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Improving students’ programming quality with the continuous inspection process: a social coding perspective

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Abstract

College students majoring in computer science and software engineering need to master skills for high-quality programming. However, rich research has shown that both the teaching and learning of high-quality programming are challenging and deficient in most college education systems. Recently, the continuous inspection paradigm has been widely used by developers on social coding sites (e.g., GitHub) as an important method to ensure the internal quality of massive code contributions. This paper presents a case where continuous inspection is introduced into the classroom setting to improve students’ programming quality. In the study, we first designed a specific continuous inspection process for students’ collaborative projects and built an execution environment for the process. We then conducted a controlled experiment with 48 students from the same course during two school years to evaluate how the process affects their programming quality. Our results show that continuous inspection can help students in identifying their bad coding habits, mastering a set of good coding rules and significantly reducing the density of code quality issues introduced in the code. Furthermore, we describe the lessons learned during the study and propose ideas to replicate and improve the process and its execution platform.

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Acknowledgements

We gratefully acknowledge the financial support from National Key R&D Program of China (2018YFB1004202) and the National Natural Science Foundation of China (Grant Nos. 61472430, 61502512, 61532004 and 61379051). We also want to thank our students on their active participation in our study.

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Authors and Affiliations

  1. College of Computer, National University of Defense Technology, Changsha, 410073, China

    Yao Lu, Xinjun Mao, Tao Wang & Gang Yin

  2. Key Laboratory of Software Engineering for Complex Systems, Changsha, 410073, China

    Xinjun Mao

  3. School of Information Science and Engineering, Central South University, Changsha, 410012, China

    Zude Li

Authors
  1. Yao Lu
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  2. Xinjun Mao
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  3. Tao Wang
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  4. Gang Yin
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  5. Zude Li
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Corresponding author

Correspondence to Xinjun Mao.

Additional information

Yao Lu is a doctoral candidate in the College of Computer, National University of Defense Technology, China. His work interests include open source software engineering, data mining, and crowdsourced learning.

Xinjun Mao is a professor in the College of Computer, National University of Defense Technology, China. He received his PhD degree in computer science from National University of Defense Technology, China in 1998. His research interests include software engineering, multi-agent system, robot system, self-adaptive system, and crowdsourcing.

Tao Wang is an assistant professor in the College of Computer, National University of Defense Technology, China. He received his PhD degree in computer science from National University of Defense Technology, China in 2015. His work interests include open source software engineering, machine learning, data mining, and knowledge discovering in open source software.

Gang Yin is an associate professor in the College of Computer, National University of Defense Technology, China. He received his PhD degree in computer science from National University of Defense Technology, China in 2006. He has published more than 60 research papers in international conferences and journals. His current research interests include distributed computing, information security, and software engineering.

Zude Li is an assistant professor at Central South University. He obtained his PhD degree in 2010 from The University of Western Ontario, Canada. His research interests are in the fields of software architecture, evolution and quality. He is appointed as a software engineering expert in SKANE SOFT.

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Lu, Y., Mao, X., Wang, T. et al. Improving students’ programming quality with the continuous inspection process: a social coding perspective. Front. Comput. Sci. 14, 145205 (2020). https://doi.org/10.1007/s11704-019-9023-2

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