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arXiv 2408.08095cs.SE

评估代码技术债务预测中的时间依赖方法与季节性效应

Evaluating Time-Dependent Methods and Seasonal Effects in Code Technical Debt Prediction

Mikel Robredo, Nyyti Saarimaki, Matteo Esposito, Davide Taibi, Rafael Penaloza, Valentina Lenarduzzi

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AI总结：

本研究评估时间依赖模型与季节性效应对代码技术债务预测的影响，通过31个Java项目训练11个模型，发现ARIMAX最优且季节性有微弱提升，行业对短期至中期预测兴趣浓厚。

摘要：

背景：代码技术债务（Code TD）预测近年来在软件工程研究中受到广泛关注。然而，目前尚无标准化的代码技术债务预测方法能够完全捕捉影响其演化的因素。目标：本研究旨在评估时间依赖模型和季节性效应对代码技术债务预测的影响。我们将此类模型与广泛使用的机器学习模型进行对比，同时考虑季节性对预测性能的影响。方法：我们使用31个Java开源项目训练了11个预测模型。为评估其性能，我们预测了SQALE指数的未来观测值。为评估我们技术债务预测模型的实际可用性及其对从业者的影响，我们调查了23位软件工程专业人士。结果：我们的研究证实了时间依赖技术的优势，其中ARIMAX模型表现优于其他模型。季节性效应改善了预测性能，尽管影响幅度较为温和。ARIMAX/SARIMAX模型展现出提供均衡长期预测的能力。调查显示，行业对短期至中期技术债务预测有强烈兴趣。结论：我们的研究结果支持在历史软件度量数据中采用捕捉时间依赖性的技术，特别是针对代码技术债务。有效解决这一证据需要采用考虑时间模式的方法。

英文摘要：

Background. Code Technical Debt (Code TD) prediction has gained significant attention in recent software engineering research. However, no standardized approach to Code TD prediction fully captures the factors influencing its evolution. Objective. Our study aims to assess the impact of time-dependent models and seasonal effects on Code TD prediction. It evaluates such models against widely used Machine Learning models, also considering the influence of seasonality on prediction performance. Methods. We trained 11 prediction models with 31 Java open-source projects. To assess their performance, we predicted future observations of the SQALE index. To evaluate the practical usability of our TD forecasting model and its impact on practitioners, we surveyed 23 software engineering professionals. Results. Our study confirms the benefits of time-dependent techniques, with the ARIMAX model outperforming the others. Seasonal effects improved predictive performance, though the impact remained modest. \ReviewerA{ARIMAX/SARIMAX models demonstrated to provide well-balanced long-term forecasts. The survey highlighted strong industry interest in short- to medium-term TD forecasts. Conclusions. Our findings support using techniques that capture time dependence in historical software metric data, particularly for Code TD. Effectively addressing this evidence requires adopting methods that account for temporal patterns.

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