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Supplement-material-EAM-EASE25

This repository contains the supplement material for the paper "Critical Considerations on Effort-aware Software Defect Prediction Metrics", submitted for review to the EASE 2025 conference.

The directories contain the cost efficiency curves and result tables for the Logistic Regression models trained on the NASA datasets, and the Random Forest regression models trained on some datasets from the J&M collection.

Each directory contains a read_me.txt file, which presents more detailed information about the content of that specific folder.

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This repository contains the supplement material for the paper "Critical Considerations on Effort-aware Software Defect Prediction Metrics", submitted for review to the EASE 2025 conference.

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