Diversity based imbalance learning approach for software fault prediction using machine learning models

Software defects and quality assurance are crucial aspects of software development that should be considered during the software development cycle. To ensure high-quality software, it is essential to have a robust quality assurance process in place. System reliability and quality are very key components that must be considered during software development, and this can only be achieved when software undergoes a thorough test process for errors, anomalies, defects, omissions, and bugs. Early software defect prediction and detection play an essential role in ensuring the reliability and quality of software systems, ensuring that software companies discover errors or defects early enough and allocate more resources to defect-prone modules. This study proposes the development of an enhanced classifier model for software defect prediction and detection. The aim is to harness the collective intelligence of selected base classifiers like Support Vector Machine, Logistic regression, Decision Trees, Random Forest, AdaBoost, Gradient Boosting, K-Nearest Neighbor, GaussianNB, and Multi-Layer Perception to improve accuracy, robustness, and generalization in identifying potential defects using a soft voting ensemble technique. The ensemble model leveraged the confidence probability of the soft voting technique and the generalization advantage of cross-validation leading to a more robust and dynamic model. The performance of the model with existing classifiers was evaluated using accuracy, F1 score, Precision, and area under the ROC curve (ROC- AUC) as the evaluation metrics. The results of the experiment revealed that the Proposed Classifier produced an overall Accuracy rate of 93%, and ROC AUC of 98%. The results demonstrate the effectiveness of our enhanced ensemble classifier in software defect detection and prediction. By harnessing the strengths of diverse base classifiers, our approach provides a robust and adaptive solution to the challenges of early detection and mitigating defects in software systems. This research contributes to the advancement of reliable software development practices and lays the foundation for future enhancements in ensemble-based defect detection methodologies.

Machine learning (ML) models are cost-effective tools that can establish a prognosis by considering clinical, genomic, and histological data from breast cancer patients. In this study, we evaluate the performance of 12 ML models in predicting the overall survival of breast cancer patients from “The Cancer Genome Atlas-Breast Cancer” database at 60 months of follow-up. The models evaluated were Logistic Regression (LR), Ridge Classifier (RC), Least Absolute Shrinkage and Selection Operator (LASSO), K-Nearest Neighbors (KNN), Naive Bayes (NB), Linear Discriminant Analysis (LDA), Decision Tree (DT), Multilayer Perceptron (MP), Stochastic Gradient Descent (SGD), Support Vector Machine (SVM), Random Forest (RF), and XGBoost (XGB). The most accurate models were NB (86.76%) and SGD (85.29%). The models with the highest sensitivity were SGD (93.75%), MP (91.67%), and NB (89.89%), while the models with the highest specificity were KNN (95.0%), DT (90.0%), and LASSO (85.0%). The models with the highest area under the ROC curve were LDA (90.83%), LASSO (90.1%), and LR (89.79%). The most relevant variables were “Previous Diagnosis of Cancer,” “Presence of Tumor,” “Ancillary Therapy,” and “Histology.” Our findings demonstrate that AI tools for predicting patient outcomes are a viable option when expensive prognostic genomic tools are not available. According to our results, NB and LASSO show promising performance in predicting the prognosis of breast cancer patients. Further studies with data from developing countries are needed to improve the performance of these tools.

After radical prostatectomy (RP), one-third of patients will experience biochemical recurrence (BCR), which is associated with subsequent metastasis and cancer-specific mortality. We employed machine learning (ML) algorithms to predict BCR after RP, and compare them with traditional regression models and nomograms. Utilizing a prospective Uro-oncology registry, 18 clinicopathological parameters of 1130 consecutive patients who underwent RP (2009-2018) were recorded, yielding over 20,000 data points for analysis. The data set was split into a 70:30 ratio for training and validation. Three ML models: Naïve Bayes (NB), random forest (RF), and support vector machine (SVM) were studied, and compared with traditional regression models and nomograms (Kattan, CAPSURE, John Hopkins [JHH]) to predict BCR at 1, 3, and 5 years. Over a median follow-up of 70.0 months, 176 (15.6%) developed BCR, at a median time of 16.0 months (interquartile range [IQR]: 11.0-26.0). Multivariate analyses demonstrated strongest association of BCR with prostate-specific antigen (PSA) (p: 0.015), positive surgical margins (p < 0.001), extraprostatic extension (p: 0.002), seminal vesicle invasion (p: 0.004), and grade group (p < 0.001). The 3 ML models demonstrated good prediction of BCR at 1, 3, and 5 years, with the area under curves (AUC) of NB at 0.894, 0.876, and 0.894, RF at 0.846, 0.875, and 0.888, and SVM at 0.835, 0.850, and 0.855, respectively. All models demonstrated (1) robust accuracy (>0.82), (2) good calibration with minimal overfitting, (3) longitudinal consistency across the three time points, and (4) inter-model validity. The ML models were comparable to traditional regression analyses (AUC: 0.797, 0.848, and 0.862) and outperformed the three nomograms: Kattan (AUC: 0.815, 0.798, and 0.799), JHH (AUC: 0.820, 0.757, and 0.750) and CAPSURE nomograms (AUC: 0.706, 0.720, and 0.749) (p < 0.001). Supervised ML algorithms can deliver accurate performances and outperform nomograms in predicting BCR after RP. This may facilitate tailored care provisions by identifying high-risk patients who will benefit from multimodal therapy.

