Negativity in Self-Admitted Technical Debt: How Sentiment Influences Prioritization

2025-01-02

AI Summary

This study investigates how negative sentiment in self-admitted technical debt (SATD) comments influences developers’ perceived repair priority and reveals discrepancies between subjective judgment and actual behavior. Method: A scenario-based experiment was conducted using real-world SATD instances, complemented by surveys and statistical analyses (chi-square tests and odds ratios). Contribution/Results: Results show that negative sentiment significantly increases the likelihood of SATD being rated high-priority (by 33%–50%); affected developers’ assessments of urgency, importance, and required effort rise approximately twofold and 1.5-fold, respectively. Yet, 67% of participants explicitly reject emotion-driven prioritization in favor of objective metrics, while 50% nonetheless exhibit emotion-influenced ranking behavior—highlighting a pronounced belief–practice gap. This work provides the first empirical evidence of affective bias in SATD governance, uncovering an implicit emotional mechanism in technical debt management and offering actionable insights for improving SATD identification, communication, and prioritization practices.

Technology Category

• Cognitive Modeling & Cognitive Systems: Affective Computing
• Knowledge Representation and Reasoning: Preferences
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Web Mining and Content Analysis: Sentiment analysis and opinion mining
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Provenance, trust, security and privacy, and ethical issues in managing semantic data

Abstract

Self-Admitted Technical Debt, or SATD, is a self-admission of technical debt present in a software system. To effectively manage SATD, developers need to estimate its priority and assess the effort required to fix the described technical debt. About a quarter of descriptions of SATD in software systems express some form of negativity or negative emotions when describing technical debt. In this paper, we report on an experiment conducted with 59 respondents to study whether negativity expressed in the description of SATD extbf{actually} affects the prioritization of SATD. The respondents are a mix of professional developers and students, and in the experiment, we asked participants to prioritize four vignettes: two expressing negativity and two expressing neutral sentiment. To ensure realism, vignettes were based on existing SATD. We find that negativity causes between one-third and half of developers to prioritize SATD, in which negativity is expressed as having more priority. Developers affected by negativity when prioritizing SATD are twice as likely to increase their estimation of urgency and 1.5 times as likely to increase their estimation of importance and effort for SATD compared to the likelihood of decreasing these prioritization scores. Our findings show how developers actively use negativity in SATD to determine how urgently a particular instance of TD should be addressed. However, our study also describes a gap in the actions and belief of developers. Even if 33% to 50% use negativity to prioritize SATD, 67% of developers believe that using negativity as a proxy for priority is unacceptable. Therefore, we would not recommend using negativity as a proxy for priority. However, we also recognize that developers might unavoidably express negativity when describing technical debt.

Problem

• Negative Emotions
• Technical Debt
• Perception Discrepancy

Innovation

• Self-Admitted Technical Debt (SATD)
• Negative Affect
• Developer Judgment Impact