Intelligent Crowdsourced Testing (PDF)
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Crowdsourced testing is an emerging paradigm that can improve the cost-effectiveness of softwaretesting and accelerate the process, especially for mobile applications. It entrusts testing tasks to online crowdworkers whose diverse testing devices/contexts, experience, and skill sets can significantly contribute to more reliable, cost-effective and efficient testing results. It has already been adopted by many software organizations, including Google, Facebook, Amazon and Microsoft.
This book provides an intelligent overview of crowdsourced testing research and practice. It employs machine learning, data mining, and deep learning techniques to process the data generated during the crowdsourced testing process, to facilitate the management of crowdsourced testing, and to improve the quality of crowdsourced testing.
Zhenyu Chen is the founder of Mooctest (mooctest.net), and he is currently a Professor at the Software Institute, Nanjing University. He received his bachelor and Ph.D. in Mathematics from Nanjing University. He worked as a Postdoctoral Researcher at the School of Computer Science and Engineering, Southeast University, China. His research interests focus on software analysis and testing. He has more than 100 publications in journals and proceedings, including TOSEM, TSE, JSS, SQJ, IJSEKE, ISSTA, ICST, QSIC, etc. He has served as the associated editor for IEEE Transactions on Reliability, PC co-chair of QRS 2016, QSIC 2013, AST2013, IWPD2012, and the programcommittee
Junjie Wang is an associate researcher at the Institute of Software, Chinese Academy of Sciences (ISCAS). She received the PhD degree from ISCAS in 2015. She was a visiting scholar at North Carolina State University from Sep.2017 to Sep.2018 and worked with Prof. Tim Menzies. Her research interests include crowdsourced testing, mining software repositories, and intelligent software engineering. She has more than 20 high-quality publications and has received the ACM SIGSOFT Distinguished Paper Award at ICSE in 2019 and 2020 respectively, as well as IEEE Best Paper Award at QRS in 2019.
Yang Feng received bachelor's and master's degrees in software engineering from Nanjing University in 2011 and 2013, respectively. He obtained the Ph.D. at the University of California, Irvine. He has published more than 30 referred papers and regularly serves PC member and reviewer for international conferences and journals. His current research interests lie in software testing, crowdsourced software engineering, and program analysis.
- Autoren: Qing Wang , Zhenyu Chen , Junjie Wang , Yang Feng
- 2022, 1st ed. 2022, 251 Seiten, Englisch
- Verlag: Springer Nature Singapore
- ISBN-10: 9811696438
- ISBN-13: 9789811696435
- Erscheinungsdatum: 16.06.2022
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- Größe: 6.72 MB
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