Anticipating Bugs: Ticket-Level Bug Prediction and Temporal Proximity Effects
Daniele La Prova, Emanuele Gentili, Davide Falessi

TL;DR
This paper introduces Ticket-Level Prediction (TLP), a novel approach to predict bugs at the ticket level throughout the ticket lifecycle, demonstrating that prediction accuracy improves as tickets approach closure and highlighting the evolving importance of different feature types.
Contribution
The paper presents TLP, a new bug prediction method at the ticket level that considers temporal stages, and analyzes feature importance changes over the ticket lifecycle.
Findings
Prediction accuracy increases as tickets progress towards closure.
Developer signals are most informative early, while code and JIT metrics dominate near closure.
Temperature features provide consistent complementary information.
Abstract
The primary goal of bug prediction is to optimize testing efforts by focusing on software fragments, i.e., classes, methods, commits (JIT), or lines of code, most likely to be buggy. However, these predicted fragments already contain bugs. Thus, the current bug prediction approaches support fixing rather than prevention. The aim of this paper is to introduce and evaluate Ticket-Level Prediction (TLP), an approach to identify tickets that will introduce bugs once implemented. We analyze TLP at three temporal points, each point represents a ticket lifecycle stage: Open, In Progress, or Closed. We conjecture that: (1) TLP accuracy increases as tickets progress towards the closed stage due to improved feature reliability over time, and (2) the predictive power of features changes across these temporal points. Our TLP approach leverages 72 features belonging to six different families: code,…
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Taxonomy
TopicsPhytoplasmas and Hemiptera pathogens · Entomopathogenic Microorganisms in Pest Control · Hemiptera Insect Studies
