Non‐negative sparse‐based SemiBoost for software defect prediction. (22nd July 2016)

• Record Type: Journal Article
• Authors: Wang, Tiejian; Zhang, Zhiwu; Jing, Xiaoyuan; Liu, Yanli
• Is Part Of: Software testing, verification & reliability. Volume 26:Number 7(2016)
• Page Start: 498
• Page End: 515
• Publication Date: 2016-07-22
• Subjects: software defect prediction -- semi‐supervised learning -- ensemble learning -- non‐negative sparse based SemiBoost (NSSB); Computer software -- Testing -- Periodicals; Computer software -- Verification -- Periodicals; Computer software -- Reliability -- Periodicals; 005.14
• DOI: 10.1002/stvr.1610
• Languages: English
• ISSNs: 0960-0833

Abstract

Summary: Software defect prediction is an important decision support activity in software quality assurance. The limitation of the labelled modules usually makes the prediction difficult, and the class‐imbalance characteristic of software defect data leads to negative influence on decision of classifiers. Semi‐supervised learning can build high‐performance classifiers by using large amount of unlabelled modules together with the labelled modules. Ensemble learning achieves a better prediction capability for class‐imbalance data by using a series of weak classifiers to reduce the bias generated by the majority class. In this paper, we propose a new semi‐supervised software defect prediction approach, non‐negative sparse‐based SemiBoost learning. The approach is capable of exploiting both labelled and unlabelled data and is formulated in a boosting framework. In order to enhance the prediction ability, we design a flexible non‐negative sparse similarity matrix, which can fully exploit the similarity of historical data by incorporating the non‐negativity constraint into sparse learning for better learning the latent clustering relationship among software modules. The widely used datasets from NASA projects are employed as test data to evaluate the performance of all compared methods. Experimental results show that non‐negative sparse‐based SemiBoost learning outperforms several representative state‐of‐the‐art semi‐supervised software defect prediction methods. Copyright © 2016 John Wiley & Sons, Ltd. Abstract : In this paper, we propose a novel semi‐supervised software defect prediction approach, non‐negative sparse based SemiBoost learning. The approach is capable of exploiting both labeled and unlabeled data via semi‐supervised learning and solving the class‐imbalance problem in software defect prediction via ensemble learning. Meanwhile, we design a flexible non‐negative sparse similarity matrix, which can fully exploit the similarity of historical data.