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LOCATION:Bldg. 6 - Room 102
DTSTART;TZID=Europe/Stockholm:20260629T113000
DTEND;TZID=Europe/Stockholm:20260629T120000
UID:submissions.pasc-conference.org_PASC26_sess169_pap126@linklings.com
SUMMARY:Multi-Artifact Analysis of Self-Admitted Technical Debt in Scienti
 fic Software
DESCRIPTION:Eric Melin (Boise State University, Oak Ridge National Laborat
 ory); Nasir Eisty (University of Tennessee); Gregory Watson (Oak Ridge Nat
 ional Laboratory); and Addi Malviya-Thakur (Oak Ridge National Laboratory,
  University of Tennessee)\n\nContext: Self-admitted technical debt (SATD) 
 occurs when developers acknowledge shortcuts in code. In scientific softwa
 re (SSW), such debt poses unique risks to the validity and reproducibility
  of results. Objective: This study aims to identify, categorize, and evalu
 ate scientific debt, a specialized form of SATD in SSW, and assess the ext
 ent to which traditional SATD categories capture these domain-specific iss
 ues. Method: We conduct a multi-artifact analysis across code comments, co
 mmit messages, pull requests, and issue trackers from 23 open-source SSW p
 rojects. We construct and validate a curated dataset of scientific debt, d
 evelop a multi-source SATD classifier to guide SATD management, and conduc
 t a practitioner validation to assess the practical relevance of scientifi
 c debt. Results: Our classifier performs strongly across 900,358 artifacts
  from 23 SSW projects. SATD is most prevalent in pull requests and issue t
 rackers, 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 scie
 ntific debt is both recognizable and useful in practice. Conclusions: Scie
 ntific debt represents a unique form of SATD in SSW that that is not adequ
 ately captured by traditional categories and requires specialized identifi
 cation and management. Our dataset, classification analysis, and practitio
 ner validation results provide the first formal multi-artifact perspective
  on scientific debt, highlighting the need for tailored SATD detection app
 roaches in SSW.\n\nSession Chair: Razvan Vass (Max Planck Society)\n\n
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