Self-Admitted Technical Debt in Scientific Software: Prioritization, Sentiment, and Propagation Across Artifacts
Eric L. Melin, Nasir U. Eisty, Gregory R. Watson, Addi Malviya-Thakur

TL;DR
This study analyzes self-admitted technical debt in scientific software, examining how it is prioritized, its sentiment, propagation, and resolution, to improve maintenance and tooling strategies.
Contribution
It introduces a comprehensive analysis of SATD in scientific software, including classification, sentiment assessment, propagation tracking, and prioritization heuristics, filling a gap in empirical understanding.
Findings
Higher priority for SATD in comments, commits, and pull requests.
Negative sentiment correlates with increased urgency.
Most SATD remains within the original artifact, with rare long propagation chains.
Abstract
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…
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Taxonomy
TopicsSoftware Engineering Research · Scientific Computing and Data Management · Open Source Software Innovations
