Paper Citation Record · LEDGER

Impact of Code Context and Prompting Strategies on Automated Unit Test Generation with Modern General-Purpose Large Language Models

As of 17 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2507.14256.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2507.14256 v1

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T16:20:48.507000Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

39 of 39 outbound references displayed

verified exact0
verified fuzzy29
unresolved10
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4785f31e-e915-4ac6-8f5f-aa6f584e279d · outbound

This paper cites Harnessing the power of llms in practice: A survey on chatgpt and beyond,.

Impact of Code Context and Prompting Strategies on Automated Unit Test Generation with Modern General-Purpose Large Language Models Harnessing the power of llms in practice: A survey on chatgpt and beyond,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T16:20:48.406486Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 73eac20b-6d39-4427-9f58-3abe2a354f3b · outbound

This paper cites Bias and unfairness in information retrieval systems: New challenges in the llm era,.

Impact of Code Context and Prompting Strategies on Automated Unit Test Generation with Modern General-Purpose Large Language Models Bias and unfairness in information retrieval systems: New challenges in the llm era,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:20:48.812218Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f27d58d0-ebca-4d18-8413-435a795f3dde · outbound

This paper cites Art or artifice? large language models and the false promise of creativity,.

Impact of Code Context and Prompting Strategies on Automated Unit Test Generation with Modern General-Purpose Large Language Models Art or artifice? large language models and the false promise of creativity,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:20:48.804643Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b5d776aa-a304-488d-8723-5f2fb0317c09 · outbound

This paper cites Art and the science of generative ai,.

Impact of Code Context and Prompting Strategies on Automated Unit Test Generation with Modern General-Purpose Large Language Models Art and the science of generative ai,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:20:48.796813Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d8d28e81-275a-4454-b570-d34877075be5 · outbound

This paper cites Comparing methods for large- scale agile software development: A systematic literature review,.

Impact of Code Context and Prompting Strategies on Automated Unit Test Generation with Modern General-Purpose Large Language Models Comparing methods for large- scale agile software development: A systematic literature review,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:20:48.789639Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 32adf962-3668-4a56-8132-edc746e1ca2b · outbound

This paper cites Hybrid intelligence,.

Impact of Code Context and Prompting Strategies on Automated Unit Test Generation with Modern General-Purpose Large Language Models Hybrid intelligence,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:20:48.782059Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation bfec1d79-4cd0-4f5e-999f-9781a533e391 · outbound

This paper cites Artificial intelligence, human intelligence and hybrid intelligence based on mutual augmentation,.

Impact of Code Context and Prompting Strategies on Automated Unit Test Generation with Modern General-Purpose Large Language Models Artificial intelligence, human intelligence and hybrid intelligence based on mutual augmentation,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:20:48.774460Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e4c612c9-fb76-43f4-a8b7-87297be92aa7 · outbound

This paper cites Experimental evidence on the productivity effects of generative artificial intelligence,.

Impact of Code Context and Prompting Strategies on Automated Unit Test Generation with Modern General-Purpose Large Language Models Experimental evidence on the productivity effects of generative artificial intelligence,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:20:48.766775Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c7851502-120f-461e-96b0-edec928f6f59 · outbound

This paper cites Attention is all you need,.

Impact of Code Context and Prompting Strategies on Automated Unit Test Generation with Modern General-Purpose Large Language Models Attention is all you need,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T16:20:48.429417Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9c471d86-9f65-47fe-b02a-dd9e417d52f5 · outbound

This paper cites Neural machine translation of rare words with subword units,.

Impact of Code Context and Prompting Strategies on Automated Unit Test Generation with Modern General-Purpose Large Language Models Neural machine translation of rare words with subword units,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:20:48.754239Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b84b8aea-80ad-45ef-8f42-ac0606baafb3 · outbound

This paper cites Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation.

Impact of Code Context and Prompting Strategies on Automated Unit Test Generation with Modern General-Purpose Large Language Models Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T16:20:48.434321Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 103679c1-1090-42fc-a88f-a153a985958a · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large language models,.

Impact of Code Context and Prompting Strategies on Automated Unit Test Generation with Modern General-Purpose Large Language Models Chain-of-thought prompting elicits reasoning in large language models,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T16:20:48.437099Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 797eb877-b688-457b-8cfb-58fe8690987b · outbound

This paper cites A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions,.

