This study explores a novel approach to predicting key bug-related outcomes,
including the time to resolution, time to fix, and ultimate status of a bug,
using data from the Bugzilla Eclipse Project. Specifically, we leverage
features available before a bug is resolved to enhance predictive accuracy. Our
methodology incorporates sentiment analysis to derive both an emotionality
score and a sentiment classification (positive or negative). Additionally, we
integrate the bug’s priority level and its topic, extracted using a BERTopic
model, as features for a Convolutional Neural Network (CNN) and a Multilayer
Perceptron (MLP). Our findings indicate that the combination of BERTopic and
sentiment analysis can improve certain model performance metrics. Furthermore,
we observe that balancing model inputs enhances practical applicability, albeit
at the cost of a significant reduction in accuracy in most cases. To address
our primary objectives, predicting time-to-resolution, time-to-fix, and bug
destiny, we employ both binary classification and exact time value predictions,
allowing for a comparative evaluation of their predictive effectiveness.
Results demonstrate that sentiment analysis serves as a valuable predictor of a
bug’s eventual outcome, particularly in determining whether it will be fixed.
Cependant, its utility is less pronounced when classifying bugs into more complex
or unconventional outcome categories.

Cet article explore les excursions dans le temps et leurs implications.

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