Defect Backlog Size Prediction for Open-Source Projects with the Autoregressive Moving Average and Exponential Smoothing Models
[ 1 ] Instytut Informatyki, Wydział Informatyki i Telekomunikacji, Politechnika Poznańska | [ S ] student | [ P ] employee
2023
chapter in monograph / paper
english
EN 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.
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