Pith. sign in

REVIEW 2 cited by

Can ChatGPT Support Developers? An Empirical Evaluation of Large Language Models for Code Generation

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2402.11702 v2 pith:4UZU75CU submitted 2024-02-18 cs.SE cs.AIcs.LG

classification cs.SEcs.AIcs.LG
keywords codedevelopersempiricalgenerationllmschatgptconversationsdevelopment
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Large language models (LLMs) have demonstrated notable proficiency in code generation, with numerous prior studies showing their promising capabilities in various development scenarios. However, these studies mainly provide evaluations in research settings, which leaves a significant gap in understanding how effectively LLMs can support developers in real-world. To address this, we conducted an empirical analysis of conversations in DevGPT, a dataset collected from developers' conversations with ChatGPT (captured with the Share Link feature on platforms such as GitHub). Our empirical findings indicate that the current practice of using LLM-generated code is typically limited to either demonstrating high-level concepts or providing examples in documentation, rather than to be used as production-ready code. These findings indicate that there is much future work needed to improve LLMs in code generation before they can be integral parts of modern software development.

Discussion (0). Sign in to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. How Robust are LLM-Generated Library Imports? An Empirical Study using Stack Overflow

    cs.SE 2025-07 conditional novelty 6.0 of 10

    Across 112 Python Stack Overflow questions, six LLMs recommended mostly mature, permissive third-party libraries, with about 4.6% of unique imports unresolvable due to import/package name mismatches.

  2. The Impact of Generative AI on Code Expertise Models: An Exploratory Study

    cs.SE 2025-07 conditional novelty 6.0 of 10

    Simulating the attribution of a mean 39% of added lines to GenAI changes Degree of Expertise values slightly and alters Truck Factor values or rankings in 73% of computed scenarios.

Pith tools