{"as_of":"2026-08-17T07:34:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:0ba4342279b8aa26bcbb579c484a595e0044767b6685b0bd01c252cdbb4ac72c","coverage":[{"denominator":32,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":32,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T20:53:54.938567Z","state":"measured"},{"denominator":33,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":33,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-17T06:30:58.91139+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T23:41:44.896555Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"pith","source_observed_at":"2026-08-10T05:30:23.456663Z","state":"measured"}],"external_citation_measurements":[{"count":0,"observed_at":"2026-08-10T05:30:23.456663Z","source":"pith"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2501.06837","last_updated":"2025-01-12T15:10:57Z","snapshot_observed_at":"2026-08-12T19:57:25.048112Z","submitted_at":"2025-01-12T15:10:57Z","title":"An efficient approach to represent enterprise web application structure using Large Language Model in the service of Intelligent Quality Engineering","version":1},"cited_work":{"arxiv_id":"2501.06837","doi":"10.48550/arxiv.2501.06837","metadata_source":"pith","pith_arxiv_id":"2501.06837","snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"An efficient approach to represent enterprise web application structure using Large Language Model in the service of Intelligent Quality Engineering","venue":"cs.AI","work_id":"5d39f1c4-b2b9-4e29-8994-4fe63bf5fae6","year":2025},"citing_paper":{"arxiv_id":"2506.16586","last_updated":"2025-06-19T20:22:47Z","snapshot_observed_at":"2026-08-06T23:35:04.524752Z","submitted_at":"2025-06-19T20:22:47Z","title":"AI-Driven Tools in Modern Software Quality Assurance: An Assessment of Benefits, Challenges, and Future Directions","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T23:41:44.896555Z"},"links":{"cited_paper":"/paper/2501.06837","citing_paper":"/paper/2506.16586"},"observation_digest":"sha256:6ff0f4d4c330828ab2b15145d4b80848b9f9ac70602f2ae813758a1ee5022554","observation_id":"721e627e-7981-4793-8f3c-776a7b4b92af","resolution":{"observed_at":"2026-08-06T23:41:46.899446Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2501.06837/citation-record","integrity":"/paper/2501.06837/integrity","json":"/paper/2501.06837/citation-record.json","paper":"/paper/2501.06837"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:53:55.445065Z","title":"Quality in web engineering","venue":null,"work_id":"fd0fc7da-27b7-479b-bc84-143d0a1db9ff","year":2010},"citing_paper":{"arxiv_id":"2501.06837","last_updated":"2025-01-12T15:10:57Z","snapshot_observed_at":"2026-08-12T19:57:25.048112Z","submitted_at":"2025-01-12T15:10:57Z","title":"An efficient approach to represent enterprise web application structure using Large Language Model in the service of Intelligent Quality Engineering","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-10T20:53:54.774287Z"},"links":{"citing_paper":"/paper/2501.06837"},"observation_digest":"sha256:0298e1e4281d19c18081dac17f49f5c8f0015c93b5ec9423f5a89fe23d8bcf34","observation_id":"8545df2b-5a67-4330-b3f5-114f1a15195b","resolution":{"observed_at":"2026-08-10T20:53:55.449840Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:53:55.431391Z","title":"Applications of automated model’s extraction in enterprise systems","venue":null,"work_id":"4c56f9ed-3d16-4d90-82ab-629b35b9cd07","year":2019},"citing_paper":{"arxiv_id":"2501.06837","last_updated":"2025-01-12T15:10:57Z","snapshot_observed_at":"2026-08-12T19:57:25.048112Z","submitted_at":"2025-01-12T15:10:57Z","title":"An efficient approach to represent enterprise web application structure using Large Language Model in the service of Intelligent Quality Engineering","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-10T20:53:54.779828Z"},"links":{"citing_paper":"/paper/2501.06837"},"observation_digest":"sha256:57bc06825918b9971418d73892faa8fa2913321537e944f6199962874003035b","observation_id":"10f5080d-d628-4f7d-b201-f806b9591156","resolution":{"observed_at":"2026-08-10T20:53:55.436247Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:53:55.415702Z","title":"Revolutionizing