Christoph Treude

A Validated Set of Smells in Model-View-Controller Architectures

2016

Code smells are symptoms of poor design and implementation choices that may hinder code comprehension, and possibly increase change-and defect-proneness. A vast catalogue of smells has been defined in the literature, and it includes smells …

Does this apply to me?

2022

Stack Overflow has become an essential technical resource for developers. However, given the vast amount of knowledge available on Stack Overflow, finding the right information that is relevant for a given task is still challenging, …

Toward Human-Like Summaries Generated from Heterogeneous Software\n Artefacts

2019 · arXiv (Cornell University)

Automatic text summarisation has drawn considerable interest in the field of\nsoftware engineering. It can improve the efficiency of software developers,\nenhance the quality of products, and ensure timely delivery. In this paper, we\npresent our initial work …

Stop Words for Processing Software Engineering Documents: Do they Matter?

2023 · arXiv (Cornell University)

Stop words, which are considered non-predictive, are often eliminated in natural language processing tasks. However, the definition of uninformative vocabulary is vague, so most algorithms use general knowledge-based stop lists to remove stop words. There …

Lessons from the Long Tail: Analysing Unsafe Dependency Updates across Software Ecosystems

2023 · arXiv (Cornell University)

A risk in adopting third-party dependencies into an application is their potential to serve as a doorway for malicious code to be injected (most often unknowingly). While many initiatives from both industry and research communities …

GitHub Actions: The Impact on the Pull Request Process

2023 · Empirical Software Engineering

Abstract Software projects frequently use automation tools to perform repetitive activities in the distributed software development process. Recently, GitHub introduced GitHub Actions , a feature providing automated workflows for software projects. Understanding and anticipating the …

APIDocBooster: An Extract-Then-Abstract Framework Leveraging Large Language Models for Augmenting API Documentation

2023 · arXiv (Cornell University)

API documentation is often the most trusted resource for programming. Many approaches have been proposed to augment API documentation by summarizing complementary information from external resources such as Stack Overflow. Existing extractive-based summarization approaches excel …

GitHubInclusifier: Finding and fixing non-inclusive language in GitHub Repositories

2024

Non-inclusive language in software artefacts has been recognised as a serious problem. We describe a tool to find and fix non-inclusive language in a variety of GitHub repository artefacts. These include various README files, PDFs, …

Leveraging Reviewer Experience in Code Review Comment Generation

2024 · arXiv (Cornell University)

Modern code review is a ubiquitous software quality assurance process aimed at identifying potential issues within newly written code. Despite its effectiveness, the process demands large amounts of effort from the human reviewers involved. To …

Do comments and expertise still matter? An experiment on programmers’ adoption of AI-generated JavaScript code

2025 · Journal of Systems and Software

This paper investigates the factors influencing programmers’ adoption of AI-generated JavaScript code recommendations within the context of lightweight, function-level programming tasks. It extends prior research by (1) utilizing objective (as opposed to the typically self-reported) …

The Shift from Writing to Pruning Software: A Bonsai-Inspired IDE for Reshaping AI Generated Code

2025 · arXiv (Cornell University)

The rise of AI-driven coding assistants signals a fundamental shift in how software is built. While AI coding assistants have been integrated into existing Integrated Development Environments (IDEs), their full potential remains largely untapped. A …