Evaluating time-dependent methods and seasonal effects in code technical debt prediction

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 their 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. 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.