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Item Application of fuzzy Delphi technique to identify analytical lenses for determining the preparation of free and open source software projects for user experience maturity(Elsevier BV, 2024) Namayala, Phesto P.; Kondo, Tabu S.User eXperience (UX) significantly influences the success of free and open source software (FOSS) projects and is measured using UX capability maturity models (UXCMMs). Every organization desires higher levels of UX maturity; however, it requires upfront preparations and process quality control. Harmonizing processes and analytical lenses for determining preparation for UX maturity are still challenging, and studies to create them are limited. The analysis is ad hoc and based on the actors’ will and experiences. This study proposes and validates analytical lenses. Findings show that UX experts agreed that the lenses could be used with a consensus percentage of 81 %, the threshold value (d) = 0.112, and crisp values greater than α-cut = 0.5. On validation, 47.57 % of stakeholders agreed, and 52.43 % strongly agreed they were relevant. Results help evaluate the status quo and change culture and policies toward ideal preparation. Two areas are suggested for future research.Item Fake review detection techniques, issues, and future research directions: a literature review(Springer Science and Business Media LLC, 2024) Duma, Ramadhani Ally; Niu, Zhendong; Nyamawe, Ally S.; Tchaye-Kondi, Jude; Jingili, Nuru; Yusuf, Abdulganiyu Abdu; Deve, Augustino FaustinoRecently, the impact of product or service reviews on customers' purchasing decisions has become increasingly significant in online businesses. Consequently, manipulating reviews for fame or profit has become prevalent, with some businesses resorting to paying fake reviewers to post spam reviews. Given the importance of reviews in decision-making, detecting fake reviews is crucial to ensure fair competition and sustainable e-business practices. Although significant efforts have been made in the last decade to distinguish credible reviews from fake ones, it remains challenging. Our literature review has identified several gaps in the existing research: (1) most fake review detection techniques have been proposed for high-resource languages such as English and Chinese, and few studies have investigated low-resource and multilingual fake review detection, (2) there is a lack of research on deceptive review detection for reviews based on language code-switching (code-mix), (3) current multi-feature integration techniques extract review representations independently, ignoring correlations between them, and (4) there is a lack of a consolidated model that can mutually learn from review emotion, coarse-grained (overall rating), and fine-grained (aspect ratings) features to supplement the problem of sentiment and overall rating inconsistency. In light of these gaps, this study aims to provide an in-depth literature analysis describing strengths and weaknesses, open issues, and future research directions.Item Exploring the non-linear trajectories of technology adoption in the digital age(Emerald, 2024) Mambile, Cesilia; Ishengoma, FredrickPurpose The objective of this research is to examine the accelerated adoption mechanisms of emerging technologies in information systems. Its goal is to comprehend the drivers behind the prompt assimilation of technology trends such as TikTok, ChatGPT, mobile payment schemes, cryptocurrency and VR. Design/methodology/approach The study follows the systematic literature review methodology (using the PRISMA protocol to guide the selection of scholarly materials from Google Scholar, Scopus and Springer). Specifically, the research draws on identified literature on the adoption trajectories of technologies (ChatGPT, TikTok, cryptocurrency, mobile payment systems, and virtual reality) to systematically assess pertinent insights, and draws on theoretical lenses of Disruptive Innovation Theory to reach interpretations. Findings The study indicates that the prompt assimilation of technology is shaped by several variables such as user-centered design, network effects, content powered through algorithms, viral trends, ease-of-use and accessibility features, engagement levels and retention rates. Research limitations/implications The selection of specific platforms may limit the generalizability of findings. Social implications The emergence of new technologies is causing a shift in societal behaviors and norms, which has significant social implications. While platforms such as TikTok offer opportunities for community-building, there are concerns regarding digital divide and privacy issues that need to be addressed. So understanding the impact of these changes becomes vital for achieving fairness in access and making technology's potential transformation practicalized effectively. Originality/value This research enhances the current body of literature by presenting a thorough examination of the non-linear patterns involved in adopting advanced technologies. By combining knowledge from numerous fields, this study delivers an integrated comprehension regarding what factors prompt swift adoption.Item The Nexus of Big Data and Big Data Analytics for Managerial Business Decision-Making: A Systematic Review Analysis(University of Dar es Salaam, 2024) Didas, Matendo; Chali, Frederick; Elisa, NoeThe