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.自动化识别公认技术债务（self-admitted technical debt, SATD）对于此类债务的管理进阶至关重要。然而，当前前沿研究往往忽略时序因素，导致实验无法真实复现开发者日常工作中面临的实际场景。本研究针对基于机器学习模型的SATD识别任务展开时序维度分析，着重强调时序因素在自动化SATD检测中的重要意义。本研究尚处于初步阶段，分为两个研究环节：一是评估基于历史数据训练、并在前瞻性场景中测试的模型性能；二是考察模型在不同项目间的泛化能力。初步研究结果表明，时序因素可对模型性能产生正向或负向影响，且部分模型在跨项目训练与测试场景下泛化能力不足。