software testing: The impact of AI, ML, and IoT","venue":null,"work_id":"5f063560-d7c6-4602-ad96-a5b2d680220a","year":2023},"citing_paper":{"arxiv_id":"2501.06837","last_updated":"2025-01-12T15:10:57Z","snapshot_observed_at":"2026-08-12T19:57:25.048112Z","submitted_at":"2025-01-12T15:10:57Z","title":"An efficient approach to represent enterprise web application structure using Large Language Model in the service of Intelligent Quality Engineering","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-10T20:53:54.785212Z"},"links":{"citing_paper":"/paper/2501.06837"},"observation_digest":"sha256:c87153313db6b1e9d0ae85ccdef36cbf834d982c9f44586e373038e3e3afbd9d","observation_id":"80d50939-7f88-447d-ab1a-a529c8b081d1","resolution":{"observed_at":"2026-08-10T20:53:55.421575Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:53:55.403732Z","title":"Artificial intelligence in software testing.International Journal of Innovative Science and Research Technology, pages 616–619, 2024","venue":null,"work_id":"6fdbfa36-7ca1-431d-a814-d9409e9be8e1","year":2024},"citing_paper":{"arxiv_id":"2501.06837","last_updated":"2025-01-12T15:10:57Z","snapshot_observed_at":"2026-08-12T19:57:25.048112Z","submitted_at":"2025-01-12T15:10:57Z","title":"An efficient approach to represent enterprise web application structure using Large Language Model in the service of Intelligent Quality Engineering","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-10T20:53:54.789753Z"},"links":{"citing_paper":"/paper/2501.06837"},"observation_digest":"sha256:2c6d7e353c6df931cc648c2048d836393ce2ce4324b1dd977f6a9c7c6e6630f2","observation_id":"0152a90b-4504-4fd4-b8ab-a2794a65b445","resolution":{"observed_at":"2026-08-10T20:53:55.407518Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:53:55.391496Z","title":"An empirical study to compare three web test automation approaches: NLP-based, programmable, and capture&replay","venue":null,"work_id":"0f5160ab-8d78-47d5-9917-87baef6fe1bd","year":2024},"citing_paper":{"arxiv_id":"2501.06837","last_updated":"2025-01-12T15:10:57Z","snapshot_observed_at":"2026-08-12T19:57:25.048112Z","submitted_at":"2025-01-12T15:10:57Z","title":"An efficient approach to represent enterprise web application structure using Large Language Model in the service of Intelligent Quality Engineering","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-10T20:53:54.794818Z"},"links":{"citing_paper":"/paper/2501.06837"},"observation_digest":"sha256:9627058b02b49f91b3c73f1d6243273585e3dcb9eae4a016f4e0ee6deb60b4aa","observation_id":"156785d4-7928-40f0-97a0-f80e3269d37e","resolution":{"observed_at":"2026-08-10T20:53:55.395648Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:53:55.379625Z","title":"AI-powered software testing: The impact of large language models on testing methodologies","venue":null,"work_id":"9dccd7f7-5a7a-437a-a1e6-a96807d553a5","year":2023},"citing_paper":{"arxiv_id":"2501.06837","last_updated":"2025-01-12T15:10:57Z","snapshot_observed_at":"2026-08-12T19:57:25.048112Z","submitted_at":"2025-01-12T15:10:57Z","title":"An efficient approach to represent enterprise web application structure using Large Language Model in the service of Intelligent Quality Engineering","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-10T20:53:54.799311Z"},"links":{"citing_paper":"/paper/2501.06837"},"observation_digest":"sha256:321a537ddde76c5eb7a1cbf2d878bf2b7943ee7fa4401900df1abb319e1c30b4","observation_id":"c5548010-a699-4611-ab02-86a3f4f769e0","resolution":{"observed_at":"2026-08-10T20:53:55.383984Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:53:55.366975Z","title":"Software testing in the era of AI: Leveraging machine learning and automation for efficient quality assurance","venue":null,"work_id":"7f2b1769-b5be-4294-8a03-c99c12568ad5","year":2021},"citing_paper":{"arxiv_id":"2501.06837","last_updated":"2025-01-12T15:10:57Z","snapshot_observed_at":"2026-08-12T19:57:25.048112Z","submitted_at":"2025-01-12T15:10:57Z","title":"An