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Defect Backlog Size Prediction for Open-Source Projects with the Autoregressive Moving Average and Exponential Smoothing Models

Anioła Paulina, Pandey Sushant Kumar, Staron Miroslaw, Ochodek Mirosław

Annals of Computer Science and Information Systems

2023

Vol. 35

83--92

Context: predicting the number of defects in a defect backlog in a given time horizon can help allocate project resources and organize software development. Goal: to compare the accuracy of three defect backlog prediction methods in the context of large open-source (OSS) projects, i.e., ARIMA, Exponential Smoothing (ETS), and the state-of-the-art method developed at Ericsson AB (SM). Method: we perform a simulation study on a sample of 20 open-source projects to compare the prediction accuracy of the methods. Also, we use the Na\"{\i}ve prediction method as a baseline for sanity check. We use statistical inference tests and effect size coefficients to compare the prediction errors. Results: ARIMA, ETS, and SM were more accurate than the Na\"{\i}ve method. Also, the prediction errors were statistically lower for ETS than for SM (however, the effect size was negligible). Conclusions: ETS seems slightly more accurate than SM when predicting defect backlog size of OSS projects.