The effect of the COVID-19 pandemic on crude oil prices just faded; at this moment, the Russia–Ukraine war brought a new crisis. In this paper, a new application is developed that predicts the change in crude oil prices by incorporating these two global effects. Unlike most existing studies, this work uses a dataset that involves data collected over twenty-two years and contains seven different features, such as crude oil opening, closing, intraday highest value, and intraday lowest value. This work applies cross-validation to predict the crude oil prices by using machine learning algorithms (support vector machine, linear regression, and rain forest) and deep learning algorithms (long short-term memory and bidirectional long short-term memory). The results obtained by machine learning and deep learning algorithms are compared. Lastly, the high-performance estimation can be achieved in this work with the average mean absolute error value over 0.3786.
BACKGROUND: Just-In-Time (JIT) models, unlike the traditional defect prediction models, detect the fix-inducing changes (or defect inducing changes). These models are designed based on the assumption that past code change properties are similar to future ones. However, as the system evolves, the expertise of developers and/or the complexity of the system also change.AIM: In this work, we aim to investigate the effect of code change properties on JIT models over time. We also study the impact of using recent data as well as all available data on the performance of JIT models. Further, we analyze the effect of weighted sampling on the performance of fix-inducing properties of JIT models. For this purpose, we used datasets from four open-source projects, namely Eclipse JDT, Mozilla, Eclipse Platform, and PostgreSQL.
METHOD:We used five families of change code properties such as size, diffusion, history, experience, and purpose. We used Random Forest to train and test the JIT model and Brier Score (BS) and Area Under Curve (AUC) for performance measurement. We applied the Wilcoxon Signed Rank Test on the output to statistically validate whether the performance of JIT models improves using all the available data or the recent data.
RESULTS:Our paper suggest that the predictive power of JIT models does not change by time. Furthermore, we observed that the chronology of data in JIT defect prediction models can be discarded by considering all the
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