Understanding Debugging as Episodes: A Case Study on Performance Bugs in Configurable Software Systems

Debugging performance bugs in configurable software systems is a complex and time-consuming task that requires not only fixing a bug, but also understanding its root cause. While there is a vast body of literature on debugging strategies, there is no consensus on general debugging. This makes it difficult to provide concrete guidance for developers, especially for configuration-dependent performance bugs. The goal of our work is to alleviate this situation by providing an framework to describe debugging strategies in a more general, unifying way. We conducted a user study with 12 professional developers who debugged a performance bug in a real-world configurable system. To observe developers in an unobstructive way, we provided an immersive virtual reality tool, SoftVR, giving them a large degree of freedom to choose the preferred debugging strategy. The results show that the existing documentation of strategies is too coarse-grained and intermixed to identify successful approaches. In a subsequent qualitative analysis, we devised a coding framework to reason about debugging approaches. With this framework, we identified five goal-oriented episodes that developers employ, which they also confirmed in subsequent interviews. Our work provides a unified description of debugging strategies, allowing researchers a common foundation to study debugging and practitioners and teachers guidance on successful debugging strategies.

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Publication Details

Journal
Proceedings of the ACM on software engineering.
Published
2025-06-19
DOI
https://doi.org/10.1145/3717523
Primary Topic
Software Engineering Research
Type
article
Field-Weighted Citation Impact
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article

Understanding Debugging as Episodes: A Case Study on Performance Bugs in Configurable Software Systems

Norbert Siegmund, Raimund Dachselt, Sven Apel, Alina Mailach et al.
Proceedings of the ACM on software engineering.
Software Engineering Research
article

Understanding Debugging as Episodes: A Case Study on Performance Bugs in Configurable Software Systems

Norbert Siegmund, Raimund Dachselt, Sven Apel, Alina Mailach, Janet Siegmund, Max Weber
article en

Abstract

Debugging performance bugs in configurable software systems is a complex and time-consuming task that requires not only fixing a bug, but also understanding its root cause. While there is a vast body of literature on debugging strategies, there is no consensus on general debugging. This makes it difficult to provide concrete guidance for developers, especially for configuration-dependent performance bugs. The goal of our work is to alleviate this situation by providing an framework to describe debugging strategies in a more general, unifying way. We conducted a user study with 12 professional developers who debugged a performance bug in a real-world configurable system. To observe developers in an unobstructive way, we provided an immersive virtual reality tool, SoftVR, giving them a large degree of freedom to choose the preferred debugging strategy. The results show that the existing documentation of strategies is too coarse-grained and intermixed to identify successful approaches. In a subsequent qualitative analysis, we devised a coding framework to reason about debugging approaches. With this framework, we identified five goal-oriented episodes that developers employ, which they also confirmed in subsequent interviews. Our work provides a unified description of debugging strategies, allowing researchers a common foundation to study debugging and practitioners and teachers guidance on successful debugging strategies.

Proceedings of the ACM on software engineering.Vol. 2(FSE)
Chemnitz University of Technology (DE), Saarland University (DE), Leipzig University (DE)
Openalex Percentile: Top 80%
Software Engineering Research
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Understanding Debugging as Episodes: A Case Study on Performance Bugs in Configurable Software Systems — Norbert Siegmund, Raimund Dachselt, et al. · Proceedings of the ACM on software engineering. (2025) | TGRS Research Map | TGRS