Anticipating bugs: Ticket-level bug prediction and temporal proximity effects

Software bugs significantly impact project time, budgets, and safety, motivating extensive research in bug prediction. The primary goal of bug prediction is to optimize testing efforts by focusing on software fragments, i.e., classes, methods, commits (i.e., Just-In-Time or JIT), or lines of code, most likely to be buggy. However, these predictions are made only after defects have already been introduced. Thus, the current bug prediction approaches support fixing rather than prevention. Motivated by the principle of "prevention is better than cure," 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 seven different families: code, developer, external temperature, internal temperature, intrinsic, ticket to tickets, and JIT. Our TLP evaluation uses a sliding-window approach, balancing feature selection and three machine-learning bug prediction classifiers on about 10,000 tickets of two Apache open-source projects. Our results show that TLP accuracy increases with proximity, con- firming the expected trade-off between early prediction and accuracy. Regarding the prediction power of feature families, no single feature family dominates across stages; developer-centric signals are most informative early, whereas code and JIT metrics prevail near closure, and temperature-based features provide complementary value throughout. Our findings complement and extend the literature on bug prediction at the class, method, or commit level by showing that defect predic- tion can be effectively moved upstream, offering opportunities for risk-aware ticket triaging and developer assignment before any code is written.