Public-key Encryption with Keyword Search (PEKS) enables users to search encrypted data stored on an untrusted server without revealing any sensitive information. However, existing PEKS schemes are typically inefficient and lack the flexibility to support complex search policies. To address this, a novel PEKS scheme based on Arithmetic Span Programs (PEKS-ASP) is proposed in this paper. This is the first scheme to enable flexible and efficient search policies by using directed acyclic graphs. This approach enhances the efficiency of complex search queries that implement AND, OR, and NOT gates, enabling a more efficient representation of complicated search policies without redundancy in keyword usage. And our proposed PEKS-ASP scheme guarantees constant-size public parameters regardless of the number of keywords. Additionally, the proposed scheme achieves adaptive security under the matrix decisional Diffie-Hellman (MDDH) assumption, employing dual system encryption techniques. Both theoretical analysis and experimental results demonstrate that PEKS-ASP significantly improves efficiency and practicality, making it well-suited for practical applications in various cloud environments.

This study presents a comprehensive evaluation of deep learning approaches for medical image classification tasks, focusing on both convolutional neural networks (CNNs) and transformer-based models. Specifically, we assess the performance of three CNN architectures (InceptionNet, DenseNet, and EfficientNet) and three transformer-based models (Vision Transformer, Swin Transformer, and Pyramid Vision Transformer) using three publicly available medical imaging datasets. These datasets represent both balanced multi-class and imbalanced binary classification scenarios, reflecting real-world challenges in medical diagnostics. The experimental results consistently show that transformer-based models outperform conventional CNNs across all datasets, demonstrating superior performance both overall and at the individual class level, particularly in recall for clinically critical classes. This is crucial for minimizing severe Type II errors, which can result in missed diagnoses in practice. While EfficientNet, a CNN architecture, remains a strong baseline with performance often comparable to that of the transformers, attention-based models demonstrate effectiveness in handling complex medical imaging tasks. These findings underscore the potential of transformer-based models for clinical deployment when high recall is essential.

In the software quality modeling process, many practitioners often ignore problems such as high dimensionality and class imbalance that exist in data repositories.They directly use the available set of software metrics to build classification models without regard to the condition of the underlying software measurement data, leading to a decline in prediction performance and extension of training time.In this study, we propose an approach, in which feature selection is combined with data sampling, to overcome these problems.Feature selection is a process of choosing a subset of relevant features so that the quality of prediction models can be maintained or improved.Data sampling seeks a more balanced dataset through the addition or removal of instances.Three different approaches would be produced when combing these two techniques: 1-sampling performed prior to feature selection, but retaining the unsampled data instances; 2-sampling performed prior to feature selection, retaining the sampled data instances; 3-sampling performed after feature selection.The empirical study was carried out on six datasets from a real-world software system.We employed one filter-based (no learning algorithm involved in the selection process) feature subset selection technique called correlationbased feature selection combined with the random undersampling method.The results demonstrate that sampling performed prior to feature selection, but retaining the unsampled data instances (Approach 1) performs better than the other two approaches.

Defect prediction is very challenging in software development practice. Classification models are useful tools that can help for such prediction. Classification models can classify program modules into quality-based classes, e.g. fault-prone (fp) or not-fault-prone (nfp). This facilitates the allocation of limited project resources. For example, more resources are assigned to program modules that are of poor quality or likely to have a high number of faults based on the classification. However, two main problems, high dimensionality and class imbalance, affect the quality of training datasets and therefore classification models. Feature selection and data sampling are often used to overcome these problems. Feature selection is a process of choosing the most important attributes from the original dataset. Data sampling alters the dataset to change its balance level. Another technique, called boosting (building multiple models, with each model tuned to work better on instances misclassified by previous models), is found to also be effective for resolving the class imbalance problem. In this study, we investigate an approach for combining feature selection with this ensemble learning (boosting) process. We focus on two different scenarios: feature selection performed prior to the boosting process and feature selection performed inside the boosting process. Ten individual base feature ranking techniques, as well as an ensemble ranker based on the ten, are examined and compared over the two scenarios. We also employ the boosting algorithm to construct classification models without performing feature selection and use the results as the baseline for further comparison. The experimental results demonstrate that feature selection is important and needed prior to the learning process. In addition, the ensemble feature ranking method generally has better or similar performance than the average of the base ranking techniques, and more importantly, the ensemble method exhibits better robustness than most base ranking techniques. As for the two scenarios, the results show that applying feature selection inside boosting performs better than using feature selection prior to boosting.

