Online reviews about the purchase of products or services provided have become the main source of users’ opinions. In order to gain profit or fame, usually spam reviews are written to promote or demote a few target products or services. This practice is known as review spamming. In the past few years, a variety of methods have been suggested in order to solve the issue of spam reviews. In this study, the researchers carry out a comprehensive review of existing studies on spam review detection using the Systematic Literature Review (SLR) approach. Overall, 76 existing studies are reviewed and analyzed. The researchers evaluated the studies based on how features are extracted from review datasets and different methods and techniques that are employed to solve the review spam detection problem. Moreover, this study analyzes different metrics that are used for the evaluation of the review spam detection methods. This literature review identified two major feature extraction techniques and two different approaches to review spam detection. In addition, this study has identified different performance metrics that are commonly used to evaluate the accuracy of the review spam detection models. Lastly, this work presents an overall discussion about different feature extraction approaches from review datasets, the proposed taxonomy of spam review detection approaches, evaluation measures, and publicly available review datasets. Research gaps and future directions in the domain of spam review detection are also presented. This research identified that success factors of any review spam detection method have interdependencies. The feature’s extraction depends upon the review dataset, and the accuracy of review spam detection methods is dependent upon the selection of the feature engineering approach. Therefore, for the successful implementation of the spam review detection model and to achieve better accuracy, these factors are required to be considered in accordance with each other. To the best of the researchers’ knowledge, this is the first comprehensive review of existing studies in the domain of spam review detection using SLR process.

Humans are blessed with the intelligence to create links, develop semantic metaphors and models for reasoning; construct rules for decision making; and to form bounded loops for interaction, socialization and knowledge sharing. But machines are inadequate with these extraordinary abilities rather, numerous algorithms and mathematical models can be used to connect physical resources with cyberspaces to control objects and, develop cognitive learning for optimal decision making. Connected users and devices in closed virtual and physical proximity give direction towards the plethora of real-world applications for physical, social and, cyber computing. Because of the increase in social media networking and 5G communication links offer real-time crowdsourcing and sensing as a complementary base for information. Proceeding this idea, in this study we have proposed Cyber-Physical and Social Networks (CPSN) for two fundamental operations in IoV (Internet of Vehicles) as CPSN-IoV; (1) to define conceptual architecture of CPSN-IoV for data-oriented network for smart infrastructure and, (2) to create the significant virtual space where the instances of smart vehicles, devices, and things will have meaningful links with the real world objects where, CPSN-IoV will evolve, emerge, compete, and collaborate with all connected objects to strengthen the decision making process. To investigate the potential impact of our proposed study, we have simulated the taxicab trajectory data of the urban city of Portugal in OMNeT++ for the in-depth understanding of road topology, connected vehicles and things, and their traffic trends; and users’ social media streams in respective edge for efficient route planning. The results of simulation demonstrate that our proposed framework has the ability to achieve human-machine intellectual association for managing the smart environment.

Web Services (WSs) are gaining worldwide popularity due to reliable and fast intercommunication services for the development of web and mobile applications. WSs are provided to client application developers through web Application Programming Interfaces (APIs), such as YouTube API, Twitter API, Facebook API, etc. Due to the popularity of WSs, the developers frequently discuss various WSs-based application’ issues on online forums, such as Stack Overflow (SO). The comprehension of such conversations on SO can give insight into the frequency, difficulty, and popularity of different WSs-related problems of developers. We downloaded 12,746 posts from SO relevant to WSs-related issues for this article. We used the topic modeling technique (LDA) to extract various topics from the SO dataset. The topics are labeled and organized into categories and sub-categories according to relationships among them. The difficulty and popularity of each topic have been analyzed. Our investigation yield several findings. First, developers focus on six topics related to WSs on SO: Client APIs development, Data Processing,Web services Authorization, Framework Support,Web APIs, and Mobile Applications. Secondly, the advantages and disadvantages of web applications topic (Fused_Popularity=0.39), from the Clients APIs development category have the highest prevalence, followed by Database (DB) and Data Processing in Applications topic (Fused_Popularity=0.38) from the Data Processing category. Third, most WSs-related topics in all categories are evolving promptly on SO, i.e., new questions are added daily about WSs development, deployment, and authorization. Fourth, the questions of type “how” are primarily asked in Framework support, Client APIs development, and Web APIs categories. Although, many questions in other categories are of the kind “What”. It is also observed that WSs developers not only used SO to ask How and What types of questions but they also used SO to ask information-seeking questions (i.e., in Data processing and Client APIs development categories). Fifth, the topics relevant to Web APIs (Fused_Popularity=10.8) and Client API Development ((Fused_Popularity=9.35) categories of WSs are very popular on SO. Sixth, the questions relevant to the Web APIs (Fused_Difficulty =3) & Client APIs development (Fused_Difficulty=2.25) categories are more difficult than the other four categories. The results of our research may be helpful for the following WSs stakeholders: WSs Client application developers, WSs Educators, and WSs researchers. The WSs Educators and investigators can get more knowledge of new methods and discover novel techniques to make challenging WSs topics easy to understand. WSs framework developers can utilize our extracted WSs topics and categories to know the preferences of WSs developers that may support them in upgrading existing frameworks or developing new ones.

