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

• URL: http://arxiv.org/abs/2501.04690v1
• Date: Wed, 08 Jan 2025 18:53:50 GMT
• Title: Comparative Analysis of Quantum and Classical Support Vector Classifiers for Software Bug Prediction: An Exploratory Study
• Authors: Md Nadim, Mohammad Hassan, Ashis Kumar Mandal, Chanchal K. Roy, Banani Roy, Kevin A. Schneider,
• Abstract summary: We investigate the practical application of Quantum Support Vectors (QSVC) for detecting buggy software commits. Our technique addresses large datasets in QSVC algorithms by dividing them into smaller subsets. We propose an aggregation method to combine predictions from these models to detect the entire test dataset.
• Score: 8.214986715680737
• License:
• Abstract: 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.

Related papers

• Modeling Quantum Machine Learning for Genomic Data Analysis [12.248184406275405]
  Quantum Machine Learning (QML) continues to evolve, unlocking new opportunities for diverse applications. We investigate and evaluate the applicability of QML models for binary classification of genome sequence data by employing various feature mapping techniques. We present an open-source, independent Qiskit-based implementation to conduct experiments on a benchmark genomic dataset.
  arXiv  Detail & Related papers  (2025-01-14T15:14:26Z)
• Quantum vs. Classical Machine Learning Algorithms for Software Defect Prediction: Challenges and Opportunities [4.2793468776829]
  This study compares the performance of three Quantum Machine Learning (QML) and five classical machine learning (CML) algorithms on software defect datasets. Our investigation reports the comparative scenarios of QML vs. CML algorithms and identifies the better-performing and consistent algorithms to predict software defects. The findings of this study can help practitioners and researchers further progress in this research domain by making software systems reliable and bug-free.
  arXiv  Detail & Related papers  (2024-12-10T17:38:36Z)
• Benchmarking Uncertainty Quantification Methods for Large Language Models with LM-Polygraph [83.90988015005934]
  Uncertainty quantification is a key element of machine learning applications. We introduce a novel benchmark that implements a collection of state-of-the-art UQ baselines. We conduct a large-scale empirical investigation of UQ and normalization techniques across eleven tasks, identifying the most effective approaches.
  arXiv  Detail & Related papers  (2024-06-21T20:06:31Z)
• LLMC: Benchmarking Large Language Model Quantization with a Versatile Compression Toolkit [55.73370804397226]
  Quantization, a key compression technique, can effectively mitigate these demands by compressing and accelerating large language models. We present LLMC, a plug-and-play compression toolkit, to fairly and systematically explore the impact of quantization. Powered by this versatile toolkit, our benchmark covers three key aspects: calibration data, algorithms (three strategies), and data formats.
  arXiv  Detail & Related papers  (2024-05-09T11:49:05Z)
• Unifying (Quantum) Statistical and Parametrized (Quantum) Algorithms [65.268245109828]
  We take inspiration from Kearns' SQ oracle and Valiant's weak evaluation oracle. We introduce an extensive yet intuitive framework that yields unconditional lower bounds for learning from evaluation queries.
  arXiv  Detail & Related papers  (2023-10-26T18:23:21Z)
• Probabilistic Sampling of Balanced K-Means using Adiabatic Quantum Computing [93.83016310295804]
  AQCs allow to implement problems of research interest, which has sparked the development of quantum representations for computer vision tasks. In this work, we explore the potential of using this information for probabilistic balanced k-means clustering. Instead of discarding non-optimal solutions, we propose to use them to compute calibrated posterior probabilities with little additional compute cost. This allows us to identify ambiguous solutions and data points, which we demonstrate on a D-Wave AQC on synthetic tasks and real visual data.
  arXiv  Detail & Related papers  (2023-10-18T17:59:45Z)
• Semisupervised Anomaly Detection using Support Vector Regression with Quantum Kernel [0.0]
  Anomaly detection (AD) involves identifying observations or events that deviate in some way from the rest of the data. This paper introduces an approach to semisupervised AD based on the reconstruction loss of a support vector regression (SVR) with quantum kernel. It is shown that our SVR model with quantum kernel performs better than the SVR with RBF kernel as well as all other models, achieving highest mean AUC over all data sets.
  arXiv  Detail & Related papers  (2023-08-01T15:00:14Z)
• Inter-case Predictive Process Monitoring: A candidate for Quantum Machine Learning? [0.0]
  This work builds upon the recent progress in inter-case Predictive Process Monitoring. It comprehensively benchmarks the impact of inter-case features on prediction accuracy. It includes quantum machine learning models, which are expected to provide an advantage over classical models. The evaluation on real-world training data from the BPI challenge shows that the inter-case features provide a significant boost by more than four percent in accuracy.
  arXiv  Detail & Related papers  (2023-06-30T18:33:45Z)
• A didactic approach to quantum machine learning with a single qubit [68.8204255655161]
  We focus on the case of learning with a single qubit, using data re-uploading techniques. We implement the different proposed formulations in toy and real-world datasets using the qiskit quantum computing SDK.
  arXiv  Detail & Related papers  (2022-11-23T18:25:32Z)
• MQBench: Towards Reproducible and Deployable Model Quantization Benchmark [53.12623958951738]
  MQBench is a first attempt to evaluate, analyze, and benchmark the and deployability for model quantization algorithms. We choose multiple platforms for real-world deployments, including CPU, GPU, ASIC, DSP, and evaluate extensive state-of-the-art quantization algorithms. We conduct a comprehensive analysis and find considerable intuitive or counter-intuitive insights.
  arXiv  Detail & Related papers  (2021-11-05T23:38:44Z)
• Machine Learning Techniques for Software Quality Assurance: A Survey [5.33024001730262]
  We discuss various approaches in both fault prediction and test case prioritization. Recent studies deep learning algorithms for fault prediction help to bridge the gap between programs' semantics and fault prediction features.
  arXiv  Detail & Related papers  (2021-04-29T00:37:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.