Three-Stage Adjusted Regression Forecasting for Software Defect Prediction
Shadow Pritchard, Bhaskar Mitra, Vidhyashree Nagaraju · 2024
Software reliability growth models (SRGM) [1] enable failure data collected during testing. Specifically, nonhomogeneous Poisson process (NHPP) [2] SRGM are the most commonly employed models. While software reliability growth models are important, efficient modeling of complex software systems increases the complexity of models [3]. Increased model complexity presents a challenge in identifying robust and computationally efficient algorithms to identify model parameters and reduces the generalizability of the models. Existing studies on traditional software reliability growth models [4] suggest that NHPP models characterize defect data as a smooth continuous curve and fail to capture changes in the defect discovery process. Therefore, the model fits well under ideal conditions, but it is not adaptable and will only fit appropriately shaped data. Neural networks and other machine learning methods have been applied to greater effect [5], however limited due to lack of large samples of defect data especially at earlier stages of testing. In this paper, a three-stage adjusted regression forecasting model is proposed to forecast the local regression model [6]. This is a growth curve approximation model that predicts the parameters for a future linear model based on a sliding window of previous linear models. The three stages of the model are as follows: •Initial fit: Train regression models on a sliding window of the date and record model coefficients. •Prediction: Fit new regression models to the coefficient lists and predict the value of the next coefficient. •Error correction: Correct coefficient prediction error using the residual of the last point of the coefficient list and moving average. The resulting model from the multi-stage process is a forecast of the local regression model that represents the future window of data and is referred to as the predicted line. Results suggest the three-stage model demonstrates better prediction capability compared to existing solutions.