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Recent Findings from Taishan University Provides New Insights into Safety Engineering (Two-Stage Cost-Sensitive Learning for Software Defect...

August 7, 2014



Recent Findings from Taishan University Provides New Insights into Safety Engineering (Two-Stage Cost-Sensitive Learning for Software Defect Prediction)

By a News Reporter-Staff News Editor at Computer Weekly News -- Researchers detail new data in Safety Engineering. According to news originating from Shandong, People's Republic of China, by VerticalNews correspondents, research stated, "Software defect prediction (SDP), which classifies software modules into defect-prone and not-defect-prone categories, provides an effective way to maintain high quality software systems. Most existing SDP models attempt to attain lower classification error rates other than lower misclassification costs."

Our news journalists obtained a quote from the research from Taishan University, "However, in many real-world applications, misclassifying defect-prone modules as not-defect-prone ones usually leads to higher costs than misclassifying not-defect-prone modules as defect-prone ones. In this paper, we first propose a new two-stage cost-sensitive learning (TSCS) method for SDP, by utilizing cost information not only in the classification stage but also in the feature selection stage. Then, specifically for the feature selection stage, we develop three novel cost-sensitive feature selection algorithms, namely, Cost-Sensitive Variance Score (CSVS), Cost-Sensitive Laplacian Score (CSLS), and Cost-Sensitive Constraint Score (CSCS), by incorporating cost information into traditional feature selection algorithms. The proposed methods are evaluated on seven real data sets from NASA projects. Experimental results suggest that our TSCS method achieves better performance in software defect prediction compared to existing single-stage cost-sensitive classifiers."

According to the news editors, the research concluded: "Also, our experiments show that the proposed cost-sensitive feature selection methods outperform traditional cost-blind feature selection methods, validating the efficacy of using cost information in the feature selection stage."

For more information on this research see: Two-Stage Cost-Sensitive Learning for Software Defect Prediction. IEEE Transactions on Reliability, 2014;63(2):676-686. IEEE Transactions on Reliability can be contacted at: Ieee-Inst Electrical Electronics Engineers Inc, 445 Hoes Lane, Piscataway, NJ 08855-4141, USA. (Institute of Electrical and Electronics Engineers - www.ieee.org/; IEEE Transactions on Reliability - ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=24)

The news correspondents report that additional information may be obtained from M.X. Liu, Taishan Univ, Sch Informat Sci & Technol, Tai An 271021, Shandong, People's Republic of China. Additional authors for this research include L.S. Miao and D.Q. Zhang.

Keywords for this news article include: Asia, Shandong, Software, Safety Engineering, People's Republic of China

Our reports deliver fact-based news of research and discoveries from around the world. Copyright 2014, NewsRx LLC


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Source: Computer Weekly News


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