Empirical Research for Self-Admitted Technical Debt Detection in Blockchain Software Projects

doi:10.23940/ijpe.22.03.p1.149157

Int J Performability Eng ›› 2022, Vol. 18 ›› Issue (3): 149-157.doi: 10.23940/ijpe.22.03.p1.149157

Empirical Research for Self-Admitted Technical Debt Detection in Blockchain Software Projects

Yubin Qu^a,b, W. Eric Wong^c,*, and Dongcheng Li^c

^aGuangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin, 541004, China;
^bSchool of Information Engineering, Jiangsu College of Engineering and Technology, Nantong, 226001, China;
^cDepartment of Computer Science, University of Texas at Dallas, Richardson, 75080, USA

Contact: * E-mail address: ewong@utdallas.edu
About author:Yubin Qu received the B.S. and M.S. degrees in Computer Science and Technology from Henan Polytechnic University in China in 2004 and 2008. Since 2009, he has been a lecture with Information Engineering Institute, Jiangsu College of Engineering and Technology. He is the author of more than 10 articles. His research interests include software maintenance, software testing and machine learning.
W. Eric Wong received the M.S. and Ph.D. degrees in computer science from Purdue University. He is currently a Full Professor and the Founding Director of the Advanced Research Center for Software Testing and Quality Assurance in Computer Science, The University of Texas at Dallas (UTD). He also has an appointment as a Guest Researcher with the National Institute of Standards and Technology (NIST), Agency of the US Department of Commerce. Prior to joining UTD, he was with Telcordia Technologies (formerly Bellcore) as a Senior Research Scientist and the Project Manager, where he was in charge of dependable telecom software development. In 2014, he was named the IEEE Reliability Society Engineer of the Year. His research interest includes helping practitioners improve the quality of software while reducing the cost of production. In particular, he is working on software testing, debugging, risk analysis/metrics, safety, and reliability. He has very strong experience developing real-life industry applications of his research results. He is the Editor-in-Chief of IEEE TRANSACTIONS ON RELIABILITY. He is also the Found-ing Steering Committee Chair of the IEEE International Conference on Software Quality, Reliability, and Security (QRS).
Dongcheng Li received the BS degree in computer science from University of Illinois at Springfield and the MS degree in software engineering from the University of Texas at Dallas. He is currently working toward the PhD degree at the University of Texas at Dallas. His research focus is on search-based software engineering and intelligent optimization algorithms.

Abstract

Abstract: Blockchain technology has been used in various fields including digital currencies, distributed storage, and more. The issue of SATD detection for open source blockchain software systems has not been studied. We provide an in-depth analysis of the code comments of open blockchain source software projects. A pre-trained model based on cost-sensitivity is proposed, which is compared with the baseline model on several evaluation metrics. The results were statistically analyzed. The results show that the pre-trained model based on natural language understanding is able to achieve better classification performance. Our method improves 102% over the baseline method in the F1 metric.

Key words: self-admitted technical debt, blockchain, pretrained model

Yubin Qu, W. Eric Wong, and Dongcheng Li. Empirical Research for Self-Admitted Technical Debt Detection in Blockchain Software Projects [J]. Int J Performability Eng, 2022, 18(3): 149-157.

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Empirical Research for Self-Admitted Technical Debt Detection in Blockchain Software Projects

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