# The Impact of Using Regression Models to Build Defect Classifiers

arXiv: https://arxiv.org/abs/2202.06157 · HTML: https://mlsanity.com/paper/2202.06157/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 46 references that are in the map.
- First posted on arXiv 12 Feb 2022.

## Built on by (most cited first, 5 of 5)

- [Model Performance-Guided Evaluation Data Selection for Effective Prompt Optimization](https://mlsanity.com/paper/2505.10736/) (2025)
- [A Comprehensive Framework for Evaluating API-oriented Code Generation in Large Language Models](https://mlsanity.com/paper/2409.15228/) (2024)
- [DeMuVGN: Effective Software Defect Prediction Model by Learning Multi-view Software Dependency via Graph Neural Networks](https://mlsanity.com/paper/2410.19550/) (2024)
- [On The Impact of Merge Request Deviations on Code Review Practices](https://mlsanity.com/paper/2506.08860/) (2025)
- [Application of machine learning to monster level prediction in tabletop RPG game design](https://mlsanity.com/paper/2607.09196/) (2026)

## Abstract (arXiv)

It is common practice to discretize continuous defect counts into defective and non-defective classes and use them as a target variable when building defect classifiers (discretized classifiers). However, this discretization of continuous defect counts leads to information loss that might affect the performance and interpretation of defect classifiers. Another possible approach to build defect classifiers is through the use of regression models then discretizing the predicted defect counts into defective and non-defective classes (regression-based classifiers). In this paper, we compare the performance and interpretation of defect classifiers that are built using both approaches (i.e., discretized classifiers and regression-based classifiers) across six commonly used machine learning classifiers (i.e., linear/logistic regression, random forest, KNN, SVM, CART, and neural networks) and 17 datasets. We find that: i) Random forest based classifiers outperform other classifiers (best AUC) for both classifier building approaches; ii) In contrast to common practice, building a defect classifier using discretized defect counts (i.e., discretized classifiers) does not always lead to better performance. Hence we suggest that future defect classification studies should consider building regression-based classifiers (in particular when the defective ratio of the modeled dataset is low). Moreover, we suggest that both approaches for building defect classifiers should be explored, so the best-performing classifier can be used when determining the most influential features.