Wong, W. Eric

Permanent URI for this collectionhttps://hdl.handle.net/10735.1/6779

W. Eric Wong is a Professor of Computer Science and Director of International Outreach for the Jonsson School. He is also the Director of the Advanced Research Center for Software Testing and Quality Assurance. In 2015 the IEEE Reliability Society named him "Engineer of the Year" for his key contributions in software testing and quality assurance. In 2016, he became Editor-in-chief of IEEE's Transactions on Reliability. His research interests include:

  • Reduce the cost of software production while improving software dependability, safety, and quality.
  • Research Program-Based Testing, Debugging, Reliability, Safety, and Amalysis.
  • Study Architecture/Design-Based Testing, Debugging, Metrics, and Analysis.
  • Develop "real-life industry applications from academic research results."

ORCID page


Recent Submissions

Now showing 1 - 4 of 4
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    Using Tri-Relation Networks for Effective Software Fault-Proneness Prediction
    (Institute of Electrical and Electronics Engineers Inc., 2019-05-15) Li, Y.; Wong, W. Eric; Lee, Shou-Yu; Wotawa, F.; 0000-0002-1021-4753 (Wong, WE); 65810187 (Wong, WE); Wong, W. Eric; Lee, Shou-Yu
    Software modules and developers are two core elements during the process of software development. Previous studies have shown that analyzing relations between 1) software modules; (2) developers; and (3) modules and developers, is critical to understand how they interact with each other, which ultimately affects software quality. Specifically, relations such as developer contribution relation, module dependency relation, and developer collaboration relation have been used independently or in pairs to build networks for software fault-proneness prediction. However, none of them investigate the joint effort of these three relations. Bearing this in mind, in this paper, we propose a tri-relation network, a weighted network that integrates developer contribution, module dependency, and developer collaboration relations to study their combined impact on software quality. Four network node centrality metrics are further derived from the proposed network to predict the fault-proneness of a given software module at the file level. Moreover, we have explored a mechanism to refine current networks in order to further improve the effectiveness of software fault-proneness prediction. © 2013 IEEE.
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    A Bibliometric Assessment of Software Engineering Scholars and Institutions (2010-2017)
    (Elsevier Science Inc, 2018-10-22) Karanatsiou, Dimitra; Li, Yihao; Arvanitou, Elvira-Maria; Misirlis, Nikolaos; Wong, W. Eric; Li, Yihao; Wong, W. Eric
    This paper presents the findings of a bibliometric study, targeting an eight-year period (2010-2017), with the aim of identifying: (a) emerging research directions, (b) the top-20 institutions, and (c) top-20 early stage, consolidated, and experienced scholars in the field of software engineering. To perform this goal, we performed a bibliometric study, by applying the mapping study technique on top-quality software engineering venues, and developed a dataset of 14,456 primary studies. As the ranking metric for institutions, we used the count of papers in which authors affiliated with this institute have been identified in the obtained dataset, whereas regarding scholars we computed the corresponding rankings based on the number of published papers and the average number of citations. Finally, we identified the top-20 rising scholars in the SE research community, based on their recent publication record (between 2015 and 2017) and their research age.
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    Improving Software Testing Education via Industry Sponsored Contests
    (Institute of Electrical and Electronics Engineers Inc.) Wong, W. Eric; Hu, Linghuan; Wang, Haolang; Chen, Z.; Wong, W. Eric; Hu, Linghuan; Wang, Haolang
    This Innovative Practice, Work in Progress Paper presents how we improve software testing education via industry sponsored contests. Over the past decades, we have built software to improve our efficiency, reliability, and safety in production, business, daily life, etc. These goals, however, cannot be accomplished if the software is not properly tested. Some universities provide classes to teach students the fundamental knowledge and techniques of software testing. However, these classes often ignore industry practices and can hardly offer real-world testing experiences to students. To address this, we partnered with industry sponsors to design and host several software testing contests along with software testing tutorials. Through the contests and tutorials, we brought real-world testing and tool experience to the students and provided excellent opportunities for them to practice their learned testing techniques to overcome industry testing challenges. ©2018 IEEE.
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    MSeer: An Advanced Technique for Locating Multiple Bugs in Parallel
    (ACM) Gao, Ruizhi; Wong, W. Eric; 65810187 (Wong, WE); Gao, Ruizhi; Wong, W. Eric
    In practice, a program may contain multiple bugs. The simultaneous presence of these bugs may deteriorate the effectiveness of existing fault-localization techniques to locate program bugs. While it is acceptable to use all failed and successful tests to identify suspicious code for programs with exactly one bug, it is not appropriate to use the same approach for programs with multiple bugs because the due-to relationship between failed tests and underlying bugs cannot be easily identified. One solution is to generate fault-focused clusters by grouping failed tests caused by the same bug into the same clusters. We propose MSeer - an advanced fault localization technique for locating multiple bugs in parallel. Our major contributions include the use of (1) a revised Kendall tau distance to measure the distance between two failed tests, (2) an innovative approach to simultaneously estimate the number of clusters and assign initial medoids to these clusters, and (3) an improved K-medoids clustering algorithm to better identify the due-to relationship between failed tests and their corresponding bugs. Case studies on 840 multiple-bug versions of seven programs suggest that MSeer performs better in terms of effectiveness and efficiency than two other techniques for locating multiple bugs in parallel.

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