Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Evaluating Large Language Models in Detecting Test Smells
As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2407.19261.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-16T11:15:40.397416Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-11T01:04:03.954758Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation 616c364a-45f6-4e45-a6e0-8660d05b530d · inbound
How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study cites this paper.
How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study Evaluating Large Language Models in Detecting Test Smells
Reference 15
- Resolution
- verified exact
- local_arxiv, observed 2026-08-11T01:04:03.960809Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 24f86a0b-968c-4614-a071-d36255375d0b · inbound
Benchmarking LLM for Code Smells Detection: OpenAI GPT-4.0 vs DeepSeek-V3 cites this paper.
Benchmarking LLM for Code Smells Detection: OpenAI GPT-4.0 vs DeepSeek-V3 Evaluating Large Language Models in Detecting Test Smells
Reference 3
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T11:15:40.397416Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 00a701e9-5551-4997-b1be-d9aaa12a97e1 · inbound
Model Context Protocol (MCP) Tool Descriptions Are Smelly! Towards Improving AI Agent Efficiency with Augmented MCP Tool Descriptions cites this paper.
Model Context Protocol (MCP) Tool Descriptions Are Smelly! Towards Improving AI Agent Efficiency with Augmented MCP Tool Descriptions Evaluating Large Language Models in Detecting Test Smells
Reference 47
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T23:04:32.529044Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.