184 citations · 455 across the 23 of their papers we have counts for
6 papers · 1 filter
FlakiMe: Laboratory-Controlled Test Flakiness Impact Assessment. A Case Study on Mutation Testing and Program Repair
Maxime Cordy, Renaud Rwemalika, Mike Papadakis +1
Much research on software testing makes an implicit assumption that test failures are deterministic such that they always witness the presence of the same defects. However, this as…
Automatic Testing and Improvement of Machine Translation
Zeyu Sun, Jie M. Zhang, Mark Harman +2
This paper presents TransRepair, a fully automatic approach for testing and repairing the consistency of machine translation systems. TransRepair combines mutation with metamorphic…
A Survey of Constrained Combinatorial Testing
Huayao Wu, Changhai Nie, Justyna Petke +2
Combinatorial Testing (CT) is a potentially powerful testing technique, whereas its failure revealing ability might be dramatically reduced if it fails to handle constraints in an…
Machine Learning Testing: Survey, Landscapes and Horizons
Jie M. Zhang, Mark Harman, Lei Ma +1
This paper provides a comprehensive survey of Machine Learning Testing (ML testing) research. It covers 144 papers on testing properties (e.g., correctness, robustness, and fairnes…
Sub-Turing Islands in the Wild
Earl T. Barr, David W. Binkley, Mark Harman +1
Recently, there has been growing debate as to whether or not static analysis can be truly sound. In spite of this concern, research on techniques seeking to at least partially answ…
Model Validation Using Mutated Training Labels: An Exploratory Study
Jie M. Zhang, Mark Harman, Benjamin Guedj +2
We introduce an exploratory study on Mutation Validation (MV), a model validation method using mutated training labels for supervised learning. MV mutates training data labels, ret…