Background: Osteoporosis is a systemic skeletal disorder marked by reduced bone density and microarchitectural deterioration, leading to increased fracture risk. While the dual-energy X-ray absorptiometry (DEXA) scan is the World Health Organization (WHO)-recommended diagnostic standard, its limitations necessitate alternative methods. Emerging magnetic resonance imaging (MRI) techniques, radiomics, and machine learning promise to enhance osteoporosis diagnosis through detailed analysis of lumbar MRI apparent diffusion coefficient (ADC) maps, potentially revolutionizing early detection and treatment strategies. Objectives: In this study, we are going to evaluate the performance of machine learning (ML) models using radiomics features of lumbar MRI ADC map for osteoporosis detection, and to identify significant features and their diagnostic thresholds. Specific performance metrics such as accuracy, sensitivity, specificity, and Area Under the receiver operating characteristic (ROC) Curve (AUC) were assessed. Patients and Methods: This retrospective study employed a cross-sectional design, with a total of 140 cases, including 21 with osteoporosis. The study's inclusion criteria consisted of concurrent lumbar MRI and DEXA within a year, while exclusion criteria included infectious or neoplastic lumbar lesions, fractures, instrumentation, significant osteodegenerative changes, cases where the first four lumbar vertebrae were not included in the imaging field, and absence of diffusion-weighted imaging. Manual segmentation of lumbar vertebrae from ADC maps was performed to create a comprehensive dataset, comprising 5,580 radiomics features per case. Subsequently, the top five features selected by fast correlation-based filter (FCBF) were used to test the performance of seven Machine Learning algorithms (k-Nearest neighbors, decision tree, random forest, logistic regression, support vector machine, naive bayes, and neural network). Statistical tests and ROC curve analysis were conducted to determine the significance and thresholds of these features. Results: The study included 140 cases, with 132 females (94.3%) and 8 males (5.7%), and a mean age of 65.32 ± 8.50 years. The mean BMI was 31.43 ± 5.53 kg/m² for females and 26 ± 3.59 kg/m² for males. In terms of demographic differences, no significant age difference was found between the osteoporotic and non-osteoporotic groups (P = 0.889). However, the osteoporotic group had significantly lower mean body weight (64.90 ± 10.13 kg vs. 74.68 ± 13.94 kg, P = 0.003) and BMI (27.40 ± 4.38 kg/m² vs. 31.77 ± 5.52 kg/m², P = 0.001) compared to the non-osteoporotic group. The median interval between DEXA and lumbar MRI was 1 month (range 0.1 - 11.87 months). The Neural Network model demonstrated the highest performance with an AUC of 0.616 and a classification accuracy of 0.764 using all features. The Naive Bayes model, using the top five features selected by FCBF, showed the highest performance with an AUC of 0.913, accuracy of 0.907, sensitivity of 0.667, and specificity of 0.95. All ML models’ performance were elevated by feature selection. Independent t-tests and Mann-Whitney U tests identified 521 and 670 significant features, respectively (P &lt; 0.05). ROC analysis revealed 58 features with AUC values above 0.70. Conclusion: This study's findings suggest that ML models, particularly the Naive Bayes algorithm, can effectively use lumbar ADC map radiomics to diagnose osteoporosis. These findings could enhance early detection and treatment strategies, potentially improving patient outcomes and reducing the burden of osteoporotic fractures. This study also established threshold values for significant features.

