The number of security attacks and the impact has grown considerably in the recent several years. As a result, new emerging software development models are required that assist in developing software that is secure by default. This article reviews the most widely used security software models. It proposes a new Security Assurance Model (SAM) for Software Development that is adaptable to all contemporary scenarios, emphasizing global software development (GSD) vendor companies. The SAM of Software Development was developed after studying 11 well-known development models and analyzing results obtained from a systematic literature review (SLR) and questionnaire survey. The SAM of Software Development consists of seven security assurance levels: Governance and Security Threat Analysis, Secure Requirement Analysis, Secure Design, Secure Coding, Secure Testing and Review, Secure Deployment, and Security Improvement. The security assurance levels of SAM of software development consist of 46 critical software security risks (CSSRs) and 388 practices for addressing these risks. The proposed SAM of Software Development was assessed based on a tool created by Motorola, which is used to evaluate the present state of a company’s software processes and find areas for improvement. We conducted 3 case studies on software development companies, using data from real software projects to examine the results of a practical experiment in each company. The results of the case studies indicate that the proposed SAM of Software Development helps measure the security assurance level of an organization. In addition, it can potentially serve as a framework for researchers to develop new software security measures.

Software bug prediction is a software maintenance technique used to predict the occurrences of bugs in the early stages of the software development process. Early prediction of bugs can reduce the overall cost of software and increase its reliability. Machine learning approaches have recently offered several prediction methods to improve software quality. This paper empirically investigates eight well-known machine learning and deep learning algorithms for software bug prediction. We compare the created models using different evaluation metrics and a well-accepted dataset to make the study results more reliable. This study uses a large dataset collected from five publicly available bug datasets that includes about 60 software metrics. The source-code metrics of internal class quality, including cohesion, coupling, complexity, documentation inheritance, and size metrics, were used as features to predict buggy and non-buggy classes. Four performance metrics, namely accuracy, macro F1 score, weighted F1 score, and binary F1 score, are considered to quantitatively evaluate and compare the performance of the constructed bug prediction models. The results demonstrate that the deep learning model (LSTM) outperforms all other models across these metrics, achieving an accuracy of 0.87.

This research is an attempt to explore the drivers of sustainable consumption and their impact on online purchase intentions (OPI) for agricultural products. To this aim, social influence (SI) and quality assurance (QA) were identified as the drivers of sustainable consumption leading to the formation of OPI. The input from the technology acceptance model (TAM) and unified theory of acceptance and use of technology (UTAUT) has been incorporated to develop a model that explains the formation of OPI. Primary data were collected from 612 respondents and subjected to partial least square (PLS) analysis. The impact of SI and QA on OPI was found to be positively significant. Furthermore, the influence of perceived value (PV) as a mediator for the SI–OPI relationship has been discovered to be positively significant. This shows that as the PV increases, the influence of SI also increases. However, when the perceived risk (PR) was introduced as a mediator for the QA–OPI relationship, the influence of QA on OPI became insignificant. SI and QA impacts are the most important aspects that influence OPI according to this study, which is useful information for agricultural enterprises and online vendors. It is proposed that the SI and QA impacts might be exploited as a new tool to influence customers’ OPI for agricultural firms and online sellers. Therefore, managers must work to produce greater levels of PV in online purchases and reduce the PR in order to develop OPI for sustainable consumption.

Attention Deficit Hyperactivity Disorder is one of the most common neurodevelopmental disorders in which patients have difficulties related to inattention, hyperactivity, and impulsivity. Those patients are in need of a psychological therapy use Cognitive Behavioral Therapy (CBT) to enhance the way they think and behave. This type of therapy is mostly common in treating patients with anxiety and depression but also is useful in treating autism, obsessive compulsive disorder and post-traumatic stress disorder. A major limitation of traditional CBT is that therapists may face difficulty in optimizing patients’ neuropsychological stimulus following a specified treatment plan. Other limitations include availability, accessibility and level-of-experience of the therapists. Hence, this paper aims to design and simulate a generic cognitive model that can be used as an appropriate alternative treatment to traditional CBT, we term as “AR-Therapist.” This model takes advantage of the current developments of augmented reality to engage patients in both real and virtual game-based environments.

Tomatoes are among the most widely cultivated and consumed vegetable crops worldwide. They are usually harvested in large quantities that need to be promptly and accurately classified into healthy and defective categories. Traditional methods for tomato classification are labor-intensive and prone to human error. Therefore, this study proposes an approach that leverages feature fusion from two pre-trained convolutional neural networks (CNNs), VGG16 and ResNet-50, to enhance classification performance. A comprehensive evaluation of multiple individual and hybrid classifiers was conducted on a dataset of 43,843 tomato images, which is heavily imbalanced toward the healthy class. The results showed that the best-performing classifier on fused features achieved an average precision (AP) and accuracy of 0.92 and 0.97, respectively, on the test set. In addition, the experimental evaluation revealed that fused features improved classification performance across multiple metrics, including accuracy, AP, recall, and F1-score, compared to individual features of VGG16 and ResNet-50. Furthermore, the proposed approach was benchmarked against three standalone CNN models, namely MobileNetV2, EfficientNetB0, and DenseNet121, and demonstrated superior performance in all evaluated metrics. These findings highlight the efficacy of deep feature fusion in addressing class imbalance and improving automated tomato defect detection.

A large class with many responsibilities is a design flaw that commonly occurs in real-world object-oriented systems during their lifespan. Such a class tends to be more difficult to comprehend, test, and change. Extract class refactoring (ECR) is the technique that is used to address this design flaw by trying to extract a set of smaller classes with better quality from the large class. Unfortunately, ECR is a costly process that takes great time and effort when it is conducted completely by hand. Thus, many approaches have been introduced in the literature that tried to automatically suggest the best set of classes that can be extracted from a large class. However, most of these approaches focus on improving the cohesion of the extracted classes yet neglect the coupling between them which can lead to the extraction of highly coupled classes. Therefore, this paper proposes a novel approach that considers the combination of the cohesion and coupling to identify the set of classes that can be extracted from a large class. The proposed approach was empirically evaluated based on real-world Blobs taken from two open-source object-oriented systems. The results of the empirical evaluation revealed that the proposed approach is potentially useful and leads to improvement in the overall quality.