Tootfinder

@… leuke blog over technical debt. Naar mijn ervaring gaat zelf veroorzaakte tech debt altijd gepaard met een uitspraak als: 'och, dat zien we dan wel'. Of 'ja, maar dat is een corner case die niet gebeurt' en 'nou, dat past voorlopig wel'. Er gaan beslissingen vooraf aan elke extra debt. Zowel bij design als bij coding. Je zou in de rep…

ASTAGEN: Empirical Evaluation of Automated SATD Taxonomy Generation with LLMs
Sota Nakashima, Yuta Ishimoto, Masanari Kondo, Tao Xiao, Yasutaka Kamei
https://arxiv.org/abs/2506.09601

ASTAGEN: Empirical Evaluation of Automated SATD Taxonomy Generation with LLMs
Technical debt refers to suboptimal code that degrades software quality. When developers intentionally introduce such debt, it is called self-admitted technical debt (SATD). Since SATD hinders maintenance, identifying its categories is key to uncovering quality issues. Traditionally, constructing such taxonomies requires manually inspecting SATD comments and surrounding code, which is time-consuming, labor-intensive, and often inconsistent due to annotator subjectivity. This study presents ASTA…

ARRC: Explainable, Workflow-Integrated Recommender for Sustainable Resource Optimization Across the Edge-Cloud Continuum
Brian-Frederik Jahnke, Ren\'e Brinkhege, Jan Peter Meyer, Daniel Tebernum, Falk Howar
https://arxiv.org/abs/2507.12032

ARRC: Explainable, Workflow-Integrated Recommender for Sustainable Resource Optimization Across the Edge-Cloud Continuum
Achieving sustainable, explainable, and maintainable automation for resource optimization is a core challenge across the edge-cloud continuum. Persistent overprovisioning and operational complexity often stem from heterogeneous platforms and layered abstractions, while systems lacking explainability and maintainability become fragile, impede safe recovery, and accumulate technical debt. Existing solutions are frequently reactive, limited to single abstraction layers, or require intrusive platfo…

Technical debt to society
Technical debtor's prisons

Today I'm fixing about a gazillion little issues with our website, mostly for SEO reasons:
- All pages should now have a semantically relevant H1 Tag
- Found the last few page titles that didn't make sense and fixed them
- Our logo had no ALT attribute
- The font in the Survey Chart Tooltips was wrong
- Generally survey charts had wayyy to many decimals
- OpenGraph tags are weird but whatever
Keeping technical debt down is like brushing your teeth…

The Technical Debt Gamble: A Case Study on Technical Debt in a Large-Scale Industrial Microservice Architecture
Klara Borowa, Andrzej Ratkowski, Roberto Verdecchia
https://arxiv.org/abs/2506.16214

The Technical Debt Gamble: A Case Study on Technical Debt in a Large-Scale Industrial Microservice Architecture
Microservice architectures provide an intuitive promise of high maintainability and evolvability due to loose coupling. However, these quality attributes are notably vulnerable to technical debt (TD). Few studies address TD in microservice systems, particularly on a large scale. This research explores how TD manifests in a large-scale microservice-based industrial system. The research is based on a mixed-method case study of a project including over 100 microservices and serving over 15k locati…

ACE: Automated Technical Debt Remediation with Validated Large Language Model Refactorings
Adam Tornhill, Markus Borg, Nadim Hagatulah, Emma S\"oderberg
https://arxiv.org/abs/2507.03536

ACE: Automated Technical Debt Remediation with Validated Large Language Model Refactorings
The remarkable advances in AI and Large Language Models (LLMs) have enabled machines to write code, accelerating the growth of software systems. However, the bottleneck in software development is not writing code but understanding it; program understanding is the dominant activity, consuming approximately 70% of developers' time. This implies that improving existing code to make it easier to understand has a high payoff and - in the age of AI-assisted coding - is an essential activity to ensure…

This https://arxiv.org/abs/2505.01136 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

