In recent years, we have witnessed an important increase in research focusing on how machine learning (ML) techniques can be used for software quality assessment and improvement. However, the derived methodologies and tools lack transparency, due to the black-box nature of the employed machine learning models, leading to decreased trust in their results. To address this shortcoming, in this paper we extend the state-of-the-art and -practice by building explainable AI models on top of machine learning ones, to interpret the factors (i.e. software metrics) that constitute a module as in risk of having high technical debt (HIGH TD), to obtain thresholds for metric scores that are alerting for poor maintainability, and finally, we dig further to achieve local interpretation that explains the specific problems of each module, pinpointing to specific opportunities for improvement during TD management. To achieve this goal, we have developed project-specific classifiers (characterizing modules as HIGH and NOT-HIGH TD) for 21 open-source projects, and we explain their rationale using the SHapley Additive exPlanation (SHAP) analysis. Based on our analysis, complexity, comments ratio, cohesion, nesting of control flow statements, coupling, refactoring activity, and code churn are the most important reasons for characterizing classes as in HIGH TD risk. The analysis is complemented with global and local means of interpretation, such as metric thresholds and case-by-case reasoning for characterizing a class as in-risk of having HIGH TD. The results of the study are compared against the state-of-the-art and are interpreted from the point of view of both researchers and practitioners.

中文翻译：

---

技术债务识别的本地和全球可解释性


近年来，我们见证了关于如何使用机器学习 (ML) 技术进行软件质量评估和改进的研究的显着增长。然而，由于所采用的机器学习模型的黑盒性质，派生的方法和工具缺乏透明度，导致对其结果的信任度降低。为了解决这个缺点，在本文中，我们通过在机器学习模型之上构建可解释的人工智能模型来扩展最先进的技术和实践，以解释构成模块的风险因素（即软件指标）。具有高技术债务（HIGH TD），以获得对可维护性差发出警报的指标分数阈值，最后，我们进一步挖掘以实现本地解释，解释每个模块的具体问题，精确指出 TD 管理过程中改进的具体机会。为了实现这一目标，我们为 21 个开源项目开发了特定于项目的分类器（将模块描述为 HIGH 和 NOT-HIGH TD），并使用 SHapley Additive exPlanation (SHAP) 分析来解释其基本原理。根据我们的分析，复杂性、注释率、内聚性、控制流语句的嵌套、耦合、重构活动和代码改动是在高 TD 风险中描述类的最重要原因。该分析还辅以全局和本地解释方法，例如度量阈值和用于将某个类别描述为具有高 TD 风险的个案推理。研究结果与最先进的技术进行比较，并从研究人员和从业者的角度进行解释。