Results: Towards Realistic SATD Identification Through Machine Learning Models: Ongoing Research and Preliminary Results

Automated identification of self-admitted technical debt (SATD) has been crucial for advancements in managing such debt. 
However, state-of-the-arts studies often overlook chronological factors, leading to experiments that do not faithfully replicate the conditions developers face in their daily routines.
This study initiates a chronological analysis of SATD identification through machine learning models, emphasizing the significance of temporal factors in automated SATD detection. 
The research is in its preliminary phase, divided into two stages: evaluating model performance trained on historical data and tested in prospective contexts, and examining model generalization across various projects. Preliminary results reveal that the chronological factor can positively or negatively influence model performance and that some models are not sufficiently general when trained and tested on different projects.