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This study analyzes self-admitted technical debt (SATD) in scientific software (SSW) by examining its prioritization, sentiment, and propagation across different artifacts. The researchers analyzed nine SSW repositories using a SATD classification model, sentiment analysis via a fine-tuned transformer, and a semantic embedding-based prioritization heuristic. They found that SATD in comments, commits, and pull requests receive higher priority and that negative sentiment amplifies urgency, while also observing that SATD removal and resolution rates are lower than OSS averages, and that longer propagation chains correlate with higher priority.
Scientific software projects struggle to resolve high-priority technical debt that propagates across multiple code artifacts, suggesting a need for better tooling and monitoring.
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.