Defect prediction is an important process activity frequently used for improving the quality and reliability of software products. Defect prediction results provide a list of fault-prone modules which are necessary in helping project managers better utilize valuable project resources. In the software quality modeling process, high dimensionality and class imbalance are the two potential problems that may exist in data repositories. In this study, we investigate three data preprocessing approaches, in which feature selection is combined with data sampling, to overcome these problems in the context of software quality estimation. These three approaches are: Approach 1 — sampling performed prior to feature selection, but retaining the unsampled data instances; Approach 2 — sampling performed prior to feature selection, retaining the sampled data instances; and Approach 3 — sampling performed after feature selection. A comparative investigation is presented for evaluating the three approaches. In the experiments, we employed three sampling methods (random undersampling, random oversampling, and synthetic minority oversampling), each combined with a filter-based feature subset selection technique called correlation-based feature selection. We built the defect prediction models using five common classification algorithms. The case study was based on software metrics and defect data collected from multiple releases of a real-world software system. The results demonstrated that the type of sampling methods used in data preprocessing significantly affected the performance of the combination approaches. It was found that when the random undersampling technique was used, Approach 1 performed better than the other two approaches. However, when the feature selection technique was used in conjunction with an oversampling method (random oversampling or synthetic minority oversampling), we strongly recommended Approach 3.

Software defect prediction models that use software metrics such as code-level measurements and defect data to build classification models are useful tools for identifying potentially-problematic program modules. Effectiveness of detecting such modules is affected by the software measurements used, making data preprocessing an important step during software quality prediction. Generally, there are two problems affecting software measurement data: high dimensionality (where a training dataset has an extremely large number of independent attributes, or features) and class imbalance (where a training dataset has one class with relatively many more members than the other class). In this paper, we present a novel form of ensemble learning based on boosting that incorporates data sampling to alleviate class imbalance and feature (software metric) selection to address high dimensionality. As we adopt two different sampling methods (Random Undersampling (RUS) and Synthetic Minority Oversampling (SMOTE)) in the technique, we have two forms of our new ensemble-based approach: selectRUSBoost and selectSMOTEBoost. To evaluate the effectiveness of these new techniques, we apply them to two groups of datasets from two real-world software systems. In the experiments, four learners and nine feature selection techniques are employed to build our models. We also consider versions of the technique which do not incorporate feature selection, and compare all four techniques (the two different ensemble-based approaches which utilize feature selection and the two versions which use sampling only). The experimental results demonstrate that selectRUSBoost is generally more effective in improving defect prediction performance than selectSMOTEBoost, and that the techniques with feature selection do help for getting better prediction than the techniques without feature selection.

An effective system regression testing for consecutive releases of very large software systems, such as modern telecommunications systems, depends considerably on the selection of test cases for execution. Classification models can classify, early in the test planning phase, those test cases that are likely to detect faults in the upcoming regression test. Due to the high uncertainties in regression test, classification models based on fuzzy logic are very useful. Recently, methods have been proposed for automatically generating fuzzy if-then rules by applying complicated rule generation procedures to numerical data. In this research, we introduce and demonstrate a new rule-based fuzzy classification (RBFC) modeling approach as a method for identifying high effective test cases. The modeling approach, based on test case metrics and the proposed rule generation technique, is applied to extracting fuzzy rules from numerical data. In addition, it also provides a convenient way to modify rules according to the costs of different misclassification errors. We illustrate our modeling technique with a case study of large-scale industrial software systems and the results showed that test effectiveness and efficiency was significantly improved.