growing usage of big data and big data analytics in business has prompted academics and professionals to widen their examination of their implications in business decision-making procedures. Until now, academics and business leaders have concentrated solely on the technical components of big data and analytics, ignoring the impact they have on the effectiveness of commercial decision-making systems. To begin, this paper intends to review the literature on the study of the relationship between the use of big data and big data analytics for its effectiveness in business industrial decision-making systems. Second, it gives important facts to assess whether big data and big data analytics catalyze the deployment of sophisticated business intelligence and informed decision-making representations. In this regard, the paper identifies the essential concerns that underpin the business-driven decision-making processes such as efficiency, and preciseness among others. Fundamentally, the current work contributes to the literature on big data and big data analytics for business-driven decision-making in both a theoretical fashion and provides a shot for future agenda possibilities to develop knowledge in this area.Item On the Impact of Refactorings on Software Attack Surface(Institute of Electrical and Electronics Engineers (IEEE), 2024) Edward, Estomii; Nyamawe, Ally S.; Elisa, NoeRefactoring is one of the techniques mostly employed by software developers to improve the quality attributes of their systems. However, little has been done to investigate how refactoring operations specifically aimed at improving the internal structure of software can impact its security. Refactoring usually entails different code change operations including the decomposition of classes, methods, and the reallocation of code elements. While this refinement aims to improve the internal design of a system, it might inadvertently disperse security-critical code elements throughout the codebase. Consequently, such dispersion could affect the software attack surface. To this end, this paper presents an empirical study of 30 open-source software systems developed in Python, C, and Javascript. The study scrutinized two subsequent versions of each subject application to uncover the refactoring operations applied and the trend of the software attack surface. Specifically, the study focused on the injection or removal of bugs, code smells and other vulnerabilities aiming to discern the impact of refactorings on the software attack surface. Data was collected using wellknown tools, namely SonarQube, RefDiff, and PyReff. The findings suggest that refactorings can have multiple impacts (i.e., positive, negative, or neutral) on bugs, code smells, and vulnerabilities. The findings further confirm that developers must be aware of the combination or sequence of refactoring operations that can improve software quality without compromising its securityItem Revolutionizing decision support: a systematic literature review of contextual implementation models for electronic health records systems(Emerald, 2024) Mwogosi, Augustino; Shao, Deo; Kibusi, Stephen; Kapologwe, NtuliPurpose This study aims to assess previously developed Electronic Health Records System (EHRS) implementation models and identify successful models for decision support. Design/methodology/approach A systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The data sources used were Scopus, PubMed and Google Scholar. The review identified peer-reviewed papers published in the English Language from January 2010 to April 2023, targeting well-defined implementation of EHRS with decision-support capabilities in healthcare. To comprehensively address the research question, we ensured that all potential sources of evidence were considered, and quantitative and qualitative studies reporting primary data and systematic review studies that directly addressed the research question were included in the review. By including these studies in our analysis, we aimed to provide a more thorough and reliable evaluation of the available evidence. Findings The findings suggest that the success of EHRS implementation is determined by organizational and human factors rather than technical factors alone. Successful implementation is dependent on a suitable implementation framework and management of EHRS. The review identified the capabilities of Clinical Decision Support (CDS) tools as essential in the effectiveness of EHRS in supporting decision-making. Originality/value This study contributes to the existing literature on EHRS implementation models and identifies successful models for decision support. The findings can inform future implementations and guide decision-making in healthcare facilities.Item SES-Net: A Novel Multi-Task Deep Neural Network Model for Analyzing E-learning Users’ Satisfaction via Sentiment, Emotion, and Semantic(Informa UK Limited, 2024) Sandiwarno, Sulis; Niu, Zhendong; Nyamawe, Ally S.Abstract Understanding users’ satisfaction is fundamental for enhancing the effectiveness and usability of e-learning platforms. The existing approaches for analyzing users’ satisfaction leverage word embedding vectors to represent sentiment information, but they often fail to fully address the complex relationship between emotional and semantic information. Additionally, several emotional and