Tootfinder

Large Language Models for Unit Testing: A Systematic Literature Review
Quanjun Zhang, Chunrong Fang, Siqi Gu, Ye Shang, Zhenyu Chen, Liang Xiao
https://arxiv.org/abs/2506.15227

Large Language Models for Unit Testing: A Systematic Literature Review
Unit testing is a fundamental practice in modern software engineering, with the aim of ensuring the correctness, maintainability, and reliability of individual software components. Very recently, with the advances in Large Language Models (LLMs), a rapidly growing body of research has leveraged LLMs to automate various unit testing tasks, demonstrating remarkable performance and significantly reducing manual effort. However, due to ongoing explorations in the LLM-based unit testing field, it is…

Model Context Protocol (MCP) at First Glance: Studying the Security and Maintainability of MCP Servers
Mohammed Mehedi Hasan, Hao Li, Emad Fallahzadeh, Bram Adams, Ahmed E. Hassan
https://arxiv.org/abs/2506.13538

Model Context Protocol (MCP) at First Glance: Studying the Security and Maintainability of MCP Servers
Although Foundation Models (FMs), such as GPT-4, are increasingly used in domains like finance and software engineering, reliance on textual interfaces limits these models' real-world interaction. To address this, FM providers introduced tool calling-triggering a proliferation of frameworks with distinct tool interfaces. In late 2024, Anthropic introduced the Model Context Protocol (MCP) to standardize this tool ecosystem, which has become the de facto standard with over eight million weekly SD…

ROSE: Transformer-Based Refactoring Recommendation for Architectural Smells
Samal Nursapa, Anastassiya Samuilova, Alessio Bucaioni. Phuong T. Nguyen
https://arxiv.org/abs/2507.12561

ROSE: Transformer-Based Refactoring Recommendation for Architectural Smells
Architectural smells such as God Class, Cyclic Dependency, and Hub-like Dependency degrade software quality and maintainability. Existing tools detect such smells but rarely suggest how to fix them. This paper explores the use of pre-trained transformer models--CodeBERT and CodeT5--for recommending suitable refactorings based on detected smells. We frame the task as a three-class classification problem and fine-tune both models on over 2 million refactoring instances mined from 11,149 open-sour…

CoQuIR: A Comprehensive Benchmark for Code Quality-Aware Information Retrieval
Jiahui Geng, Fengyu Cai, Shaobo Cui, Qing Li, Liangwei Chen, Chenyang Lyu, Haonan Li, Derui Zhu, Walter Pretschner, Heinz Koeppl, Fakhri Karray
https://arxiv.org/abs/2506.11066

CoQuIR: A Comprehensive Benchmark for Code Quality-Aware Information Retrieval
Code retrieval is essential in modern software development, as it boosts code reuse and accelerates debugging. However, current benchmarks primarily emphasize functional relevance while neglecting critical dimensions of software quality. Motivated by this gap, we introduce CoQuIR, the first large-scale, multilingual benchmark specifically designed to evaluate quality-aware code retrieval across four key dimensions: correctness, efficiency, security, and maintainability. CoQuIR provides fine-gra…

How Robust are LLM-Generated Library Imports? An Empirical Study using Stack Overflow
Jasmine Latendresse, SayedHassan Khatoonabadi, Emad Shihab
https://arxiv.org/abs/2507.10818

How Robust are LLM-Generated Library Imports? An Empirical Study using Stack Overflow
Software libraries are central to the functionality, security, and maintainability of modern code. As developers increasingly turn to Large Language Models (LLMs) to assist with programming tasks, understanding how these models recommend libraries is essential. In this paper, we conduct an empirical study of six state-of-the-art LLMs, both proprietary and open-source, by prompting them to solve real-world Python problems sourced from Stack Overflow. We analyze the types of libraries they import…

Echoes of AI: Investigating the Downstream Effects of AI Assistants on Software Maintainability
Markus Borg, Dave Hewett, Nadim Hagatulah, Noric Couderc, Emma S\"oderberg, Donald Graham, Uttam Kini, Dave Farley
https://arxiv.org/abs/2507.00788

Echoes of AI: Investigating the Downstream Effects of AI Assistants on Software Maintainability
[Context] AI assistants, like GitHub Copilot and Cursor, are transforming software engineering. While several studies highlight productivity improvements, their impact on maintainability requires further investigation. [Objective] This study investigates whether co-development with AI assistants affects software maintainability, specifically how easily other developers can evolve the resulting source code. [Method] We conducted a two-phase controlled experiment involving 151 participants, 95% o…

LLMCup: Ranking-Enhanced Comment Updating with LLMs
Hua Ge, Juan Zhai, Minxue Pan, Fusen He, Ziyue Tan
https://arxiv.org/abs/2507.08671 https://

LLMCup: Ranking-Enhanced Comment Updating with LLMs
While comments are essential for enhancing code readability and maintainability in modern software projects, developers are often motivated to update code but not comments, leading to outdated or inconsistent documentation that hinders future understanding and maintenance. Recent approaches such as CUP and HebCup have attempted automatic comment updating using neural sequence-to-sequence models and heuristic rules, respectively. However, these methods can miss or misinterpret crucial informatio…

The Impact of AI-Generated Solutions on Software Architecture and Productivity: Results from a Survey Study
Giorgio Amasanti, Jasmin Jahic
https://arxiv.org/abs/2506.17833

The Impact of AI-Generated Solutions on Software Architecture and Productivity: Results from a Survey Study
AI-powered software tools are widely used to assist software engineers. However, there is still a need to understand the productivity benefits of such tools for software engineers. In addition to short-term benefits, there is a question of how adopting AI-generated solutions affects the quality of software over time (e.g., maintainability and extendability). To provide some insight on these questions, we conducted a survey among software practitioners who use AI tools. Based on the data colle…