The predictive accuracy of a classification modelis often affected by the quality of training data. However, there are two problems which may affect the quality of the training data: high dimensionality (too many independent attributes in a dataset) and class imbalance (many more instances of one class than the other class in a binary-classification problem). In this study, we present an iterative feature selection approach working with an ensemble learning method to solve both of these problems. The iterative feature selection approach samples the dataset k times and applies feature ranking to each sampled dataset, the k different rankings are then aggregated to create a single feature ranking. The ensemble learning method used is RUSBoost, in which random under sampling(RUS) is integrated into a boosting algorithm. The main purpose of this paper is to investigate the impact of feature selection as well as the RUSBoost approach on the classification performance in the context of software quality prediction. In the experiment, we explore six rankers, each used along with RUS in the iterative feature selection process. Following feature selection, models are built either using a plain learner or byusing the RUSBoost algorithm. We also examine the case of no feature selection and use this as the baseline for comparisons. The experimental results demonstrate that with the exception of one learner, feature selection combined with boosting provides better classification performance than when either is applied alone or when neither are applied.

High dimensionality and class imbalance are the two main problems affecting many software defect prediction. In this paper, we propose a new technique, named SelectRUSBoost, which is a form of ensemble learning that in-corporates data sampling to alleviate class imbalance and feature selection to resolve high dimensionality. To evaluate the effectiveness of the new technique, we apply it to a group of datasets in the context of software defect prediction. We employ two classification learners and six feature selection techniques. We compare the technique to the approach where feature selection and data sampling are used together, as well as the case where feature selection is used alone (no sampling used at all). The experimental results demonstrate that the SelectRUSBoost technique is more effective in improving classification performance compared to the other approaches.

Artificial Bee Colony (ABC) algorithm is a kind of newly developed bio-inspired intelligence. In this paper, we conduct a investigation on bees' behaviors when seeking for food, and a method for optimizing parameters has been developed. By using this algorithm, we can obtain the best solution to many problems. Pulse Coupled Neural Networks (PCNN) is a kind of algorithm widely used in image processing. The current model of PCNN has several shortcomings, such as losing basic details of the original images. ABC algorithm is adopted to search the best value of PCNN parameters. In this way, the image can be enhanced to the fullest extent. Experimental results verified the feasibility and effectiveness of our proposed approach.

The primary goal of software quality engineering is to produce a high quality software product through the use of some specific techniques and processes. One strategy is applying data mining techniques to software metric and defect data collected during the software development process to identify potential low-quality program modules. In this paper, we investigate the use of feature selection in the context of software quality estimation (also referred to as software defect prediction), where a classification model is used to predict whether program modules (instances) are fault-prone or not-fault-prone. Seven filter-based feature ranking techniques are examined. Among them, six are commonly used, and the other one, named signal to noise ratio (SNR), is rarely employed. The objective of the paper is to compare these seven techniques for various software data sets and assess their effectiveness for software quality modeling. A case study is performed on 16 software data sets, and classification models are built with five different learners and evaluated with two performance metrics. Our experimental results are summarized based on statistical tests for significance. The main conclusion is that the SNR technique performs as well as the best performer of the six commonly used techniques.