Descriptor: C++ Self-Admitted Technical Debt Dataset (CppSATD)
In software development, technical debt (TD) refers to suboptimal implementation choices made by the developers to meet urgent deadlines and limited resources, posing challenges for future maintenance. Self-Admitted Technical Debt (SATD) is a sub-type of TD, representing specific TD instances ``openly admitted'' by the developers and often expressed through source code comments. Previous research on SATD has focused predominantly on the Java programming language, revealing a significant gap in …

How Do Community Smells Influence Self-Admitted Technical Debt in Machine Learning Projects?
Shamse Tasnim Cynthia, Nuri Almarimi, Banani Roy
https://arxiv.org/abs/2506.15884

How Do Community Smells Influence Self-Admitted Technical Debt in Machine Learning Projects?
Community smells reflect poor organizational practices that often lead to socio-technical issues and the accumulation of Self-Admitted Technical Debt (SATD). While prior studies have explored these problems in general software systems, their interplay in machine learning (ML)-based projects remains largely underexamined. In this study, we investigated the prevalence of community smells and their relationship with SATD in open-source ML projects, analyzing data at the release level. First, we ex…

Managing Technical Debt in a Multidisciplinary Data Intensive Software Team: an Observational Case Study
Ulrike M. Graetsch, Rashina Hoda, Hourieh Khalazjadeh, Mojtaba Shahin, John Grundy
https://arxiv.org/abs/2506.18219

Managing Technical Debt in a Multidisciplinary Data Intensive Software Team: an Observational Case Study
Context: There is an increase in the investment and development of data-intensive (DI) solutions, systems that manage large amounts of data. Without careful management, this growing investment will also grow associated technical debt (TD). Delivery of DI solutions requires a multidisciplinary skill set, but there is limited knowledge about how multidisciplinary teams develop DI systems and manage TD. Objective: This research contributes empirical, practice based insights about multidisciplina…

Does Editing Improve Answer Quality on Stack Overflow? A Data-Driven Investigation
Saikat Mondal, Chanchal K. Roy
https://arxiv.org/abs/2507.21329 https://…

Does Editing Improve Answer Quality on Stack Overflow? A Data-Driven Investigation
High-quality answers in technical Q&A platforms like Stack Overflow (SO) are crucial as they directly influence software development practices. Poor-quality answers can introduce inefficiencies, bugs, and security vulnerabilities, and thus increase maintenance costs and technical debt in production software. To improve content quality, SO allows collaborative editing, where users revise answers to enhance clarity, correctness, and formatting. Several studies have examined rejected edits and ide…

BitsAI-Fix: LLM-Driven Approach for Automated Lint Error Resolution in Practice
Yuanpeng Li, Qi Long, Zhiyuan Yao, Jian Xu, Lintao Xie, Xu He, Lu Geng, Xin Han, Yueyan Chen, Wenbo Duan
https://arxiv.org/abs/2508.03487

BitsAI-Fix: LLM-Driven Approach for Automated Lint Error Resolution in Practice
As enterprise codebases continue to grow in scale and complexity, the volume of lint errors far exceeds engineers' manual remediation capacity, leading to continuous accumulation of technical debt and hindered development efficiency. This paper presents BitsAI-Fix, an automated lint error remediation workflow based on Large Language Models (LLMs), designed to address this critical challenge in industrial-scale environments. BitsAI-Fix employs tree-sitter for context expansion and generates sear…

Build It Clean: Large-Scale Detection of Code Smells in Build Scripts
Mahzabin Tamanna, Yash Chandrani, Matthew Burrows, Brandon Wroblewski, Laurie Williams, Dominik Wermke
https://arxiv.org/abs/2506.17948

Build It Clean: Large-Scale Detection of Code Smells in Build Scripts
Build scripts are files that automate the process of compiling source code, managing dependencies, running tests, and packaging software into deployable artifacts. These scripts are ubiquitous in modern software development pipelines for streamlining testing and delivery. While developing build scripts, practitioners may inadvertently introduce code smells. Code smells are recurring patterns of poor coding practices that may lead to build failures or increase risk and technical debt. The goal o…