efficient approach to represent enterprise web application structure using Large Language Model in the service of Intelligent Quality Engineering","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-10T20:53:54.804233Z"},"links":{"citing_paper":"/paper/2501.06837"},"observation_digest":"sha256:20069452c49dd4171cd74e8cb8f38046d8223307f5500d864348a61117f81684","observation_id":"ccc77aab-e274-4b44-b69c-6cdf7aeddbef","resolution":{"observed_at":"2026-08-10T20:53:55.371720Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:53:55.354847Z","title":"A comprehensive enterprise system metamodel for quality assurance","venue":null,"work_id":"85424230-04d3-4a98-b7e7-34d00662e353","year":2021},"citing_paper":{"arxiv_id":"2501.06837","last_updated":"2025-01-12T15:10:57Z","snapshot_observed_at":"2026-08-12T19:57:25.048112Z","submitted_at":"2025-01-12T15:10:57Z","title":"An efficient approach to represent enterprise web application structure using Large Language Model in the service of Intelligent Quality Engineering","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-10T20:53:54.808736Z"},"links":{"citing_paper":"/paper/2501.06837"},"observation_digest":"sha256:b34ee9a3cb96a9903f949317a3fe71bee9178677047b49ecb6c2afe049249281","observation_id":"b2e893d8-a866-4483-b82b-8485c2d6f928","resolution":{"observed_at":"2026-08-10T20:53:55.358922Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:53:55.340956Z","title":"Natural language processing-based software testing: A systematic literature review","venue":null,"work_id":"4ef0bd9f-f3bf-4be0-a90b-4e052340d927","year":2024},"citing_paper":{"arxiv_id":"2501.06837","last_updated":"2025-01-12T15:10:57Z","snapshot_observed_at":"2026-08-12T19:57:25.048112Z","submitted_at":"2025-01-12T15:10:57Z","title":"An efficient approach to represent enterprise web application structure using Large Language Model in the service of Intelligent Quality Engineering","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-10T20:53:54.812692Z"},"links":{"citing_paper":"/paper/2501.06837"},"observation_digest":"sha256:5fe85872d909aba713626eef3dd0c28961cac99f4efa8b1568a7a4eff8c9067c","observation_id":"ba9f96e7-97ed-48fc-8169-1fd21fdf918c","resolution":{"observed_at":"2026-08-10T20:53:55.346469Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:53:55.328372Z","title":"Large language models for software engineering: Survey and open problems","venue":null,"work_id":"d9bfc571-e490-400d-973e-ab39dd70235e","year":2023},"citing_paper":{"arxiv_id":"2501.06837","last_updated":"2025-01-12T15:10:57Z","snapshot_observed_at":"2026-08-12T19:57:25.048112Z","submitted_at":"2025-01-12T15:10:57Z","title":"An efficient approach to represent enterprise web application structure using Large Language Model in the service of Intelligent Quality Engineering","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-10T20:53:54.817295Z"},"links":{"citing_paper":"/paper/2501.06837"},"observation_digest":"sha256:a0c2f10928433dfb2777454f2dc21c00c20f2cb187da253590b7853206d8464f","observation_id":"13f89742-926b-4864-9683-e93e9a1cdb42","resolution":{"observed_at":"2026-08-10T20:53:55.332646Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:53:55.314292Z","title":"Software testing with large language models: Survey, landscape, and vision","venue":null,"work_id":"f3eea8bf-8e4c-4f76-843d-b37da3ff58c5","year":2024},"citing_paper":{"arxiv_id":"2501.06837","last_updated":"2025-01-12T15:10:57Z","snapshot_observed_at":"2026-08-12T19:57:25.048112Z","submitted_at":"2025-01-12T15:10:57Z","title":"An efficient approach to represent enterprise web application structure using Large Language Model in the service of Intelligent Quality Engineering","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-10T20:53:54.822322Z"},"links":{"citing_paper":"/paper/2501.06837"},"observation_digest":"sha256:deaf76cc32ca78f7a3bd257a8f1de2a65d65b9331ba5d401e26dabe1c02d8eaf","observation_id":"7272ae3b-e2d2-4efc-8b61-7f416c3a9dc4","resolution":{"observed_at":"2026-08-10T20:53:55.319169Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:53:55.300800Z","title":"A