Predicting software defects early in the development process not only enhances the quality and reliability of the software but also decreases the cost of development. A wide range of machine learning techniques can be employed to create software defect prediction models, but the effectiveness and accuracy of these models are often influenced by the choice of appropriate feature subset. Since finding the optimal feature subset is computationally intensive, heuristic and metaheuristic approaches are commonly employed to identify near-optimal solutions within a reasonable time frame. Recently, the quantum computing paradigm quantum annealing (QA) has been deployed to find solutions to complex optimization problems. This opens up the possibility of addressing the feature subset selection problem with a QA machine. Although several strategies have been proposed for feature subset selection using a QA machine, little exploration has been done regarding the viability of a QA machine for feature subset selection in software defect prediction. This study investigates the potential of D-Wave QA system for this task, where we formulate a mutual information (MI)-based filter approach as an optimization problem and utilize a D-Wave Quantum Processing Unit (QPU) solver as a QA solver for feature subset selection. We evaluate the performance of this approach using multiple software defect datasets from the AEEM, JIRA, and NASA projects. We also utilize a D-Wave classical solver for comparative analysis. Our experimental results demonstrate that QA-based feature subset selection can enhance software defect prediction. Although the D-Wave QPU solver exhibits competitive prediction performance with the classical solver in software defect prediction, it significantly reduces the time required to identify the best feature subset compared to its classical counterpart.
Predicting software defects early in the development process not only enhances the quality and reliability of the software but also decreases the cost of development. A wide range of machine learning techniques can be employed to create software defect prediction models, but the effectiveness and accuracy of these models are often influenced by the choice of appropriate feature subset. Since finding the optimal feature subset is computationally intensive, heuristic and metaheuristic approaches are commonly employed to identify near-optimal solutions within a reasonable time frame. Recently, the quantum computing paradigm quantum annealing (QA) has been deployed to find solutions to complex optimization problems. This opens up the possibility of addressing the feature subset selection problem with a QA machine. Although several strategies have been proposed for feature subset selection using a QA machine, little exploration has been done regarding the viability of a QA machine for feature subset selection in software defect prediction. This study investigates the potential of D-Wave QA system for this task, where we formulate a mutual information (MI)-based filter approach as an optimization problem and utilize a D-Wave Quantum Processing Unit (QPU) solver as a QA solver for feature subset selection. We evaluate the performance of this approach using multiple software defect datasets from the AEEM, JIRA, and NASA projects. We also utilize a D-Wave classical solver for comparative analysis. Our experimental results demonstrate that QA-based feature subset selection can enhance software defect prediction. Although the D-Wave QPU solver exhibits competitive prediction performance with the classical solver in software defect prediction, it significantly reduces the time required to identify the best feature subset compared to its classical counterpart.
Feature selection is crucial for enhancing the accuracy and efficiency of machine learning models. This work investigates the possibility of the usefulness of a quantum annealer to tackle the challenge of selecting features based on maximizing mutual information (MI) and conditional mutual information (CMI). Calculating the optimal feature set for maximum MI and CMI remains computationally intractable for large datasets on classical computers, even with approximation methods. This study employs a Mutual Information Quadratic Unconstrained Binary Optimization (MIQUBO) formulation, enabling its solution on a quantum annealer. Importantly, the study demonstrates the relevancy of this approach in identifying the best feature combinations that maximizes the MI and CMI. To showcase its real-world applicability, we apply MIQUBO to forecasting the price of used excavators. Our results demonstrate that using the MIQUBO approach there is an improvement in the prediction of machine learning models for datasets, with a smaller MI concentration on a subset of all features. • Problem Addressed: We tackle the computationally difficult problem of feature selection for machine learning models, specifically by maximizing mutual information (MI) and conditional mutual information (CMI) between features and the target variable. • Proposed Solution: We employ the adiabatic quantum computation paradigm to tackle this problem. The study utilizes a hybrid annealing approach, specifically the D-Wave Kerberos framework, which combines classical and quantum algorithms to solve the complex optimization problem. The feature selection task is reformulated as a Mutual Information Quadratic Unconstrained Binary Optimization (MIQUBO) problem, which can be solved on a quantum annealer. • Real-World Application: The MIQUBO method is applied to a practical, real-world scenario: forecasting the prices of used excavators. • Key Findings: The research demonstrates that using the MIQUBO approach leads to an improvement in the prediction performance of machine learning models. This is particularly true for datasets where the mutual information is less concentrated on a small subset of features.
