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cs.SE2026
Quality-Driven Selective Mutation for Deep Learning
Zaheed Ahmed, Emmanuel Charleson Dapaah, Philip Makedonski +1
Mutants support testing and debugging in two roles: (i) as test goals and (ii) as substitutes for real faults. Hard-to-kill mutants provide better guidance for test improvement, wh…
cs.SE2025
An Empirical Study of the Realism of Mutants in Deep Learning
Zaheed Ahmed, Philip Makedonski, Jens Grabowski
The core assumption behind using mutants as substitutes for real faults is that they represent them faithfully. In traditional software testing, this representativeness has been em…
cs.SE2020
A Fine-grained Data Set and Analysis of Tangling in Bug Fixing Commits
Steffen Herbold, Alexander Trautsch, Benjamin Ledel +45
Context: Tangled commits are changes to software that address multiple concerns at once. For researchers interested in bugs, tangled commits mean that they actually study not only…