conceptual framework for quality assurance of LLM-based socio-critical systems","venue":null,"work_id":"52330679-cfd4-44f4-87d0-cd34e1ef964d","year":2024},"citing_paper":{"arxiv_id":"2501.06837","last_updated":"2025-01-12T15:10:57Z","snapshot_observed_at":"2026-08-12T19:57:25.048112Z","submitted_at":"2025-01-12T15:10:57Z","title":"An efficient approach to represent enterprise web application structure using Large Language Model in the service of Intelligent Quality Engineering","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-10T20:53:54.826727Z"},"links":{"citing_paper":"/paper/2501.06837"},"observation_digest":"sha256:b5816fba82a25fc22f06c895146294f4db5a77050751904ebe93ebe33ae8d872","observation_id":"758a5263-393f-4360-84c8-0c99dc1a97c5","resolution":{"observed_at":"2026-08-10T20:53:55.306016Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:53:55.288723Z","title":"LLM for test script generation and migration: Challenges, capabilities, and opportunities","venue":null,"work_id":"bff882b7-a7e7-4beb-a1ed-d8093a67fd3a","year":2023},"citing_paper":{"arxiv_id":"2501.06837","last_updated":"2025-01-12T15:10:57Z","snapshot_observed_at":"2026-08-12T19:57:25.048112Z","submitted_at":"2025-01-12T15:10:57Z","title":"An efficient approach to represent enterprise web application structure using Large Language Model in the service of Intelligent Quality Engineering","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-10T20:53:54.832200Z"},"links":{"citing_paper":"/paper/2501.06837"},"observation_digest":"sha256:d3c9c65ed945f7c4329e88c34720e2fcf1ba2f0c5c6e7387845c6231d7a997d2","observation_id":"a8af9da3-0932-45e9-a8dd-45acadf37fdf","resolution":{"observed_at":"2026-08-10T20:53:55.293309Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2312.12598","last_updated":"2023-12-21T20:33:06Z","snapshot_observed_at":"2026-08-16T14:33:11.270446Z","submitted_at":"2023-12-19T20:59:02Z","title":"A Case Study on Test Case Construction with Large Language Models: Unveiling Practical Insights and Challenges","version":2},"cited_work":{"arxiv_id":"2312.12598","doi":null,"metadata_source":"pith","pith_arxiv_id":"2312.12598","snapshot_observed_at":"2026-08-10T20:53:55.001618Z","title":"A Case Study on Test Case Construction with Large Language Models: Unveiling Practical Insights and Challenges","venue":"cs.SE","work_id":"69a5ea0a-fb32-47c6-8627-0951a6f5c394","year":2023},"citing_paper":{"arxiv_id":"2501.06837","last_updated":"2025-01-12T15:10:57Z","snapshot_observed_at":"2026-08-12T19:57:25.048112Z","submitted_at":"2025-01-12T15:10:57Z","title":"An efficient approach to represent enterprise web application structure using Large Language Model in the service of Intelligent Quality Engineering","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-10T20:53:54.836711Z"},"links":{"cited_paper":"/paper/2312.12598","citing_paper":"/paper/2501.06837"},"observation_digest":"sha256:bc51db7bc0beaca266abdb9e9b7cdc1c5741cf593f9dc4343bfaca8eec77a285","observation_id":"43ed1991-810a-4ad1-8795-0302709dbc77","resolution":{"observed_at":"2026-08-10T20:53:55.009100Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:53:55.275418Z","title":"Automated test case generation from requirements: A systematic literature review","venue":null,"work_id":"1595f993-bd79-4c75-ac8c-783ec4c3fccf","year":2021},"citing_paper":{"arxiv_id":"2501.06837","last_updated":"2025-01-12T15:10:57Z","snapshot_observed_at":"2026-08-12T19:57:25.048112Z","submitted_at":"2025-01-12T15:10:57Z","title":"An efficient approach to represent enterprise web application structure using Large Language Model in the service of Intelligent Quality Engineering","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-10T20:53:54.842317Z"},"links":{"citing_paper":"/paper/2501.06837"},"observation_digest":"sha256:0e6161ed82e2647db8d158e5fcd1537da695ca47f5c18ab20489413791835453","observation_id":"7673af47-9906-4aad-a64c-84d30b1b3fa6","resolution":{"observed_at":"2026-08-10T20:53:55.279526Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:53:55.262870Z","title":"Requirement-based