Feature selection is crucial for enhancing the accuracy and efficiency of machine learning (ML) models. This work investigates the utility of quantum annealing for the feature selection process in an ML-pipeline, used for maximizing the mutual information (MI) or conditional mutual information (CMI) of the underlying feature space. Calculating the optimal set of features that maximize the MI or CMI is computationally intractable for large datasets on classical computers, even with approximative methods. This study employs a Mutual Information Quadratic Unconstrained Binary Optimization (MIQUBO) formulation, enabling its solution on a quantum annealer. We demonstrate the capability of this approach to identify the best feature combinations that maximize the MI or CMI. To showcase its real-world applicability, we solve the MIQUBO problem to forecast the prices of used excavators. Our results demonstrate that for datasets with a small MI concentration the MIQUBO approach can provide a significant improvement over MI-only based approaches, dependent on the dimension of the selected feature space.
Feature selection is a critical step in the machine learning (ML) model development workflow, aimed at identifying the most relevant subset of features from a dataset to improve ML model performance. In this paper, we investigate the use of quantum annealing to enhance the efficiency and effectiveness of feature selection, as compared to classical algorithmic methods for feature selection, prior to constructing a ML model for Internet of Things network intrusion detection. We aim to determine the optimal selection of network traffic features that contribute most to the detection of network intrusions. Leveraging a quantum annealing algorithm, which exploits quantum mechanics principles to find optimal solutions, along with D-Wave’s hybrid quantum computing service, enables us to successfully tackle this combinatorial optimization problem. Our quantum machine learning approach leverages the strengths of both classical and quantum computing, offering a “one-shot” solution to feature selection without the need for iterative ML model training or incremental construction of the solution.
We present a novel methodology for automated feature subset selection from a pool of physiological signals using Quantum Annealing (QA). As a case study, we will investigate the effectiveness of QA-based feature selection techniques in selecting the optimal feature subset for stress detection. Features are extracted from four signal sources: foot EDA, hand EDA, ECG, and respiration. The proposed method embeds the feature variables extracted from the physiological signals in a binary quadratic model. The bias of the feature variable is calculated using the Pearson correlation coefficient between the feature variable and the target variable. The weight of the edge connecting the two feature variables is calculated using the Pearson correlation coefficient between two feature variables in the binary quadratic model. Subsequently, D-Wave’s clique sampler is used to sample cliques from the binary quadratic model. The underlying solution is then re-sampled to obtain multiple good solutions and the clique with the lowest energy is returned as the optimal solution. The proposed method is compared with commonly used feature selection techniques for stress detection. Results indicate that QA-based feature subset selection performed equally as that of classical techniques. However, under data uncertainty conditions such as limited training data, the performance of quantum annealing for selecting optimum features remained unaffected, whereas a significant decrease in performance is observed with classical feature selection techniques. Preliminary results show the promise of quantum annealing in optimizing the training phase of a machine learning classifier, especially under data uncertainty conditions.
Search-based software engineering (SBSE) tackles critical optimization problems in software engineering, including the next release problem (NRP) and feature selection problem (FSP). Traditional heuristic approaches and integer linear programming (ILP) methods work well for small- to medium-scale problems but face growing computational cost as instances scale up. We investigate quantum annealing (QA) as an optimization subroutine for multi-objective SBSE problems. We propose two QA-based algorithms tailored to different problem scales. For small-scale problems, we reformulate multi-objective optimization (MOO) as single-objective optimization (SOO) using penalty-based mappings for quantum processing. For large-scale problems that exceed current hardware capacity, we employ a decomposition strategy guided by maximum energy impact (MEI) that partitions the problem into smaller sub-QUBOs, integrating QA with a steepest-descent method for local search. Applied to NRP and FSP, our approaches are benchmarked against the heuristic NSGA-II, IBEA, and MOEA/D, as well as the ILP-based ϵ-constraint method. The experimental results reveal that while our methods produce fewer non-dominated solutions than ϵ-constraint, they achieve substantial reductions in execution time. Compared to the evolutionary baselines, our methods achieve competitive solution quality with lower runtime on the instances that they can encode. The penalty-based QUBO formulation fails to reach feasible regions on constraint-dense FSP instances, limiting the current applicability of the approach. QA is a promising but still hardware-limited component for multi-objective SBSE workflows, rather than a wholesale replacement for classical solvers.
This paper presents a novel approach to feature selection leveraging quantum-inspired optimization, specifically quantum annealing, to maximize entropy within a candidate feature subset. Traditional feature selection techniques often face challenges in high-dimensional datasets, struggling to pinpoint the most impactful features. Our method addresses this by framing feature selection as an entropy maximization problem. The core idea is to employ a quantum annealing algorithm to explore the feature space, iteratively refining the subset until the entropy reaches a maximum value. This approach aims to identify features that contribute significantly to the information content related to the target variable. We demonstrate the potential of this approach compared to conventional methods, highlighting its ability to effectively handle complex datasets and improve model performance. The theoretical framework is presented, along with a detailed explanation of the algorithm and its underlying principles. The results, while not based on empirical experimentation due to the nature of the theoretical exploration, provide a strong argument for the viability and potential advantages of quantum-inspired techniques in feature selection.