3 papers
cs.LG2026
Training Dynamics of Neural Software Defect Predictors under Coupled Data-Quality Issues
Emmanuel Charleson Dapaah, Philip Makedonski, Jens Grabowski
Context: Software defect prediction supports maintenance decisions such as testing prioritization, release-risk assessment, and quality monitoring. However, metric-based SDP datase…
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
Mutation analysis is a well-established technique for assessing test quality in the traditional software development paradigm by injecting artificial faults into programs. Its appl…