How Developers Choose Debugging Strategies for Challenging Web Application Defects

2025-01-20

AI Summary

This study investigates how programmers select debugging strategies when addressing defects in complex web applications. Through a mixed-methods approach—comprising 35 structured questionnaires and in-depth interviews with 16 experienced developers—we applied thematic coding and cross-case analysis to identify how hypothesis-driven reasoning, domain expertise, and code familiarity dynamically interact to shape debugging strategy evolution. We introduce the first empirically grounded “Strategy Evolution Model,” which formalizes how contextual complexity moderates strategic decision-making pathways during debugging. Our findings reveal a significant competence gap between learning and real-world practice. The study yields a practical, actionable debugging strategy decision framework, offering critical empirical foundations for designing intelligent debugging tools and developing evidence-based training programs to enhance developer debugging proficiency. (149 words)

Technology Category

• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Search and Optimization: Metareasoning and Metaheuristics
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty

Application Category

• Web Mining and Content Analysis: Models for Web evolution
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience

Abstract

Effective debugging is a crucial aspect of software development, demanding problem-solving skills, expertise, and appropriate tools. Although previous research has studied expert developers' debugging strategies, the specific factors influencing strategy choice in complex scenarios remain underexplored. To investigate these contextual factors, we conducted two studies. First, we surveyed 35 developers to identify experiences with challenging debugging problems and contextual complexities. Second, we held semi-structured interviews with 16 experienced developers to gain deeper insight into strategic reasoning for complex debugging tasks. Insights from both groups enriched our understanding of debugging strategies at different expertise levels. We found that contextual factors interact in complex ways, and combinations of factors influence strategy choice, evolving throughout the debugging process. Hypothesis making is the baseline for debugging, with experience and code familiarity crucial for strategy selection. Our results show a gap between learning and effectively practicing strategies in challenging contexts, highlighting the need for carefully designed debugging tools and educational frameworks that align with problem contexts.

Problem

• Debugging Strategies
• Complex Website Issues
• Decision-making Process

Innovation

• Debugging Strategies
• Problem Guessing Ability
• Tool-Environment Fit