{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:G5GWN3QBM4UT4OLZ547BPN7PQA","short_pith_number":"pith:G5GWN3QB","schema_version":"1.0","canonical_sha256":"374d66ee0167293e3979ef3e17b7ef80386ca4da5346fc6c08ca570465bda225","source":{"kind":"arxiv","id":"2402.11910","version":2},"attestation_state":"computed","paper":{"title":"Enhancing Large Language Models for Text-to-Testcase Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"Aldeida Aleti, Chakkrit Tantithamthavorn, Chetan Arora, Saranya Alagarsamy, Wannita Takerngsaksiri","submitted_at":"2024-02-19T07:50:54Z","abstract_excerpt":"Context: Test-driven development (TDD) is a widely employed software development practice that involves developing test cases based on requirements prior to writing the code. Although various methods for automated test case generation have been proposed, they are not specifically tailored for TDD, where requirements instead of code serve as input. Objective: In this paper, we introduce a text-to-testcase generation approach based on a large language model (GPT-3.5) that is fine-tuned on our curated dataset with an effective prompt design. Method: Our approach involves enhancing the capabilitie"},"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":"2402.11910","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SE","submitted_at":"2024-02-19T07:50:54Z","cross_cats_sorted":[],"title_canon_sha256":"66cc784cbc36f7f061f67c1c57ab2c5b885d957707ad631d016cdeef56aa33b3","abstract_canon_sha256":"5dd9a87e9b5ef10aec2787a08aff3ccb32a034ec53cbabaec255fbcb3393d646"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:42:10.994262Z","signature_b64":"g+96bcYj35ZjYs0C+1yl/sxeOgM3IicIOQw2K3COhoQYjNAVlYNYHyNYDSf8zpAa6FeOZRbLFgyTRp2EvreIDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"374d66ee0167293e3979ef3e17b7ef80386ca4da5346fc6c08ca570465bda225","last_reissued_at":"2026-07-05T10:42:10.993776Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:42:10.993776Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Enhancing Large Language Models for Text-to-Testcase Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"Aldeida Aleti, Chakkrit Tantithamthavorn, Chetan Arora, Saranya Alagarsamy, Wannita Takerngsaksiri","submitted_at":"2024-02-19T07:50:54Z","abstract_excerpt":"Context: Test-driven development (TDD) is a widely employed software development practice that involves developing test cases based on requirements prior to writing the code. Although various methods for automated test case generation have been proposed, they are not specifically tailored for TDD, where requirements instead of code serve as input. Objective: In this paper, we introduce a text-to-testcase generation approach based on a large language model (GPT-3.5) that is fine-tuned on our curated dataset with an effective prompt design. Method: Our approach involves enhancing the capabilitie"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.11910","kind":"arxiv","version":2},"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/2402.11910/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":"2402.11910","created_at":"2026-07-05T10:42:10.993834+00:00"},{"alias_kind":"arxiv_version","alias_value":"2402.11910v2","created_at":"2026-07-05T10:42:10.993834+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.11910","created_at":"2026-07-05T10:42:10.993834+00:00"},{"alias_kind":"pith_short_12","alias_value":"G5GWN3QBM4UT","created_at":"2026-07-05T10:42:10.993834+00:00"},{"alias_kind":"pith_short_16","alias_value":"G5GWN3QBM4UT4OLZ","created_at":"2026-07-05T10:42:10.993834+00:00"},{"alias_kind":"pith_short_8","alias_value":"G5GWN3QB","created_at":"2026-07-05T10:42:10.993834+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2502.06556","citing_title":"MultiFileTest: A Multi-File-Level LLM Unit Test Generation Benchmark and Impact of Error Fixing Mechanisms","ref_index":2,"is_internal_anchor":false},{"citing_arxiv_id":"2505.13766","citing_title":"A Blueprint for AI-Driven Software Quality: Integrating LLMs with Established Standards","ref_index":66,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/G5GWN3QBM4UT4OLZ547BPN7PQA","json":"https://pith.science/pith/G5GWN3QBM4UT4OLZ547BPN7PQA.json","graph_json":"https://pith.science/api/pith-number/G5GWN3QBM4UT4OLZ547BPN7PQA/graph.json","events_json":"https://pith.science/api/pith-number/G5GWN3QBM4UT4OLZ547BPN7PQA/events.json","paper":"https://pith.science/paper/G5GWN3QB"},"agent_actions":{"view_html":"https://pith.science/pith/G5GWN3QBM4UT4OLZ547BPN7PQA","download_json":"https://pith.science/pith/G5GWN3QBM4UT4OLZ547BPN7PQA.json","view_paper":"https://pith.science/paper/G5GWN3QB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2402.11910&json=true","fetch_graph":"https://pith.science/api/pith-number/G5GWN3QBM4UT4OLZ547BPN7PQA/graph.json","fetch_events":"https://pith.science/api/pith-number/G5GWN3QBM4UT4OLZ547BPN7PQA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/G5GWN3QBM4UT4OLZ547BPN7PQA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/G5GWN3QBM4UT4OLZ547BPN7PQA/action/storage_attestation","attest_author":"https://pith.science/pith/G5GWN3QBM4UT4OLZ547BPN7PQA/action/author_attestation","sign_citation":"https://pith.science/pith/G5GWN3QBM4UT4OLZ547BPN7PQA/action/citation_signature","submit_replication":"https://pith.science/pith/G5GWN3QBM4UT4OLZ547BPN7PQA/action/replication_record"}},"created_at":"2026-07-05T10:42:10.993834+00:00","updated_at":"2026-07-05T10:42:10.993834+00:00"}