Impact of Code Context and Prompting Strategies on Automated Unit Test Generation with Modern General-Purpose Large Language Models A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T16:20:48.439176Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a4872d7b-a10f-4af5-ae39-680ba9f6ff20 · outbound

This paper cites Study of the software development life cycle and the function of testing,.

Impact of Code Context and Prompting Strategies on Automated Unit Test Generation with Modern General-Purpose Large Language Models Study of the software development life cycle and the function of testing,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:20:48.736614Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 949216a2-92ed-439d-bde0-034f516c9f61 · outbound

This paper cites Cohn, Succeeding with agile: software development using Scrum.

Impact of Code Context and Prompting Strategies on Automated Unit Test Generation with Modern General-Purpose Large Language Models Cohn, Succeeding with agile: software development using Scrum

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:20:48.729547Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ff98c0b8-477e-4b95-b8d8-2cfb3613260b · outbound

This paper cites Test automation pyramid from theory to practice,.

Impact of Code Context and Prompting Strategies on Automated Unit Test Generation with Modern General-Purpose Large Language Models Test automation pyramid from theory to practice,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:20:48.722491Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 27f6aafd-d306-4ffb-b638-5270563c7d1a · outbound

This paper cites The testing mechanism for software and services based on mike cohn’s testing pyramid modification,.

Impact of Code Context and Prompting Strategies on Automated Unit Test Generation with Modern General-Purpose Large Language Models The testing mechanism for software and services based on mike cohn’s testing pyramid modification,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:20:48.715372Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 9750e64a-428a-4a6e-ac9e-f2b6cce50cbd · outbound

This paper cites Toward successful devops: a decision-making frame- work,.

Impact of Code Context and Prompting Strategies on Automated Unit Test Generation with Modern General-Purpose Large Language Models Toward successful devops: a decision-making frame- work,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:20:48.707822Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 5d1245a8-fb6c-4e87-8050-f0a0ac1058f8 · outbound

This paper cites Approach to automation of the initial stages of software design,.

Impact of Code Context and Prompting Strategies on Automated Unit Test Generation with Modern General-Purpose Large Language Models Approach to automation of the initial stages of software design,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:20:48.701063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 9e0468c3-94cf-4f9c-84c7-10991417f4e9 · outbound

This paper cites H ¨uttermann, DevOps for developers.

Impact of Code Context and Prompting Strategies on Automated Unit Test Generation with Modern General-Purpose Large Language Models H ¨uttermann, DevOps for developers

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:20:48.693721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b8a2f307-4d4f-4415-8731-84028b7f18ca · outbound

This paper cites An empirical evaluation of using large language models for automated unit test generation,.

Impact of Code Context and Prompting Strategies on Automated Unit Test Generation with Modern General-Purpose Large Language Models An empirical evaluation of using large language models for automated unit test generation,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:20:48.686236Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 14ae6066-fe9e-4458-a1e5-056f900c6ac8 · outbound

This paper cites Evaluating Instruction-Tuned Large Language Models on Code Comprehension and Generation.

Impact of Code Context and Prompting Strategies on Automated Unit Test Generation with Modern General-Purpose Large Language Models Evaluating Instruction-Tuned Large Language Models on Code Comprehension and Generation

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T16:20:48.461201Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8507b7ce-8295-436a-b1d0-2b0a60929666 · outbound

This paper cites Ontology driven software development for automated documentation.

Impact of Code Context and Prompting Strategies on Automated Unit Test Generation with Modern General-Purpose Large Language Models Ontology driven software development for automated documentation

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:20:48.679327Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 16967dd0-b529-42df-82a5-f58f9e42435f · outbound

This paper cites Adopting devops in the real world: A theory, a model, and a case study,.

Impact of Code Context and Prompting Strategies on Automated Unit Test Generation with Modern General-Purpose Large Language Models Adopting devops in the real world: A theory, a model, and a case study,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:20:48.672006Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 41266862-9ec6-44a0-8820-c21e94be8bcc · outbound

This paper cites LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code.

Impact of Code Context and Prompting Strategies on Automated Unit Test Generation with Modern General-Purpose Large Language Models LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-06T16:20:48.469309Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 47b36802-8e6c-45e4-8df3-9dee51ad2ed5 · outbound

This paper cites Testeval: Benchmarking large language models for test case generation,.

Impact of Code Context and Prompting Strategies on Automated Unit Test Generation with Modern General-Purpose Large Language Models Testeval: Benchmarking large language models for test case generation,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:20:48.664501Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 15a667c5-2ca5-4146-8109-d671dbfab04c · outbound

This paper cites Evidence-based methodological framework for machine learning studies,.