automated test case generation: Systematic literature review","venue":null,"work_id":"d3ade1a1-20eb-4c74-9d1a-e3d1990a9613","year":2022},"citing_paper":{"arxiv_id":"2501.06837","last_updated":"2025-01-12T15:10:57Z","snapshot_observed_at":"2026-08-12T19:57:25.048112Z","submitted_at":"2025-01-12T15:10:57Z","title":"An efficient approach to represent enterprise web application structure using Large Language Model in the service of Intelligent Quality Engineering","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-10T20:53:54.846700Z"},"links":{"citing_paper":"/paper/2501.06837"},"observation_digest":"sha256:d4e65b5d446abfcffe34dc9980b29f8c2915e59ebd8e0c12bb47deb3890fc167","observation_id":"237816ab-3137-488e-8fa0-a1c6e617ccba","resolution":{"observed_at":"2026-08-10T20:53:55.267375Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:53:55.249243Z","title":"A semi-automated approach for requirement-based early validation of flight control platforms","venue":null,"work_id":"efa93848-11d2-4011-bb16-83cc682e80fa","year":2022},"citing_paper":{"arxiv_id":"2501.06837","last_updated":"2025-01-12T15:10:57Z","snapshot_observed_at":"2026-08-12T19:57:25.048112Z","submitted_at":"2025-01-12T15:10:57Z","title":"An efficient approach to represent enterprise web application structure using Large Language Model in the service of Intelligent Quality Engineering","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-10T20:53:54.850641Z"},"links":{"citing_paper":"/paper/2501.06837"},"observation_digest":"sha256:f15d6c0118e7ce8d193fe3bab305637c8754bd5e8f571dc4f22cae8d90b42d65","observation_id":"c8fcf0e9-995c-4095-8c4d-438321a28bb3","resolution":{"observed_at":"2026-08-10T20:53:55.254773Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:53:55.230966Z","title":"Functional test generation from ui test scenarios using reinforcement learning for android applications","venue":null,"work_id":"6e72d5c8-b809-4cf7-a998-b92c53b6b1b4","year":2020},"citing_paper":{"arxiv_id":"2501.06837","last_updated":"2025-01-12T15:10:57Z","snapshot_observed_at":"2026-08-12T19:57:25.048112Z","submitted_at":"2025-01-12T15:10:57Z","title":"An efficient approach to represent enterprise web application structure using Large Language Model in the service of Intelligent Quality Engineering","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-10T20:53:54.854867Z"},"links":{"citing_paper":"/paper/2501.06837"},"observation_digest":"sha256:953e9a4d11e6feb3a47af3a31e05316c1a8f3da98d5b3cf4edac3997239c7a23","observation_id":"90b5bcda-3d21-46ce-b8f5-6cf6e0b35ed8","resolution":{"observed_at":"2026-08-10T20:53:55.236530Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:53:55.214747Z","title":"Web program testing using selenium python: Best practices and effective approaches","venue":null,"work_id":"84d26185-05cc-4ff7-b964-501832e5229e","year":2024},"citing_paper":{"arxiv_id":"2501.06837","last_updated":"2025-01-12T15:10:57Z","snapshot_observed_at":"2026-08-12T19:57:25.048112Z","submitted_at":"2025-01-12T15:10:57Z","title":"An efficient approach to represent enterprise web application structure using Large Language Model in the service of Intelligent Quality Engineering","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-10T20:53:54.858469Z"},"links":{"citing_paper":"/paper/2501.06837"},"observation_digest":"sha256:fc5efe90c6703152b17203434ad82438e04e58e5945dcbc6965094d2b6bf1df8","observation_id":"f52f5a7b-711b-4bc6-ad4c-c6b023d142f1","resolution":{"observed_at":"2026-08-10T20:53:55.221395Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:53:55.198989Z","title":"Automated testing of web project functionality with using of error propagation analysis.Computer Systems and Information Technologies, 2023","venue":null,"work_id":"721e742d-5ac0-4b8c-8fc3-5da9bb3cfea0","year":2023},"citing_paper":{"arxiv_id":"2501.06837","last_updated":"2025-01-12T15:10:57Z","snapshot_observed_at":"2026-08-12T19:57:25.048112Z","submitted_at":"2025-01-12T15:10:57Z","title":"An efficient approach to represent enterprise web application structure using Large Language Model in the service