# Benchmarking Machine Learning Technologies for Software Defect Detection

arXiv: https://arxiv.org/abs/1506.07563 · HTML: https://mlsanity.com/paper/1506.07563/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jun 2015.

## Built on by (most cited first, 4 of 4)

- [A Meta-analytical Comparison of Naive Bayes and Random Forest for Software Defect Prediction](https://mlsanity.com/paper/2306.15369/) (2023)
- [Advancing Machine Learning in Industry 4.0: Benchmark Framework for Rare-event Prediction in Chemical Processes](https://mlsanity.com/paper/2409.00485/) (2024)
- [DeMuVGN: Effective Software Defect Prediction Model by Learning Multi-view Software Dependency via Graph Neural Networks](https://mlsanity.com/paper/2410.19550/) (2024)
- [Reproducibility of Machine Learning-Based Fault Detection and Diagnosis for HVAC Systems in Buildings: An Empirical Study](https://mlsanity.com/paper/2508.00880/) (2025)

## Abstract (arXiv)

Machine Learning approaches are good in solving problems that have less information. In most cases, the software domain problems characterize as a process of learning that depend on the various circumstances and changes accordingly. A predictive model is constructed by using machine learning approaches and classified them into defective and non-defective modules. Machine learning techniques help developers to retrieve useful information after the classification and enable them to analyse data from different perspectives. Machine learning techniques are proven to be useful in terms of software bug prediction. This study used public available data sets of software modules and provides comparative performance analysis of different machine learning techniques for software bug prediction. Results showed most of the machine learning methods performed well on software bug datasets.