# Comparative Analysis of Quantum and Classical Support Vector Classifiers for Software Bug Prediction: An Exploratory Study

arXiv: https://arxiv.org/abs/2501.04690 · HTML: https://mlsanity.com/paper/2501.04690/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 60 references that are in the map.
- First posted on arXiv 8 Jan 2025.

## Builds on (most cited first, 20 of 20)

- [Supervised learning with quantum enhanced feature spaces](https://mlsanity.com/paper/1804.11326/) (2018)
- [The power of quantum neural networks](https://mlsanity.com/paper/2011.00027/) (2020)
- [Quantum Machine Learning](https://mlsanity.com/paper/1611.09347/) (2016)
- [Efficient Learning for Deep Quantum Neural Networks](https://mlsanity.com/paper/1902.10445/) (2019)
- [Quantum Long Short-Term Memory](https://mlsanity.com/paper/2009.01783/) (2020)
- [Quantum machine learning: a classical perspective](https://mlsanity.com/paper/1707.08561/) (2017)
- [Quantum-enhanced machine learning](https://mlsanity.com/paper/1610.08251/) (2016)
- [Systematic Literature Review: Quantum Machine Learning and its applications](https://mlsanity.com/paper/2201.04093/) (2022)
- [Quantum machine learning with differential privacy](https://mlsanity.com/paper/2103.06232/) (2021)
- [Quantum autoencoders via quantum adders with genetic algorithms](https://mlsanity.com/paper/1709.07409/) (2017)
- [Supervised Quantum Learning without Measurements](https://mlsanity.com/paper/1612.05535/) (2016)
- [Basic protocols in quantum reinforcement learning with superconducting circuits](https://mlsanity.com/paper/1701.05131/) (2017)
- [Q#: Enabling scalable quantum computing and development with a high-level domain-specific language](https://mlsanity.com/paper/1803.00652/) (2018)
- [A kernel-based quantum random forest for improved classification](https://mlsanity.com/paper/2210.02355/) (2022)
- [SZZ Unleashed: An Open Implementation of the SZZ Algorithm -- Featuring Example Usage in a Study of Just-in-Time Bug Prediction for the Jenkins Project](https://mlsanity.com/paper/1903.01742/) (2019)
- [Statistical Assertions for Validating Patterns and Finding Bugs in Quantum Programs](https://mlsanity.com/paper/1905.09721/) (2019)
- [ApacheJIT: A Large Dataset for Just-In-Time Defect Prediction](https://mlsanity.com/paper/2203.00101/) (2022)
- [Leveraging Structural Properties of Source Code Graphs for Just-In-Time Bug Prediction](https://mlsanity.com/paper/2201.10137/) (2022)
- [Using Quantum Computers to Speed Up Dynamic Testing of Software](https://mlsanity.com/paper/2209.04860/) (2022)
- [Utilizing Source Code Syntax Patterns to Detect Bug Inducing Commits using Machine Learning Models](https://mlsanity.com/paper/2212.03399/) (2022)

## Built on by (most cited first, 1 of 1)

- [Maritime object classification with SAR imagery using quantum kernel methods](https://mlsanity.com/paper/2512.11367/) (2025)

## Abstract (arXiv)

Purpose: Quantum computing promises to transform problem-solving across various domains with rapid and practical solutions. Within Software Evolution and Maintenance, Quantum Machine Learning (QML) remains mostly an underexplored domain, particularly in addressing challenges such as detecting buggy software commits from code repositories. Methods: In this study, we investigate the practical application of Quantum Support Vector Classifiers (QSVC) for detecting buggy software commits across 14 open-source software projects with diverse dataset sizes encompassing 30,924 data instances. We compare the QML algorithm PQSVC (Pegasos QSVC) and QSVC against the classical Support Vector Classifier (SVC). Our technique addresses large datasets in QSVC algorithms by dividing them into smaller subsets. We propose and evaluate an aggregation method to combine predictions from these models to detect the entire test dataset. We also introduce an incremental testing methodology to overcome the difficulties of quantum feature mapping during the testing approach. Results: The study shows the effectiveness of QSVC and PQSVC in detecting buggy software commits. The aggregation technique successfully combines predictions from smaller data subsets, enhancing the overall detection accuracy for the entire test dataset. The incremental testing methodology effectively manages the challenges associated with quantum feature mapping during the testing process. Conclusion: We contribute to the advancement of QML algorithms in defect prediction, unveiling the potential for further research in this domain. The specific scenario of the Short-Term Activity Frame (STAF) highlights the early detection of buggy software commits during the initial developmental phases of software systems, particularly when dataset sizes remain insufficient to train machine learning models.