Impact of Code Context and Prompting Strategies on Automated Unit Test Generation with Modern General-Purpose Large Language Models Evidence-based methodological framework for machine learning studies,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:20:48.657433Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 9b1d8749-44f4-48fb-bf28-56ff1ae84057 · outbound

This paper cites Reforms: Consensus-based recommendations for machine-learning- based science,.

Impact of Code Context and Prompting Strategies on Automated Unit Test Generation with Modern General-Purpose Large Language Models Reforms: Consensus-based recommendations for machine-learning- based science,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:20:48.649749Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 8df2b24f-1549-4ba5-bbf8-9559e98f279f · outbound

This paper cites Unit testing in practice,.

Impact of Code Context and Prompting Strategies on Automated Unit Test Generation with Modern General-Purpose Large Language Models Unit testing in practice,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:20:48.641188Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6a34dad0-bc3f-4788-b544-9353a4ac4b37 · outbound

This paper cites No More Manual Tests? Evaluating and Improving ChatGPT for Unit Test Generation.

Impact of Code Context and Prompting Strategies on Automated Unit Test Generation with Modern General-Purpose Large Language Models No More Manual Tests? Evaluating and Improving ChatGPT for Unit Test Generation

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-06T16:20:48.483080Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 19c42bc3-1dd2-4f35-a992-4e7091489c1a · outbound

This paper cites On the Evaluation of Large Language Models in Unit Test Generation.

Impact of Code Context and Prompting Strategies on Automated Unit Test Generation with Modern General-Purpose Large Language Models On the Evaluation of Large Language Models in Unit Test Generation

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-06T16:20:48.485955Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 827b558a-7938-4460-b0ea-d75d91bbc41f · outbound

This paper cites A system for automated unit test generation using large language models and assessment of generated test suites,.

Impact of Code Context and Prompting Strategies on Automated Unit Test Generation with Modern General-Purpose Large Language Models A system for automated unit test generation using large language models and assessment of generated test suites,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:20:48.633282Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 9fdf2104-4e95-49f9-8c03-0c98b0157409 · outbound

This paper cites Bidirectional symbolic analysis for effective branch testing,.

Impact of Code Context and Prompting Strategies on Automated Unit Test Generation with Modern General-Purpose Large Language Models Bidirectional symbolic analysis for effective branch testing,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:20:48.624458Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 031aa0b3-1bfe-4011-908b-6b6b94387051 · outbound

This paper cites Mutation-driven generation of unit tests and oracles,.

Impact of Code Context and Prompting Strategies on Automated Unit Test Generation with Modern General-Purpose Large Language Models Mutation-driven generation of unit tests and oracles,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:20:48.615794Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 5994effe-f816-418a-8de0-6c2af925eb2e · outbound

This paper cites Performance regression unit testing: a case study,.

Impact of Code Context and Prompting Strategies on Automated Unit Test Generation with Modern General-Purpose Large Language Models Performance regression unit testing: a case study,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:20:48.608011Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 450f6879-9a4d-4936-b694-d31f134828f8 · outbound

This paper cites Utilizing performance unit tests to increase performance awareness,.

Impact of Code Context and Prompting Strategies on Automated Unit Test Generation with Modern General-Purpose Large Language Models Utilizing performance unit tests to increase performance awareness,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:20:48.599963Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b7b95fcd-6d41-4dfd-bc9f-b0f94e8aefae · outbound

This paper cites Microsoft announces new copilot copyright commitment for customers,.

Impact of Code Context and Prompting Strategies on Automated Unit Test Generation with Modern General-Purpose Large Language Models Microsoft announces new copilot copyright commitment for customers,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:20:48.592055Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 5c925b11-9b21-4148-a40a-336c00ea3717 · outbound

This paper cites Self-Consistency Improves Chain of Thought Reasoning in Language Models.

Impact of Code Context and Prompting Strategies on Automated Unit Test Generation with Modern General-Purpose Large Language Models Self-Consistency Improves Chain of Thought Reasoning in Language Models

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-06T16:20:48.504376Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 745903e2-041a-4432-a504-912d413247d8 · outbound

This paper cites Mutmut: Python mutation testing tool,.

Impact of Code Context and Prompting Strategies on Automated Unit Test Generation with Modern General-Purpose Large Language Models Mutmut: Python mutation testing tool,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:20:48.583210Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Pith citing papers

No inbound Pith citation observations are available.