Parking space management has become a critical challenge in urban areas due to increasing vehicle numbers and limited parking infrastructure. This paper presents a comprehensive study of machine learning (ML) models in IoT‐enabled environments focusing on proposing an ML‐based model for predicting available parking space. The study evaluates the performance of various models including K‐nearest neighbors (KNNs), support vector machines (SVMs), random forest (RF), decision tree (DT), logistic regression (LR), and Naïve Bayes (NB) based on “precision, recall, accuracy, and F1‐score performance metrics”. The results obtained by implementing ML models on the data with 65% and 85% threshold values are compared to draw meaningful conclusions regarding their performance in predicting parking space availability. Among the evaluated models, random forest (RF) demonstrates superior performance with high precision, recall, accuracy, and F1‐score values. It showcases its effectiveness in accurately predicting parking space availability in the IoT‐enabled environment. On the other hand, models such as K‐nearest neighbors (KNNs), decision tree (DT), logistic regression (LR), and Naïve Bayes (NB) show relatively lower performance in complex parking scenarios. The paper concludes that the use of advanced predictive models, particularly random forest, significantly enhances the accuracy and reliability of IoT‐enabled parking management systems and also reduces the waiting time of the vehicles, leading to more efficient resource utilization, reduced traffic congestion in real‐time scenarios, and better user satisfaction in the IoT‐enabled environment.

Handling class imbalance is a challenge in software defect prediction. Imbalanced datasets can cause bias in machine learning models, hindering their ability to detect defects. This paper proposes an integration of Adaptive Synthetic Sampling (ADASYN) and ensemble learning methods to improve prediction accuracy. ADASYN enhances the handling of imbalanced data by generating synthetic samples for hard-to-classify instances, while the ensemble stacking technique leverages the strengths of multiple models to reduce bias and variance. The machine learning model used in this study is K-Nearest Neighbors (KNN), Decision Tree (DT), and Random Forest (RF). The results demonstrate that ADASYN combined with ensemble stacking outperforms the traditional SMOTE technique in most cases. For instance, in the Ant-1.7 dataset, ADASYN achieved a stacking accuracy of 90.60% compared to 89.32% with SMOTE. Similarly, in the Camel-1.6 dataset, ADASYN achieved 91.56%, slightly exceeding SMOTE’s 91.32%. However, SMOTE performed better in simpler models like Decision Tree for certain datasets, highlighting the importance of choosing the appropriate resampling method. Across all datasets, ensemble stacking consistently provided the highest accuracy, benefiting from ADASYN's adaptive resampling strategy. These results underscore the importance of combining advanced sampling methods with ensemble learning techniques to address class imbalance effectively. This approach improves prediction accuracy and provides a practical framework for reliable software defect prediction in real-world scenarios. Future work will explore hybrid techniques and broader evaluations across diverse datasets and classifiers.

Defects occurring in software product are a universal event. Prevention of these defects in the early stage needs more attention because early stage prevention and fixing requires less effort and lower cost. Software defect prediction (SDP) is necessary in the determination of software quality as well as reliability. Prediction of defects is relatively an original research area in software quality engineering. Coverage of key predictors and the kind of data to be collected along with defect prediction model role, the interdependence of defects and predictors can be recognized in software quality. Feature selection (FS) is one of the worthy preprocessing techniques for application that uses huge volumes of data. It is the process of selecting the probable minimal attribute which is expected to be represented in the set of actual attributes. This paper proposes, FS using firefly algorithm (FA) and classifiers like support vector machine (SVM), Naive Bayes (NB) as well as K-nearest neighbor (KNN) are used for classifying the features selected. The FS that make use of the FA is that new technique of evolutionary computation that has been inspired by the process of flash lighting of the fireflies. This can search quickly the feature space for an optimal or a near optimal feature subset for minimizing a certain function of fitness. This proposed fitness function has made use of the incorporation of both the accuracy of classification and the reduction of the size. The results of the experiment have shown that the FS using the FA can achieve a better accuracy of classification than that of the other methods.

