Weifeng Sun
Jiangsu University
12 Papers
10 Citations
Weifeng Sun is an academic researcher from Jiangsu University. The author has contributed to research in topics: Computer science & Test case. The author has an hindex of 3, co-authored 10 publications.
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Papers
A Survey on Adaptive Random Testing
TL;DR: A comprehensive survey on ART is provided, classifying techniques, summarizing application areas, and analyzing experimental evaluations, and addressing some misconceptions about ART are addressed.
Poster: Is Euclidean Distance the best Distance Measurement for Adaptive Random Testing?
Rubing Huang,Chenhui Cui,Weifeng Sun,Dave Towey +3 more
- 01 Oct 2020
TL;DR: A series of simulations are conducted to investigate the impact that the Euclidean distance, and its many variations, has on the testing effectiveness of DART, and show that when the dimensionality of the input domain is low, the EuclIDEan distance may indeed be a good choice, however, when thedimensionality is high, it appears to be less suitable.
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Abstract Test Case Prioritization Using Repeated Small-Strength Level-Combination Coverage
TL;DR: A new family of Repeated Small-strength Level-combination Coverage-based Prioritization techniques, RSLCP (RSLCP), that repeatedly achieves the full combination coverage at lower strengths is proposed.
On the Selection of Strength for Fixed-Strength Interaction Coverage Based Prioritization
Rubing Huang,Weiwen Zong,Tsong Yueh Chen,Dave Towey,Jinfu Chen,Yunan Zhou,Weifeng Sun +6 more
- 18 Jun 2018
TL;DR: An empirical study involving four real-life programs and a proposed fixed-strength interaction coverage based prioritization algorithm indicates that λ should be set approximately equal to a value corresponding to half of the number of parameters, when testing resources are sufficient.
Identification of Failure Regions for Programs With Numeric Inputs
Rubing Huang,Weifeng Sun,Tsong Yueh Chen,Sebastian Ng,Jinfu Chen +4 more
- 01 Aug 2021
TL;DR: A new IFR strategy, namely Search for Boundary (SB), is introduced, to identify an approximate failure region of a numeric input domain and the results show that the methods can effectively identify a failure region, within the limited testing resources.
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