08d8346fb26a01979d0ad"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"62+E241NjDBmBtFL9ye81fcNHvLOox8dYaOSjZ8kcg9BUMVauTcb/qRvlDc/AnyBIi4kTtKrPAkYobWwfoGzBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-18T17:40:01.365868Z","bundle_sha256":"ff2369fe1124007abf0580401d70d3c85e64344c66e9c112524db4d9f53bcf84"}}