Recent reviews of the literature indicate the need for empirical studies on cross-project defect prediction (CPDP) that would allow aggregation of the evidence and improve predictive performance. Most empirical studies predict defects at granularity levels of method, class, file, and module/package during the coding phase, and thereby avoid external failure costs. The main goal of the study is to perform an empirical study on early defect prediction at the beginning of a project at the product level of granularity for using it as input in planning quality activities of the project. Hence, both internal and external failure costs could be avoided as much as possible through proper planning of quality. We first made a systematic mapping study (SMS) on secondary studies (literature reviews) on defect prediction to identify the most used datasets, the project attributes and metrics utilized as estimators, and the supervised learning methods employed for training the data. Then, we made an empirical study on defect density prediction using cross-project data. We collected 760 project data from the International Software Benchmarking (ISBSG) dataset version 11, which reported both defects and functional size attributes. We trained the prediction models using: i) the complete set of project attributes, ii) the individual attributes, and iii) multiple subsets of attributes. We employed classification and regression approaches of machine learning. The machine learning models are trained using original values of the dataset, and z-score and logged transformations of original values to explore the effects of data normalization on prediction. Most machine learning models trained on the z-score transformation of the dataset performed best for classifying defects. The Multilayer-Perceptron (Neural Network) model trained on the z-score transformation of complete dataset predicted defects with the highest F1-score of 0.89 using binary classification. The logged transformation and feature selection methods improved the results for multivariable regression. The multivariable regression predicted defects with the highest Root Mean Squared Error (RMSE) and R ² (r-squared) values of 0.4 and 0.9, respectively, with a subset of 11 features using logged transformation. The results of classification and regression approaches indicate that defects can be predicted with reasonable accuracy at the software product level using cross-project data.

As technology advances and new features emerge, the demand for Android applications continues to grow, leading to rapid release schedules. These accelerated development timelines often push developers to make rushed changes, often resulting in suboptimal design practices, commonly known as code smells. These issues can degrade application quality, drive up maintenance costs, lead to unexpected behaviors, and complicate evolution and re-engineering efforts. While substantial research has focused on identifying Android-specific and object-oriented code smells, comparatively less attention has been devoted to their systematic refactoring and evaluation. This study introduces a web-based technique, validated through a tool specifically developed to detect 20 Android-specific code smells and automatically refactor 10 of them. Our approach surpasses traditional desktop and plugin solutions by providing easy accessibility, cross-platform compatibility, and eliminating setup requirements. When applied to six open-source and two industrial Android applications and evaluated against the ISO/IEC 25010 quality standard, our tool demonstrated considerable improvements: reducing CPU utilization by 15.39%, lowering memory consumption by 12.85%, and enhancing battery efficiency by up to 5.78%. The tool’s accuracy, validated through precision, recall, and F-measure metrics, achieved averages of 91.81% precision, 97.77% recall, and a 94.67% F-measure. This study enhances the Android application development lifecycle by offering developers a feasible solution for optimizing CPU efficiency, reducing memory use, and minimizing battery consumption.