of Intelligent Quality Engineering","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-10T20:53:54.862579Z"},"links":{"citing_paper":"/paper/2501.06837"},"observation_digest":"sha256:4ea59ccb3b67956143d4c638a61c415b6b3b1de75a457e6e80786a9427a622c1","observation_id":"6246fa36-da55-4e3a-9dc5-1bdd49173bba","resolution":{"observed_at":"2026-08-10T20:53:55.204073Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:53:55.179947Z","title":"Automated functional testing pada api menggunakan keyword driven framework.Journal of Informatics and Communication Technology, 2021","venue":null,"work_id":"b4015bd5-a34a-479e-acb0-32c75e189682","year":2021},"citing_paper":{"arxiv_id":"2501.06837","last_updated":"2025-01-12T15:10:57Z","snapshot_observed_at":"2026-08-12T19:57:25.048112Z","submitted_at":"2025-01-12T15:10:57Z","title":"An efficient approach to represent enterprise web application structure using Large Language Model in the service of Intelligent Quality Engineering","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-10T20:53:54.867049Z"},"links":{"citing_paper":"/paper/2501.06837"},"observation_digest":"sha256:eef14a1b89e45a2390827ca038e1d804df9e94026ba2bf3f0d48d63a85756544","observation_id":"78c41c34-3e02-4a9b-baab-9abf6bcae529","resolution":{"observed_at":"2026-08-10T20:53:55.187442Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:53:55.167567Z","title":"Automating test oracles from restricted natural language agile requirements","venue":null,"work_id":"9f24024e-3558-4b8c-8e99-f70c32532090","year":2020},"citing_paper":{"arxiv_id":"2501.06837","last_updated":"2025-01-12T15:10:57Z","snapshot_observed_at":"2026-08-12T19:57:25.048112Z","submitted_at":"2025-01-12T15:10:57Z","title":"An efficient approach to represent enterprise web application structure using Large Language Model in the service of Intelligent Quality Engineering","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-10T20:53:54.873830Z"},"links":{"citing_paper":"/paper/2501.06837"},"observation_digest":"sha256:c1e3abd100bc81f26199bd271291bd8d19200980bf5681e90e3d24106b221c95","observation_id":"fb2fbf5a-aa89-45b7-82c1-cd63b788adc7","resolution":{"observed_at":"2026-08-10T20:53:55.171679Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:53:55.151571Z","title":"A bert-based transfer learning approach to text classification on software requirements specifications","venue":null,"work_id":"c4dfdd02-3cc8-497d-9be4-e69d7b5df0e6","year":2021},"citing_paper":{"arxiv_id":"2501.06837","last_updated":"2025-01-12T15:10:57Z","snapshot_observed_at":"2026-08-12T19:57:25.048112Z","submitted_at":"2025-01-12T15:10:57Z","title":"An efficient approach to represent enterprise web application structure using Large Language Model in the service of Intelligent Quality Engineering","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-10T20:53:54.878239Z"},"links":{"citing_paper":"/paper/2501.06837"},"observation_digest":"sha256:1bc6bae8643384cacecdc94deb2516da09958c82587ecbadbb2e1fdc4cde9aab","observation_id":"b209b59f-a3bd-4d6f-9304-aa9807ad1066","resolution":{"observed_at":"2026-08-10T20:53:55.155995Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:53:55.133627Z","title":"User stories and natural language processing: A systematic literature review","venue":null,"work_id":"d84fa128-6801-47f9-83d4-417d834cf4f9","year":2021},"citing_paper":{"arxiv_id":"2501.06837","last_updated":"2025-01-12T15:10:57Z","snapshot_observed_at":"2026-08-12T19:57:25.048112Z","submitted_at":"2025-01-12T15:10:57Z","title":"An efficient approach to represent enterprise web application structure using Large Language Model in the service of Intelligent Quality Engineering","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-10T20:53:54.883285Z"},"links":{"citing_paper":"/paper/2501.06837"},"observation_digest":"sha256:3902469360d95c5f6774904a90f988fcb86fa9a19f1814fcc1f2604689d0cae3","observation_id":"de7aaeee-bbf9-4349-b23e-55e25c8b03b7","resolution":{"observed_at":"2026-08-10T20:53:55.139390Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:53:55.114565Z","title":"MuFBDTester: A