Software defect prediction can be considered a binary classification problem. Generally, practitioners utilize historical software data, including metric and fault data collected during the software development process, to build a classification model and then employ this model to predict new program modules as either fault-prone (fp) or not-fault-prone (nfp). Limited project resources can then be allocated according to the prediction results by (for example) assigning more reviews and testing to the modules predicted to be potentially defective. Two challenges often come with the modeling process: (1) high-dimensionality of software measurement data and (2) skewed or imbalanced distributions between the two types of modules (fp and nfp) in those datasets. To overcome these problems, extensive studies have been dedicated towards improving the quality of training data. The commonly used techniques are feature selection and data sampling. Usually, researchers focus on evaluating classification performance after the training data is modified. The present study assesses a feature selection technique from a different perspective. We are more interested in studying the stability of a feature selection method, especially in understanding the impact of data sampling techniques on the stability of feature selection when using the sampled data. Some interesting findings are found based on two case studies performed on datasets from two real-world software projects.

Real-world software systems are becoming larger, more complex, and much more unpredictable. Software systems face many risks in their life cycles. Software practitioners strive to improve software quality by constructing defect prediction models using metric (feature) selection techniques. Finding faulty components in a software system can lead to a more reliable final system and reduce development and maintenance costs. This paper presents an empirical study of six commonly used filter-based software metric rankers and our proposed ensemble technique using rank ordering of the features (mean or median), applied to three large software projects using five commonly used learners. The classification accuracy was evaluated in terms of the AUC (Area Under the ROC (Receiver Operating Characteristic) Curve) performance metric. Results demonstrate that the ensemble technique performed better overall than any individual ranker and also possessed better robustness. The empirical study also shows that variations among rankers, learners and software projects significantly impacted the classification outcomes, and that the ensemble method can smooth out performance.

Abstract The selection of software metrics for building software quality prediction models is a search‐based software engineering problem. An exhaustive search for such metrics is usually not feasible due to limited project resources, especially if the number of available metrics is large. Defect prediction models are necessary in aiding project managers for better utilizing valuable project resources for software quality improvement. The efficacy and usefulness of a fault‐proneness prediction model is only as good as the quality of the software measurement data. This study focuses on the problem of attribute selection in the context of software quality estimation. A comparative investigation is presented for evaluating our proposed hybrid attribute selection approach, in which feature ranking is first used to reduce the search space, followed by a feature subset selection. A total of seven different feature ranking techniques are evaluated, while four different feature subset selection approaches are considered. The models are trained using five commonly used classification algorithms. The case study is based on software metrics and defect data collected from multiple releases of a large real‐world software system. The results demonstrate that while some feature ranking techniques performed similarly, the automatic hybrid search algorithm performed the best among the feature subset selection methods. Moreover, performances of the defect prediction models either improved or remained unchanged when over 85were eliminated. Copyright © 2011 John Wiley & Sons, Ltd.

The data mining and machine learning community is often faced with two key problems: working with imbalanced data and selecting the best features for machine learning. This paper presents a process involving a feature selection technique for selecting the important attributes and a data sampling technique for addressing class imbalance. The application domain of this study is software engineering, more specifically, software quality prediction using classification models. When using feature selection and data sampling together, different scenarios should be considered. The four possible scenarios are: (1) feature selection based on original data, and modeling (defect prediction) based on original data; (2) feature selection based on original data, and modeling based on sampled data; (3) feature selection based on sampled data, and modeling based on original data; and (4) feature selection based on sampled data, and modeling based on sampled data. The research objective is to compare the software defect prediction performances of models based on the four scenarios. The case study consists of nine software measurement data sets obtained from the PROMISE software project repository. Empirical results suggest that feature selection based on sampled data performs significantly better than feature selection based on original data, and that defect prediction models perform similarly regardless of whether the training data was formed using sampled or original data.

