Taşer, Pelin Yıldırım2023-03-222023-03-2220220266-47201468-0394https://doi.org/10.1111/exsy.13044https://hdl.handle.net/20.500.14034/620Software bug prediction aims to enhance software quality and testing efficiency by constructing predictive classification models using code properties. This enables the prompt detection of fault-prone modules. There are several machine learning-based software bug prediction studies, which mainly focus on single view data by disregarding the natural ordering relation among the class labels in the literature. Thus, these studies cause losing each view's own intrinsic structure and the inherent order of the labels that positively affect the prediction performance. To overcome this drawback, this study focuses on integrating ordering information and a multi-view learning strategy. This paper proposes a novel approach multi-view ordinal classification (MVOC), which learns from different views (complexity, coupling, cohesion, inheritance and scale) of the software dataset separately and predicts software bugs taking the inherent order of class labels (non-buggy, less buggy and more buggy) into consideration. To demonstrate its prediction performance, the MVOC approach was executed on the 40 different real-world software datasets using six different classification algorithms as base learners. In the experiments, the MVOC approach was compared with traditional classifiers and their multi-view implementations in terms of precision, recall, f-measure and accuracy rate metrics. The results indicate that the MVOC approach presents better prediction performance on average than the multi-view-based and traditional classifiers. It is also observed from the results that the MVOC.RF model achieved the highest classification performance with an average accuracy rate of 85.65%.eninfo:eu-repo/semantics/closedAccessmachine learningmulti-view learningordinal classificationsoftware bug predictionsoftware engineeringArticle10.1111/exsy.13044397Q2WOS:0008002883000012-s2.0-85130628729Q2