arXiv subjects

Eric L. Melin

Publications and source records attributed to Eric L. Melin.

5 recordsLinked to original sources

arXiv2026

Self-Admitted Technical Debt in Scientific Software: Prioritization, Sentiment, and Propagation Across Artifacts

Self-admitted technical debt (SATD) impairs scientific software (SSW), yet its prioritization, sentiment, persistence, and propagation remains underexplored. Understanding how SSW developers express, and address SATD is crucial for improving SSW maintenance, and tooling. This study investigates how SATD types and artifacts in SSW are prioritized, how sentiment relates to urgency, SATD removal and resolution rates, and the extent to which SATD propagates across artifacts. We analyzed nine SSW repositories using a SATD classification model and a semantic embedding-based prioritization heuristic. SATD was examined across multiple artifacts, with sentiment assessed via a fine-tuned transformer. Propagation was traced, priority scores compared to static analysis, and removal and resolution rates quantified. SATD in comments, commits, and pull requests receive higher priority than SATD in issues, with negative sentiment amplifying urgency. Resolution and removal rates lag behind open-source software (OSS) averages. Most SATD remains confined to the originating artifact, but longer propagation chains are rare and correlate with higher priority, highlighting persistent and high impact debt. Prioritization is influenced by artifact type and sentiment, while low removal and resolution rates signal persistent debt. Cross-artifact propagation marks high priority, unresolved SATD, providing empirical guidance for targeted monitoring, review prioritization, and tool supported maintenance in SSW.

cs.SE↗

arXiv2026

Precision or Peril: A PoC of Python Code Quality from Quantized Large Language Models

Context: Large Language Models (LLMs) like GPT-5 and LLaMA-405b exhibit advanced code generation abilities, but their deployment demands substantial computation resources and energy. Quantization can reduce memory footprint and hardware requirements, yet may degrade code quality. Objective: This study investigates code generation performance of smaller LLMs, examines the effect of quantization, and identifies common code quality issues as a proof of concepts (PoC). Method: Four open-source LLMs are evaluated on Python benchmarks using code similarity metrics, with an analysis on 8-bit and 4-bit quantization, alongside static code quality assessment. Results: While smaller LLMs can generate functional code, benchmark performance is limited. Quantization impacts are variable, and generated code exhibits quality and maintainability concerns. Conclusions: LLM-generated code should be carefully validated before integration into software projects.

cs.SE↗

arXiv2026

Multi-Artifact Analysis of Self-Admitted Technical Debt in Scientific Software

Context: Self-admitted technical debt (SATD) occurs when developers acknowledge shortcuts in code. In scientific software (SSW), such debt poses unique risks to the validity and reproducibility of results. Objective: This study aims to identify, categorize, and evaluate scientific debt, a specialized form of SATD in SSW, and assess the extent to which traditional SATD categories capture these domain-specific issues. Method: We conduct a multi-artifact analysis across code comments, commit messages, pull requests, and issue trackers from 23 open-source SSW projects. We construct and validate a curated dataset of scientific debt, develop a multi-source SATD classifier, and conduct a practitioner validation to assess the practical relevance of scientific debt. Results: Our classifier performs strongly across 900,358 artifacts from 23 SSW projects. SATD is most prevalent in pull requests and issue trackers, underscoring the value of multi-artifact analysis. Models trained on traditional SATD often miss scientific debt, emphasizing the need for its explicit detection in SSW. Practitioner validation confirmed that scientific debt is both recognizable and useful in practice. Conclusions: Scientific debt represents a unique form of SATD in SSW that that is not adequately captured by traditional categories and requires specialized identification and management. Our dataset, classification analysis, and practitioner validation results provide the first formal multi-artifact perspective on scientific debt, highlighting the need for tailored SATD detection approaches in SSW.

cs.SE↗

arXiv2025

Exploring Scientific Debt: Harnessing AI for SATD Identification in Scientific Software

Developers often leave behind clues in their code, admitting where it falls short, known as Self-Admitted Technical Debt (SATD). In the world of Scientific Software (SSW), where innovation moves fast and collaboration is key, such debt is not just common but deeply impactful. As research relies on accurate and reproducible results, accumulating SATD can threaten the very foundations of scientific discovery. Yet, despite its significance, the relationship between SATD and SSW remains largely unexplored, leaving a crucial gap in understanding how to manage SATD in this critical domain. This study explores SATD in SSW repositories, comparing SATD in scientific versus general-purpose open-source software and evaluating transformer-based models for SATD identification. We analyzed SATD in 27 scientific and general-purpose repositories across multiple domains and languages. We fine-tuned and compared 10 transformer-based models (100M-7B parameters) on 67,066 labeled code comments. SSW contains 9.25x more Scientific Debt and 4.93x more SATD than general-purpose software due to complex computations, domain constraints, and evolving research needs. Furthermore, our best model outperforms existing ones. This study uncovers how SATD in SSW differs from general software, revealing its impact on quality and scientific validity. By recognizing these challenges, developers and researchers can adopt smarter strategies to manage debt and safeguard the integrity of scientific discovery.

cs.SE↗

arXiv2024

Exploring the Advances in Using Machine Learning to Identify Technical Debt and Self-Admitted Technical Debt

In software engineering, technical debt, signifying the compromise between short-term expediency and long-term maintainability, is being addressed by researchers through various machine learning approaches. This study seeks to provide a reflection on the current research landscape employing machine learning methods for detecting technical debt and self-admitted technical debt in software projects and compare the machine learning research about technical debt and self-admitted technical debt. We performed a literature review of studies published up to 2024 that discuss technical debt and self-admitted technical debt identification using machine learning. Our findings reveal the utilization of a diverse range of machine learning techniques, with BERT models proving significantly more effective than others. This study demonstrates that although the performance of techniques has improved over the years, no universally adopted approach reigns supreme. The results suggest prioritizing BERT techniques over others in future works.

cs.SE↗