An Empirical Study of Software Metrics Diversity for Cross-Project Defect Prediction

Abstract

Cross-project defect prediction (CPDP) is a mainstream method estimating the most defect-prone components of software with limited historical data. Several studies investigate how software metrics are used and how modeling techniques influence prediction performance. However, the software’s metrics diversity impact on the predictor remains unclear. Thus, this paper aims to assess the impact of various metric sets on CPDP and investigate the feasibility of CPDP with hybrid metrics. Based on four software metrics types, we investigate the impact of various metric sets on CPDP in terms of F-measure and statistical methods. Then, we validate the dominant performance of CPDP with hybrid metrics. Finally, we further verify the CPDP-OSS feasibility built with three types of metrics (orient-object, semantic, and structural metrics) and challenge them against two current models. The experimental results suggest that the impact of different metric sets on the performance of CPDP is significantly distinct, with semantic and structural metrics performing better. Additionally, trials indicate that it is helpful for CPDP to increase the software’s metrics diversity appropriately, as the CPDP-OSS improvement is up to 53.8%. Finally, compared with two baseline methods, TCA+ and TDSelector, the optimized CPDP model is viable in practice, and the improvement rate is up to 50.6% and 25.7%, respectively.

Impact of a Synthetic Data Vault for Imbalanced Class in Cross-Project Defect Prediction

“…To effectively tackle CI issues inherent in the datasets, synthetic data is generated using the SDV, a proficient tool in developing generative models within relational databases. SDV facilitates data synthesis by selectively sampling across database components post-model formulation, ensuring adherence to underlying structural constraints [31]. Moreover, the study incorporates the utilization of five classification algorithms, namely DT, LR, KNN, NB, and RF, to conduct a comprehensive assessment of defect prediction effectiveness across multiple projects.…”

“…Within the software defect dataset, most of the data exhibits a significantly larger proportion of non-defective samples compared to defective ones [31]. CI often results in bias within machine learning models towards the majority class [35].…”

Quality Analysis of Software Applications using Software Reliability Growth Models and Deep Learning Models

“…The proposed approach focused on redundancy and extracting the parameters by using principal component analysis (PCA). Yiwen Zhong et al, [17] CPDP-orientobject, semantic, and structural metrics (OSS)…”