In this paper, we study the learning impact of data sampling followed by attribute selection on the classification models built with binary class imbalanced data within the scenario of software quality engineering. We use a wrapper-based attribute ranking technique to select a subset of attributes, and the random undersampling technique (RUS) on the majority class to alleviate the negative effects of imbalanced data on the prediction models. The datasets used in the empirical study were collected from numerous software projects. Five data preprocessing scenarios were explored in these experiments, including: (1) training on the original, unaltered fit dataset, (2) training on a sampled version of the fit dataset, (3) training on an unsampled version of the fit dataset using only the attributes chosen by feature selection based on the unsampled fit dataset, (4) training on an unsampled version of the fit dataset using only the attributes chosen by feature selection based on a sampled version of the fit dataset, and (5) training on a sampled version of the fit dataset using only the attributes chosen by feature selection based on the sampled version of the fit dataset. We compared the performances of the classification models constructed over these five different scenarios. The results demonstrate that the classification models constructed on the sampled fit data with or without feature selection (case 2 and case 5) significantly outperformed the classification models built with the other cases (unsampled fit data). Moreover, the two scenarios using sampled data (case 2 and case 5) showed very similar performances, but the subset of attributes (case 5) is only around 15% or 30% of the complete set of attributes (case 2).

Feature selection is a process of selecting a subset of relevant features for building learning models. It is an important activity for data preprocessing used in software quality modeling and other data mining problems. Feature selection algorithms can be divided into two categories, feature ranking and feature subset selection. Feature ranking orders the features by a criterion and a user selects some of the features that are appropriate for a given scenario. Feature subset selection techniques search the space of possible feature subsets and evaluate the suitability of each. This paper investigates performance metric based feature ranking techniques by using the multilayer perceptron (MLP) learner with nine different performance metrics. The nine performance metrics include overall accuracy (OA), default F-measure (DFM), default geometric mean (DGM), default arithmetic mean (DAM), area under ROC (AUC), area under PRC (PRC), best F-measure (BFM), best geometric mean (BGM) and best arithmetic mean (BAM). The goal of the paper is to study the effect of the different performance metrics on the feature ranking results, which in turn influences the classification performance. We assessed the performance of the classification models constructed on those selected feature subsets through an empirical case study that was carried out on six data sets of real-world software systems. The results demonstrate that AUC, PRC, BFM, BGM and BAM as performance metrics for feature ranking outperformed the other performance metrics, OA, DFM, DGMand DAM, unanimously across all the data sets and therefore are recommended based on this study. In addition, the performances of the classification models were maintained or even improved when over 85 percent of the features were eliminated from the original data sets.

Software metrics collected during project development play a critical role in software quality assurance. A software practitioner is very keen on learning which software metrics to focus on for software quality prediction. While a concise set of software metrics is often desired, a typical project collects a very large number of metrics. Minimal attention has been devoted to finding the minimum set of software metrics that have the same predictive capability as a larger set of metrics - we strive to answer that question in this paper. We present a comprehensive comparison between seven commonly-used filter-based feature ranking techniques (FRT) and our proposed hybrid feature selection (HFS) technique. Our case study consists of a very high-dimensional (42 software attributes) software measurement data set obtained from a large telecommunications system. The empirical analysis indicates that HFS performs better than FRT; however, the Kolmogorov-Smirnov feature ranking technique demonstrates competitive performance. For the telecommunications system, it is found that only 10% of the software attributes are sufficient for effective software quality prediction.

A new rule-based classification model (RBCM) and rulebased model selection technique are presented. The RBCM utilizes rough set theory to significantly reduce the number of attributes, discretation to partition the domain of attribute values, and Boolean predicates to generate the decision rules that comprise the model. When the domain values of an attribute are continuous and relatively large, rough set theory requires that they be discretized. The subsequent discretized domain must have the same characteristics as the original domain values. However, this can lead to a large number of partitions of the attribute's domain space, which in turn leads to large rule sets. These rule sets tend to form models that over-fit. To address this issue, the proposed rule-based model adopts a new model selection strategy that minimizes over-fitting for the RBCM. Empirical validation of the RBCM is accomplished through a case study on a large legacy telecommunications system. The results demonstrate that the proposed RBCM and the model selection strategy are effective in identifying the classification model that minimizes over-fitting and high cost classification errors. Keywords: rule-based classification model, rough set, reducts, discretization, software quality classification

Count models, such as the Poisson regression model, and the negative binomial regression model, can be used to obtain software fault predictions. With the aid of such predictions, the development team can improve the quality of operational software. The zero-inflated, and hurdle count models may be more appropriate when, for a given software system, the number of modules with faults are very few. Related literature lacks quantitative guidance regarding the application of count models for software quality prediction. This study presents a comprehensive empirical investigation of eight count models in the context of software fault prediction. It includes comparative hypothesis testing, model selection, and performance evaluation for the count models with respect to different criteria.

