Technical debt (TD) refers to suboptimal code resulting from compromises made by developers to meet business budgets or time constraints. Existing studies mainly focus either on code or comments, which restricts their applicability. Additionally, current research often overlooks the granularity of TD, which can lead to inaccurate identification and inefficient remediation of technical debt. To address these challenges, this paper introduces TDD-TCNN, a novel deep-learning approach for method-level TD detection through source code and comments from multiple perspectives. We first collect method-level source code and comments from nine open-source projects to construct a large method-level technical debt dataset, containing 103,006 validated Java methods, all of which have undergone thorough manual verification. Afterward, we use the dataset to train a deep learning model combining Convolutional Neural Network and Transformer for method-level TD detection, integrating local syntactic features with global contextual dependencies from code snippets to enhance accuracy and robustness. Experimental results demonstrate our approach achieves state-of-the-art performance, with precision of 83.26%, recall of 80.84%, and F1-score of 81.92%. This work provides developers with actionable code quality improvement suggestions through precise TD localization at the method level.
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