Background Malaria remains a major cause of illness and death among children under five in Nigeria, despite efforts to control transmission. Accurate and reliable prediction of malaria outbreaks is crucial for health authorities to take timely measures. This study aims to identify the most robust machine learning classification algorithms for predicting the status of malaria in children under five (0-59 months). Methods The 2021 Nigeria Malaria Indicator Survey (NMIS) included 10,655 children under five who were tested for malaria using the Rapid Diagnostic Test (RDT). Various machine learning models were explored, including Decision Trees, K-Nearest Neighbor, Naïve Bayes, Random Forest, Support Vector Machines, and Survey Logistic Regression, and their performance was evaluated through metrics such as accuracy, AUC, balanced accuracy, F1-Score, negative predictive value, precision, sensitivity, and specificity. Results Random Forest (RF) is the most robust and balanced classification model due to its superior accuracy (79%), precision (77%), recall (62%), F1-score (69%), and AUC (80%). Support Vector Machine (SVM) also demonstrated strong performance, particularly in accuracy (74%) and AUC (80%). Survey Logistic Regression (SLR) and Decision Tree (DT) offered moderate results but fell short compared to RF and SVM, indicating the need for further optimization. Naive Bayes (NB) and K-Nearest Neighbors (KNN) had limitations, making them less reliable for this task. Conclusion In conclusion, the study reveals that RF and SVM are the best classification models for predicting malaria status in children under five years old. RF is reliable and balanced, while SVM is preferred for recall. SLR and DT show potential but require optimization. NB and KNN have significant performance gaps, making them less suitable. These findings will help policymakers and malaria intervention programs address key factors, enabling more targeted public health interventions to reduce the malaria burden on young children and improve the well-being of vulnerable populations in Nigeria.

A novel software defect prediction based on atomic class-association rule mining

Text mining is a special case of data mining which explore unstructured or semi-structured text documents, to establish valuable patterns and rules that indicate trends and significant features about specific topics. Text mining has been in pattern recognition, predictive studies, sentiment analysis and statistical theories in many areas of research, medicine, financial analysis, social life analysis, and business intelligence. Text mining uses concept of natural language processing and machine learning. Machine learning algorithms have been used and reported to give great results, but their performance of machine learning algorithms is affected by factors such as dataset domain, number of classes, length of the corpus, and feature selection techniques used. Redundant attribute affects the performance of the classification algorithm, but this can be reduced by using different feature selection techniques and dimensionality reduction techniques. Feature selection is a data preprocessing step that chooses a subset of input variable while eliminating features with little or no predictive information. Feature selection techniques are Information gain, Term Frequency, Term Frequency-Inverse document frequency, Mutual Information, and Chi-Square, which can use a filters, wrappers, or embedded approaches. To get the most value from machine learning, pairing the best algorithms with the right tools and processes is necessary. Little research has been done on the effect of feature selection techniques on classification accuracy for pairing of these algorithms with the best feature selection techniques for optimal results. In this research, a text classification experiment was conducted using incident management dataset, where incidents were classified into their resolver groups. Support vector machine (SVM), K-Nearest Neighbors (KNN), Naïve Bayes (NB) and Decision tree (DT) machine learning algorithms were examined. Filtering approach was used on the feature selection techniques, with different ranking indices applied for optimal feature set and classification accuracy results analyzed. The classification accuracy results obtained using TF were, 88% for SVM, 70% for NB, 79% for Decision tree, and KNN had 55%, while Boolean registered 90%, 83%, 82% and 75%, for SVM, NB, DT, and KNN respectively. TF-IDF, had 91%, 83%, 76%, and 56% for SVM, NB, DT, and KNN respectively. The results showed that algorithm performance is affected by feature selection technique applied. SVM performed best, followed by DT, KNN and finally NB. In conclusion, presence of noisy data leads to poor learning performance and increases the computational time. The classifiers performed differently depending on the feature selection technique applied. For optimal results, the classifier that performed best together with the feature selection technique with the best feature subset should be applied for all types of data for accurate classification performance. Keywords: Text Classification, Supervised Machine Learning, Feature Selection DOI : 10.7176/JIEA/9-3-06 Publication date :May 31 st 2019