Identifying which software modules, during the software development process, are likely to be faulty is an effective technique for improving software quality. Such an approach allows a more focused software quality & reliability enhancement endeavor. The development team may also like to know the number of faults that are likely to exist in a given program module, i.e., a quantitative quality prediction. However, classification techniques such as the logistic regression model (lrm) cannot be used to predict the number of faults. In contrast, count models such as the Poisson regression model (prm), and the zero-inflated Poisson (zip) regression model can be used to obtain both a qualitative classification, and a quantitative prediction for software quality. In the case of the classification models, a classification rule based on our previously developed generalized classification rule is used. In the context of count models, this study is the first to propose a generalized classification rule. Case studies of two industrial software systems are examined, and for each we developed two count models, (prm, and zip), and a classification model (lrm). Evaluating the predictive capabilities of the models, we concluded that the prm, and the zip models have similar classification accuracies as the lrm. The count models are also used to predict the number of faults for the two case studies. The zip model yielded better fault prediction accuracy than the prm. As compared to other quantitative prediction models for software quality, such as multiple linear regression (mlr), the prm, and zip models have a unique property of yielding the probability that a given number of faults will occur in any module

In this paper, a new classification model, RB2CBL, is proposed. Its structure and methodology are described. By cascading a rule-based (RB) model with two case-based learning (CBL) models, RB2CBL possesses the merits of both RB model and CBL model and restrains their drawbacks. In the RB2CBL model, the parameter optimization of the CBL models is essential, and the embedded genetic algorithm optimizer is used. In our case study, a dataset collected from initial releases of two large, Windowscopy-based embedded system applications, which were used primarily for customizing the configuration of wireless telecommunications products, is processed to investigate and evaluate the models. The results show that, by suitably choosing accuracy settings of the RB model, RB2CBL model outperforms the RB model alone without overfitting. In practice, the RB2CBL model effectively reduced the misclassification rates and improved prediction accuracy for the embedded software system

This study investigates the attribute selection problem for reducing the number of software metrics (program attributes) used by a case-based reasoning (CBR) software quality classification model. The metrics are selected using the Kolmogorov-Smirnov (K-S) two sample test. The "modified expected cost of misclassification" measure, recently proposed by our research team, is used as a performance measure to select, evaluate, and compare classification models. The attribute selection procedure presented in this paper can assist a software development organization in determining the software metrics that are better indicators of software quality. By reducing the number of software metrics to be collected during the development process, the metrics data collection task can be simplified. Moreover, reducing the number of metrics would result in reducing the computation time of a CBR model. Using an empirical case study of a real-world software system, it is shown that with a reduced number of metrics the CBR technique is capable of yielding useful software quality classification models. Moreover, their performances were better than or similar to CBR models calibrated without attribute selection.

Poisson regression model is widely used in software quality modeling. When the response variable of a data set includes a large number of zeros, Poisson regression model will underestimate the probability of zeros. A zero-inflated model changes the mean structure of the pure Poisson model. The predictive quality is therefore improved. In this paper, we examine a full-scale industrial software system and develop two models, Poisson regression and zero-inflated Poisson regression. To our knowledge, this is the first study that introduces the zero-inflated Poisson regression model in software reliability. Comparing the predictive qualities of the two competing models, we conclude that for this system, the zero-inflated Poisson regression model is more appropriate in theory and practice.