On the Applicability of Time Series Models for Technical Debt Forecasting

Technical debt (TD) is commonly used to indicate additional costs caused by quality compromises that can yield short-term benefits in the software development process, but may negatively affect the long-term quality of software products. Predicting the future value of TD could facilitate decision-making tasks regarding software maintenance and assist developers and project managers in taking proactive actions regarding TD repayment. However, no notable contributions exist in the field of TD forecasting, indicating that it is a scarcely investigated field. This study constitutes an initial attempt towards this direction. To this end, in the present study, we empirically evaluate the ability of time series analysis to model and predict TD evolution. To create our dataset, we obtain weekly snapshots of five open source software projects over three years and compute their TD values. We find that the autoregressive integrated moving average model ARIMA(0,1,1) can provide accurate predictions over a fairly long time period for all sampled projects. The model can be used to facilitate planning for software evolution budget and time allocation. The approach presented in this paper provides a basis for predictive TD analysis, suitable for projects with a relatively long history