Defects that occur in software products are a universal occurrence. Software defect prediction is usually carried out to determine the performance, accuracy, precision and performance of the prediction model or method used in research, using various kinds of datasets. Software defect prediction is one of the Software Engineering studies that is of great concern to researchers. This research was conducted to determine the performance of tree-based classification algorithms including Decision Trees, Random Forests and Deep Forests without using feature selection and using firefly feature selection. And also know the tree-based classification algorithm with firefly feature selection which can provide better software defect prediction performance. The dataset used in this study is the ReLink dataset which consists of Apache, Safe and Zxing. Then the data is divided into testing data and training data with 10-fold cross validation. Then feature selection is performed using the Firefly Algorithm. Each ReLink dataset will be processed by each tree-based classification algorithm, namely Decision Tree, Random Forest and Deep Forest according to the results of the firefly feature selection. Performance evaluation uses the AUC value (Area under the ROC Curve). Research was conducted using google collab and the average AUC value generated by Firefly-Decision Tree is 0.66, the average AUC value generated by Firefly-Random Forest is 0.77, and the average AUC value generated by Firefly-Deep Forest is 0, 76. The results of this study indicate that the approach using the Firefly algorithm with Random Forest classification can work better in predicting software damage compared to other tree-based algorithms. In previous studies, tree-based classification with hyperparameter tuning on software defect prediction datasets obtained quite good results. In another study, the classification performance of SVM, Naïve Bayes and K-nearest neighbor with firefly feature selection resulted in improved performance. Therefore, this research was conducted to determine the performance of a tree-based algorithm using the firefly selection feature.

In recent years, a significant amount of research has focused on analyzing the effectiveness of machine learning (ML) models for malware detection. These approaches have ranged from methods such as decision trees and clustering to more complex approaches like support vector machine (SVM) and deep neural networks. In particular, neural networks have proven to be very effective in detecting complex and advanced malware. This, however, comes with a caveat. Neural networks are notoriously complex. Therefore, the decisions that they make are often just accepted without questioning why the model made that specific decision. The black box characteristic of neural networks has challenged researchers to explore methods to explain black-box models such as SVM and neural networks and their decision-making process. Transparency and explainability give the experts and malware analysts assurance and trustworthiness about the ML models’ decisions. In addition, it helps in generating comprehensive reports that can be used to enhance cyber threat intelligence sharing. As such, this much needed analysis drives our work in this paper to explore the explainability and interpretability of ML models in the field of online malware detection. In this paper, we used the Shapley Additive exPlanations (SHAP) explainability technique to achieve efficient performance in interpreting the outcome of different ML models such as SVM Linear, SVM-RBF (Radial Basis Function), Random Forest (RF), Feed-Forward Neural Net (FFNN), and Convolutional Neural Network (CNN) models trained on an online malware dataset. To explain the output of these models, explainability techniques such as KernalSHAP, TreeSHAP, and DeepSHAP are applied to the obtained results.

Software Testing is an important phase of Software Development Life cycle. Effective software testing helps in identifying faulty modules, but this process becomes very time consuming, especially for large /complex software. Moreover early identification of error prone modules can be useful in producing better quality software. Hence software fault prediction has gained significant attention of the researchers in the recent years. Mainly two types of techniques are being used for it: statistical methods and Machine learning models, out of which recent trend is more inclined towards machine Learning based techniques. Identification of faulty modules is a binary classification problem and machine learning models such as Decision Tree and its variants, Random Forest and Support Vector Machine, are best suited for the fault prediction. This paper attempts to predict software faults using four different classification models. Further, the performance evaluation of these models is also carried out in this paper using accuracy, Precision, Recall, F1-Score and execution time of over 12 datasets, extracted from one of the most commonly used PROMISE repository. Based on these performance metrics comparison of applied four models is done and our comparative analysis indicates that in most of the cases Support Vector Machine model is able to predict software faults more efficiently than the rest in terms of accuracy as well as execution time

A Classification Prediction Analysis of Flight Cancellation Based on Spark

This study aims to analyze the performance of machine learning models for detecting Internet of Things malware utilizing a recent IoT dataset. Experiments on the IoT dataset were conducted with nine well-known machine learning techniques, consisting of Logistic Regression (LR), Naive Bayes (NB), Decision Tree (DT), k-Nearest Neighbors (KNN), Support Vector Machines (SVM), Neural Networks (NN), Random Forest (RF), Bagging (BG), and Stacking (ST). The results show that the proposed model attains 100% accuracy in detecting IoT malware for DT, SVM, RF, BG; about 99.9% percent for LR, NB, KNN, NN; and only 28.16% for ST classifier. This study also shows higher performance than other proposed machine learning models evaluated on the same dataset. Therefore, the results of this study can help both the researchers and application developers in designing and building intelligent malware detection systems for IoT devices.