Cover Image

Evaluating Large Language Models for Functional and Maintainable Code in Industrial Settings: A Case Study at ASML

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Mundhra, Yash, Valk, Max, Izadi, Maliheh
Format:	Preprint
Published:	2025
Subjects:	Software Engineering Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Traces of Memorisation in Large Language Models for Code
by: Al-Kaswan, Ali, et al.
Published: (2023)

Leveraging Large Language Models for Enhancing the Understandability of Generated Unit Tests
by: Deljouyi, Amirhossein, et al.
Published: (2024)

Code Red! On the Harmfulness of Applying Off-the-shelf Large Language Models to Programming Tasks
by: Al-Kaswan, Ali, et al.
Published: (2025)

Rethinking IDE Customization for Enhanced HAX: A Hyperdimensional Perspective
by: Koohestani, Roham, et al.
Published: (2025)

Model See, Model Do? Exposure-Aware Evaluation of Bug-vs-Fix Preference in Code LLMs
by: Al-Kaswan, Ali, et al.
Published: (2026)

AST-PAC: AST-guided Membership Inference for Code
by: Koohestani, Roham, et al.
Published: (2026)

Does In-IDE Calibration of Large Language Models work at Scale?
by: Koohestani, Roham, et al.
Published: (2025)

Code4MeV2: a Research-oriented Code-completion Platform
by: Koohestani, Roham, et al.
Published: (2025)

TreeRanker: Fast and Model-agnostic Ranking System for Code Suggestions in IDEs
by: Cipollone, Daniele, et al.
Published: (2025)

Benchmarking AI Models in Software Engineering: A Review, Search Tool, and Unified Approach for Elevating Benchmark Quality
by: Koohestani, Roham, et al.
Published: (2025)

A Qualitative Investigation into LLM-Generated Multilingual Code Comments and Automatic Evaluation Metrics
by: Katzy, Jonathan, et al.
Published: (2025)

TriCEGAR: A Trace-Driven Abstraction Mechanism for Agentic AI
by: Koohestani, Roham, et al.
Published: (2026)

Prompt-with-Me: in-IDE Structured Prompt Management for LLM-Driven Software Engineering
by: Li, Ziyou, et al.
Published: (2025)

A Transformer-Based Approach for Smart Invocation of Automatic Code Completion
by: de Moor, Aral, et al.
Published: (2024)

Code Readability in the Age of Large Language Models: An Industrial Case Study from Atlassian
by: Takerngsaksiri, Wannita, et al.
Published: (2025)

Long Code Arena: a Set of Benchmarks for Long-Context Code Models
by: Bogomolov, Egor, et al.
Published: (2024)

Human-AI Experience in Integrated Development Environments: A Systematic Literature Review
by: Sergeyuk, Agnia, et al.
Published: (2025)

A Note on Code Quality Score: LLMs for Maintainable Large Codebases
by: Wong, Sherman, et al.
Published: (2025)

Evaluating Large Language Models for Code Review
by: Cihan, Umut, et al.
Published: (2025)

Do Agents Dream of Root Shells? Partial-Credit Evaluation of LLM Agents in Capture the Flag Challenges
by: Al-Kaswan, Ali, et al.
Published: (2026)

A Multi-agent Onboarding Assistant based on Large Language Models, Retrieval Augmented Generation, and Chain-of-Thought
by: Ionescu, Andrei Cristian, et al.
Published: (2025)

RobuNFR: Evaluating the Robustness of Large Language Models on Non-Functional Requirements Aware Code Generation
by: Lin, Feng, et al.
Published: (2025)

Leveraging LLMs for Multi-File DSL Code Generation: An Industrial Case Study
by: Chand, Sivajeet, et al.
Published: (2026)

How to Trick Your AI TA: A Systematic Study of Academic Jailbreaking in LLM Code Evaluation
by: Sahoo, Devanshu, et al.
Published: (2025)

CodeGolf Bench: A Multi-Language Benchmark for Evaluating Concise Code Generation Capabilities of Large Language Models
by: Padwal, Vedant
Published: (2026)

Investigating Autonomous Agent Contributions in the Wild: Activity Patterns and Code Change over Time
by: Popescu, Razvan Mihai, et al.
Published: (2026)

Automating Patch Set Generation from Code Review Comments Using Large Language Models
by: Rahman, Tajmilur, et al.
Published: (2024)

Bugs in Large Language Models Generated Code: An Empirical Study
by: Tambon, Florian, et al.
Published: (2024)

Output Format Biases in the Evaluation of Large Language Models for Code Translation
by: Macedo, Marcos, et al.
Published: (2024)

Flow2Code: Evaluating Large Language Models for Flowchart-based Code Generation Capability
by: He, Mengliang, et al.
Published: (2025)

Beyond Functional Correctness: Investigating Coding Style Inconsistencies in Large Language Models
by: Wang, Yanlin, et al.
Published: (2024)

CrossPL: Evaluating Large Language Models on Cross Programming Language Code Generation
by: Xiong, Zhanhang, et al.
Published: (2025)

COMPASS: A Multi-Dimensional Benchmark for Evaluating Code Generation in Large Language Models
by: Meaden, James, et al.
Published: (2025)

AdaptEval: A Benchmark for Evaluating Large Language Models on Code Snippet Adaptation
by: Zhang, Tanghaoran, et al.
Published: (2026)

An Exploratory Investigation into Code License Infringements in Large Language Model Training Datasets
by: Katzy, Jonathan, et al.
Published: (2024)

An Empirical Study on Capability of Large Language Models in Understanding Code Semantics
by: Nguyen, Thu-Trang, et al.
Published: (2024)

In-IDE Human-AI Experience in the Era of Large Language Models; A Literature Review
by: Sergeyuk, Agnia, et al.
Published: (2024)

Large Language Models as Test Case Generators: Performance Evaluation and Enhancement
by: Li, Kefan, et al.
Published: (2024)

How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study
by: Velasco, Alejandro, et al.
Published: (2024)

Beyond Autoregression: An Empirical Study of Diffusion Large Language Models for Code Generation
by: Li, Chengze, et al.
Published: (2025)