{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:VXLIG4OI5SAJJF6KWQPUOMIBEC","short_pith_number":"pith:VXLIG4OI","schema_version":"1.0","canonical_sha256":"add68371c8ec809497cab41f47310120b74b597afb152d8845a9c516dfa032a1","source":{"kind":"arxiv","id":"2506.00520","version":1},"attestation_state":"computed","paper":{"title":"Temac: Multi-Agent Collaboration for Automated Web GUI Testing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"Chenxu Liu, Guoquan Wu, Jun Wei, Tao Xie, Ying Zhang, Zhiyu Gu","submitted_at":"2025-05-31T11:43:37Z","abstract_excerpt":"Quality assurance of web applications is critical, as web applications play an essential role in people's daily lives. To reduce labor costs, automated web GUI testing (AWGT) is widely adopted, exploring web applications via GUI actions such as clicks and text inputs. However, these approaches face limitations in generating continuous and meaningful action sequences capable of covering complex functionalities. Recent work incorporates large language models (LLMs) for GUI testing. However, these approaches face various challenges, including low efficiency of LLMs, high complexity of rich web ap"},"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":"2506.00520","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SE","submitted_at":"2025-05-31T11:43:37Z","cross_cats_sorted":[],"title_canon_sha256":"330d9b6edadd4befecfc21d936ce14f843b469a0d99771b0b466b9a5975809aa","abstract_canon_sha256":"ac19c3436f7b3831ce7e860fb0fc2cabd3f3d5a9628d48c54e79064dcb84662b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:13:36.391767Z","signature_b64":"ThWpNQ0RNTE5smL0TDE65aea/lOFuxRvDxBL4XC/vKdurxEjHc93vbn+mpjIi7IYuftHLCxoZNDH6CORcJYJCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"add68371c8ec809497cab41f47310120b74b597afb152d8845a9c516dfa032a1","last_reissued_at":"2026-07-05T11:13:36.391199Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:13:36.391199Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Temac: Multi-Agent Collaboration for Automated Web GUI Testing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"Chenxu Liu, Guoquan Wu, Jun Wei, Tao Xie, Ying Zhang, Zhiyu Gu","submitted_at":"2025-05-31T11:43:37Z","abstract_excerpt":"Quality assurance of web applications is critical, as web applications play an essential role in people's daily lives. To reduce labor costs, automated web GUI testing (AWGT) is widely adopted, exploring web applications via GUI actions such as clicks and text inputs. However, these approaches face limitations in generating continuous and meaningful action sequences capable of covering complex functionalities. Recent work incorporates large language models (LLMs) for GUI testing. However, these approaches face various challenges, including low efficiency of LLMs, high complexity of rich web ap"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.00520","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/2506.00520/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":"2506.00520","created_at":"2026-07-05T11:13:36.391263+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.00520v1","created_at":"2026-07-05T11:13:36.391263+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.00520","created_at":"2026-07-05T11:13:36.391263+00:00"},{"alias_kind":"pith_short_12","alias_value":"VXLIG4OI5SAJ","created_at":"2026-07-05T11:13:36.391263+00:00"},{"alias_kind":"pith_short_16","alias_value":"VXLIG4OI5SAJJF6K","created_at":"2026-07-05T11:13:36.391263+00:00"},{"alias_kind":"pith_short_8","alias_value":"VXLIG4OI","created_at":"2026-07-05T11:13:36.391263+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.20373","citing_title":"AutoPass: Evidence-Guided LLM Agents for Compiler Performance Tuning","ref_index":32,"is_internal_anchor":false},{"citing_arxiv_id":"2602.11724","citing_title":"WebTestPilot: Agentic End-to-End Web Testing against Natural Language Specification by Inferring Oracles with Symbolized GUI Elements","ref_index":34,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/VXLIG4OI5SAJJF6KWQPUOMIBEC","json":"https://pith.science/pith/VXLIG4OI5SAJJF6KWQPUOMIBEC.json","graph_json":"https://pith.science/api/pith-number/VXLIG4OI5SAJJF6KWQPUOMIBEC/graph.json","events_json":"https://pith.science/api/pith-number/VXLIG4OI5SAJJF6KWQPUOMIBEC/events.json","paper":"https://pith.science/paper/VXLIG4OI"},"agent_actions":{"view_html":"https://pith.science/pith/VXLIG4OI5SAJJF6KWQPUOMIBEC","download_json":"https://pith.science/pith/VXLIG4OI5SAJJF6KWQPUOMIBEC.json","view_paper":"https://pith.science/paper/VXLIG4OI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.00520&json=true","fetch_graph":"https://pith.science/api/pith-number/VXLIG4OI5SAJJF6KWQPUOMIBEC/graph.json","fetch_events":"https://pith.science/api/pith-number/VXLIG4OI5SAJJF6KWQPUOMIBEC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VXLIG4OI5SAJJF6KWQPUOMIBEC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VXLIG4OI5SAJJF6KWQPUOMIBEC/action/storage_attestation","attest_author":"https://pith.science/pith/VXLIG4OI5SAJJF6KWQPUOMIBEC/action/author_attestation","sign_citation":"https://pith.science/pith/VXLIG4OI5SAJJF6KWQPUOMIBEC/action/citation_signature","submit_replication":"https://pith.science/pith/VXLIG4OI5SAJJF6KWQPUOMIBEC/action/replication_record"}},"created_at":"2026-07-05T11:13:36.391263+00:00","updated_at":"2026-07-05T11:13:36.391263+00:00"}