mutation-based test sequence generator for FBD programs implementing nuclear power plant software","venue":null,"work_id":"bb812dec-d232-4271-9892-7ee95e67c52e","year":2022},"citing_paper":{"arxiv_id":"2501.06837","last_updated":"2025-01-12T15:10:57Z","snapshot_observed_at":"2026-08-12T19:57:25.048112Z","submitted_at":"2025-01-12T15:10:57Z","title":"An efficient approach to represent enterprise web application structure using Large Language Model in the service of Intelligent Quality Engineering","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-10T20:53:54.891698Z"},"links":{"citing_paper":"/paper/2501.06837"},"observation_digest":"sha256:e9a8c8d5938af4d68c279b0a50be44b3a07c1d68947e5ba962033a9e800197bd","observation_id":"8579411f-e88d-4a62-869b-6f3a46c9c299","resolution":{"observed_at":"2026-08-10T20:53:55.119965Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:53:55.095284Z","title":"Software test case generation using natural language processing (NLP): A systematic literature review","venue":null,"work_id":"87c95a1a-809f-4ee0-a5a1-015535f02dd0","year":2024},"citing_paper":{"arxiv_id":"2501.06837","last_updated":"2025-01-12T15:10:57Z","snapshot_observed_at":"2026-08-12T19:57:25.048112Z","submitted_at":"2025-01-12T15:10:57Z","title":"An efficient approach to represent enterprise web application structure using Large Language Model in the service of Intelligent Quality Engineering","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-10T20:53:54.900518Z"},"links":{"citing_paper":"/paper/2501.06837"},"observation_digest":"sha256:f9219d38a729abadcdff9de7557ea14aff5c3b798b506414b0829f7255e56fa3","observation_id":"63c8cd5e-721c-4f14-81c9-b1cddd4cf88a","resolution":{"observed_at":"2026-08-10T20:53:55.101900Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:53:55.081357Z","title":"An empirical study to compare three web test automation approaches: NLP-based, programmable, and capture&replay","venue":null,"work_id":"5740d11b-4975-4e83-a2eb-391d4f9a80d3","year":2023},"citing_paper":{"arxiv_id":"2501.06837","last_updated":"2025-01-12T15:10:57Z","snapshot_observed_at":"2026-08-12T19:57:25.048112Z","submitted_at":"2025-01-12T15:10:57Z","title":"An efficient approach to represent enterprise web application structure using Large Language Model in the service of Intelligent Quality Engineering","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-10T20:53:54.907197Z"},"links":{"citing_paper":"/paper/2501.06837"},"observation_digest":"sha256:7f879ce8d58861a93b31784e88f92a8fd774ad6c5439d1a5fc602cd21632864f","observation_id":"58ddc8e0-d0ca-4d6a-883d-d82cd397ea39","resolution":{"observed_at":"2026-08-10T20:53:55.086539Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:53:55.064990Z","title":null,"venue":null,"work_id":"b961adb7-33e3-4cfb-ab3b-0f41cecd9f95","year":2022},"citing_paper":{"arxiv_id":"2501.06837","last_updated":"2025-01-12T15:10:57Z","snapshot_observed_at":"2026-08-12T19:57:25.048112Z","submitted_at":"2025-01-12T15:10:57Z","title":"An efficient approach to represent enterprise web application structure using Large Language Model in the service of Intelligent Quality Engineering","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-10T20:53:54.912006Z"},"links":{"citing_paper":"/paper/2501.06837"},"observation_digest":"sha256:0e84d3186827585720dfed757795a5d09761521a7961eed93e912e988a0bc647","observation_id":"e2e0157a-9029-40eb-b89b-f1d0c0d586d6","resolution":{"observed_at":"2026-08-10T20:53:55.069838Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:53:55.050915Z","title":"Chen, Gunvant Chaudhari, Thienkhai Vu, Youngho Seo, Jared Narvid, and Jae Ho Sohn","venue":null,"work_id":"9eb6d3d7-8d84-4442-bd88-ceadf3b6b7f4","year":2021},"citing_paper":{"arxiv_id":"2501.06837","last_updated":"2025-01-12T15:10:57Z","snapshot_observed_at":"2026-08-12T19:57:25.048112Z","submitted_at":"2025-01-12T15:10:57Z","title":"An efficient approach to represent enterprise web application structure using Large Language Model in the service of Intelligent Quality