Cover Image

Increasing, not Diminishing: Investigating the Returns of Highly Maintainable Code

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Borg, Markus, Pruvost, Ilyana, Mones, Enys, Tornhill, Adam
Format:	Preprint
Published:	2024
Subjects:	Software Engineering
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Industrial Code Quality Benchmarks: Toward Gamification of Software Maintainability
by: Borg, Markus, et al.
Published: (2024)

Ghost Echoes Revealed: Benchmarking Maintainability Metrics and Machine Learning Predictions Against Human Assessments
by: Borg, Markus, et al.
Published: (2024)

Code for Machines, Not Just Humans: Quantifying AI-Friendliness with Code Health Metrics
by: Borg, Markus, et al.
Published: (2026)

Quality Requirements for Code: On the Untapped Potential in Maintainability Specifications
by: Borg, Markus
Published: (2024)

ACE: Automated Technical Debt Remediation with Validated Large Language Model Refactorings
by: Tornhill, Adam, et al.
Published: (2025)

QUPER-MAn: Benchmark-Guided Target Setting for Maintainability Requirements
by: Borg, Markus, et al.
Published: (2025)

Does Co-Development with AI Assistants Lead to More Maintainable Code? A Registered Report
by: Borg, Markus, et al.
Published: (2024)

Echoes of AI: Investigating the Downstream Effects of AI Assistants on Software Maintainability
by: Borg, Markus, et al.
Published: (2025)

Requirements for Organizational Resilience: Engineering Developer Happiness
by: Borg, Markus, et al.
Published: (2024)

MaintainCoder: Maintainable Code Generation Under Dynamic Requirements
by: Wang, Zhengren, et al.
Published: (2025)

Is LLM-Generated Code More Maintainable \& Reliable than Human-Written Code?
by: Molison, Alfred Santa, et al.
Published: (2025)

Trust Calibration in IDEs: Paving the Way for Widespread Adoption of AI Refactoring
by: Borg, Markus
Published: (2024)

Challenges in Comparing Code Maintainability across Different Programming Languages
by: Ponsard, Christophe, et al.
Published: (2024)

How Maintainable is Proficient Code? A Case Study of Three PyPI Libraries
by: Febriyanti, Indira, et al.
Published: (2024)

Novice Developers Produce Larger Review Overhead for Project Maintainers while Vibe Coding
by: Asdaque, Syed Ammar, et al.
Published: (2026)

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

Maintaining the Heterogeneity in the Organization of Software Engineering Research
by: Yue, Yang, et al.
Published: (2026)

Code with Me or for Me? How Increasing AI Automation Transforms Developer Workflows
by: Chen, Valerie, et al.
Published: (2025)

Fostering Microservice Maintainability Assurance through a Comprehensive Framework
by: Abdelfattah, Amr S.
Published: (2024)

Investigating the Impact of Code Comment Inconsistency on Bug Introducing
by: Radmanesh, Shiva, et al.
Published: (2024)

Towards Supporting Open Source Library Maintainers with Community-Based Analytics
by: Raj, Rachna, et al.
Published: (2025)

Bias Unveiled: Investigating Social Bias in LLM-Generated Code
by: Ling, Lin, et al.
Published: (2024)

Investigating the Readability of Test Code: Combining Scientific and Practical Views
by: Winkler, Dietmar, et al.
Published: (2024)

Investigating Code Reuse in Software Redesign: A Case Study
by: Zhang, Xiaowen, et al.
Published: (2026)

Evaluating Large Language Models for Functional and Maintainable Code in Industrial Settings: A Case Study at ASML
by: Mundhra, Yash, et al.
Published: (2025)

Information-Theoretic Detection of Unusual Source Code Changes
by: Torres, Adriano, et al.
Published: (2025)

A Fuzzy Logic-Based Quality Model For Identifying Microservices With Low Maintainability
by: Yilmaz, Rahime, et al.
Published: (2024)

What About Our Bug? A Study on the Responsiveness of NPM Package Maintainers
by: Saeidi, Mohammadreza, et al.
Published: (2025)

Investigating the Impact of SOLID Design Principles on Machine Learning Code Understanding
by: Cabral, Raphael, et al.
Published: (2024)

Investigating Issues that Lead to Code Technical Debt in Machine Learning Systems
by: Ximenes, Rodrigo, et al.
Published: (2025)

Can We Identify Stack Overflow Questions Requiring Code Snippets? Investigating the Cause & Effect of Missing Code Snippets
by: Mondal, Saikat, et al.
Published: (2024)

Quality In, Quality Out: Investigating Training Data's Role in AI Code Generation
by: Improta, Cristina, et al.
Published: (2025)

Integrating Static Code Analysis Toolchains
by: Kern, Matthias, et al.
Published: (2024)

Investigating Student Reasoning in Method-Level Code Refactoring: A Think-Aloud Study
by: Oliveira, Eduardo Carneiro, et al.
Published: (2024)

Quality Gatekeepers: Investigating the Effects ofCode Review Bots on Pull Request Activities
by: Wessel, Mairieli, et al.
Published: (2021)

Accountability in Code Review: The Role of Intrinsic Drivers and the Impact of LLMs
by: Alami, Adam, et al.
Published: (2025)

Static Analysis as a Feedback Loop: Enhancing LLM-Generated Code Beyond Correctness
by: Blyth, Scott, et al.
Published: (2025)

A Survey on Large Language Model Impact on Software Evolvability and Maintainability: the Good, the Bad, the Ugly, and the Remedy
by: Matias, Bruno Claudino, et al.
Published: (2026)

Better Python Programming for all: With the focus on Maintainability
by: Shivashankar, Karthik, et al.
Published: (2024)

Investigating The Smells of LLM Generated Code
by: Paul, Debalina Ghosh, et al.
Published: (2025)