Engineering","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-10T20:53:54.916748Z"},"links":{"citing_paper":"/paper/2501.06837"},"observation_digest":"sha256:abc58c883fcc1ea8ff34449a50b351a371803500222b25f34e7449ef9a6ddc76","observation_id":"7ce05371-c954-4652-95d4-7d1e2330e11c","resolution":{"observed_at":"2026-08-10T20:53:55.055517Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:53:55.037061Z","title":"Natural language processing for assessing quality indicators in free-text colonoscopy and pathology reports: Development and usability study","venue":null,"work_id":"19d263e4-aef0-418d-8279-11b62bcba924","year":2022},"citing_paper":{"arxiv_id":"2501.06837","last_updated":"2025-01-12T15:10:57Z","snapshot_observed_at":"2026-08-12T19:57:25.048112Z","submitted_at":"2025-01-12T15:10:57Z","title":"An efficient approach to represent enterprise web application structure using Large Language Model in the service of Intelligent Quality Engineering","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-10T20:53:54.922825Z"},"links":{"citing_paper":"/paper/2501.06837"},"observation_digest":"sha256:f117edfda40a1acd6efd3c94df2f35b4ca7af326e68c39590a229ee9d666ab79","observation_id":"cadceeee-22fa-4c09-92b4-6ee8ac920e0c","resolution":{"observed_at":"2026-08-10T20:53:55.041992Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:53:55.022962Z","title":"Tignanelli, Greg Silverman, Elizabeth Lindemann, A","venue":null,"work_id":"4936cd23-40a0-4725-a60d-55f954a8035b","year":2020},"citing_paper":{"arxiv_id":"2501.06837","last_updated":"2025-01-12T15:10:57Z","snapshot_observed_at":"2026-08-12T19:57:25.048112Z","submitted_at":"2025-01-12T15:10:57Z","title":"An efficient approach to represent enterprise web application structure using Large Language Model in the service of Intelligent Quality Engineering","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-10T20:53:54.933959Z"},"links":{"citing_paper":"/paper/2501.06837"},"observation_digest":"sha256:07c8c01b8be8eb17043c7df543c390c07e4589a44855dbd87e3481b0733339ca","observation_id":"f22bc29c-73aa-44de-914b-f792cd863043","resolution":{"observed_at":"2026-08-10T20:53:55.027430Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2205.06439","last_updated":"2022-05-13T03:47:13Z","snapshot_observed_at":"2026-08-16T17:00:37.394408Z","submitted_at":"2022-05-13T03:47:13Z","title":"AEON: A Method for Automatic Evaluation of NLP Test Cases","version":1},"cited_work":{"arxiv_id":"2205.06439","doi":null,"metadata_source":"pith","pith_arxiv_id":"2205.06439","snapshot_observed_at":"2026-08-10T20:53:54.977923Z","title":"AEON: A Method for Automatic Evaluation of NLP Test Cases","venue":"cs.SE","work_id":"7965c3b9-e6d0-48b6-8334-565ea9e74623","year":2022},"citing_paper":{"arxiv_id":"2501.06837","last_updated":"2025-01-12T15:10:57Z","snapshot_observed_at":"2026-08-12T19:57:25.048112Z","submitted_at":"2025-01-12T15:10:57Z","title":"An efficient approach to represent enterprise web application structure using Large Language Model in the service of Intelligent Quality Engineering","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-10T20:53:54.938567Z"},"links":{"cited_paper":"/paper/2205.06439","citing_paper":"/paper/2501.06837"},"observation_digest":"sha256:34e2f9a0b2eaf0a8dc5bfc5f1ab61c0513a4969404109e49bf09ab4dca1ac3c9","observation_id":"b64b9294-45de-46d1-b87c-9c319ef92fb4","resolution":{"observed_at":"2026-08-10T20:53:54.987738Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2501.06837","last_updated":"2025-01-12T15:10:57Z","latest_version":1,"primary_category":"cs.AI","snapshot_observed_at":"2026-08-12T19:57:25.048112Z","submitted_at":"2025-01-12T15:10:57Z","title":"An efficient approach to represent enterprise web application structure using Large Language Model in the service of Intelligent Quality Engineering"},"reference_resolution":{"displayed":32,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":1,"verified_exact":2,"verified_fuzzy":29},"total_outbound_references":32},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 17 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 1 inbound Pith citation observation for arXiv:2501.06837."}