semantic word embedding models are proposed, but they require sentiment information. In this study, we propose a novel multi-task deep neural model, called Sentiment-Emotion-Semantic Network (SES-Net), capable of learning sentiment, emotion, and semantic information simultaneously. The proposed model comprises three main sub-neural tasks: Bidirectional Long Short-Term Memory (BiLSTM) to capture sentiment, BiLSTM to extract semantics, and Convolutional Neural Networks (CNN) to learn emotional features. Experimental results reveal that, SES-Net outperforms the previous approaches by achieving an average F1-score of 90.59%.Item A framework for security improvement on usage of mobile money application based on iris biometric authentication method(Informa UK Limited, 2024) Rashidi, Florence U.; Mohsini, Mustafa H.; Mega, BakariOffering transactions through mobile devices has many advantages, such as cashless payments, lower transaction costs, and provide employment opportunities. However, it introduces access security challenges that must be dealt with, which may allow unauthorized access, resulting in theft. This work proposes a framework to improve security on the usage of Mobile Money Services (MMS) by using two-factor authentication (2FA) of PIN and iris biometric authentication method (IRBAM). The rapid application development (RAD) approach was used to develop mobile money applications based on the proposed framework. The proposed framework will improve the security of accessing MMS.Item A deep feature interaction and fusion model for fake review detection: Advocating heterogeneous graph convolutional network(Elsevier BV, 2024) Duma, Ramadhani Ally; Niu, Zhendong; Nyamawe, Ally S.; Manjotho, Ali AsgharIn contemporary real-world scenarios, opinion spammers are hired to fabricate reviews that unfairly promote or demote particular products or services for personal gain. Although considerable attention has been devoted to addressing the problem, existing approaches often overlook the heterogeneous nature of reviewer–product interactions. Specifically, the correlation between review text (comments) and overall ratings, which provides various latent rich information to expose fake reviews, remains inadequately explored. Current methodologies focus on limited interactions, such as reviewer–review, product–review, or reviewer–product interactions, while neglecting significant aspects like reviewer–review–product and reviewer–rating–product interactions, leading to inadequate classifier performance. Motivated by this observation, this study proposes a novel Deep Feature Interaction and Fusion Model (DFIFM) whose ideas are five-folds: (a) constructing a reviewer–product interaction bipartite graph that represents heterogeneous feature node interactions through review text and overall rating values; (b) recognizing the existing mutual interactive relationship between review text and overall rating features, we construct a unified GCN to gain additional insights into feature relationships and capture mutual heterogeneous interactions between nodes; (c) to handle the encoding of unstructured review text features as edge attributes, we adopt a convolutional neural network (CNN); (d) attention mechanisms and fusion techniques are employed to capture interdependencies among reviewer–product latent features; and (e) a Multilayer Perceptron (MLP) utilizes the resulting latent feature representation for review classification. Experimental results on three publicly available datasets demonstrate its superiority over state-of-the-art baselines.Item Quantifying vulnerabilities: A systematic review of the state-of-the-art Web-Based Systems(University of Dar es Salaam, 2024) Masue, Wilbard G.; Ngondya, Daniel; Kondo, Tabu SWeb-based Systems Vulnerabilities (WSVs) have been existing over a long time in all Open System Interconnection (OSI) layers. WSV tends to affect online business operations by letting attackers to gain unauthorized access. Different researchers have been publishing common WSVs regularly. From the published vulnerabilities, it can be noted that the ranking of vulnerabilities is not static. Prevalence of common vulnerabilities tends to vary with time. Moreover, ranking of vulnerabilities from various practitioners, such as OWASP and CWE, at a particular point in time tends to be different because of different approaches and sources. This work sought to come up with an objective way of establishing the latest ranking of common WSV by conducting a Systematic Literature Review from scholarly sources. This study extracted 127 publications from Scholarly Databases: Association of Computing Machineries, ScienceDirect, Springer, IEEE, and Google scholar. After the review, only 62 articles were considered based on five inclusion and exclusion criteria. The review reveals that cross site script, structured query language injection, broken authentication and session management, operating system command injection and file inclusion are the most common WSV.Item A deep feature interaction and fusion model for fake review detection: Advocating heterogeneous graph convolutional network(Elsevier BV, 2024) Duma, R. A.; Niu, Z.; Nyamawe, A. S.; Manjotho, A. A.In contemporary real-world scenarios, opinion spammers are hired to fabricate reviews that unfairly promote or demote particular products or services for personal gain. Although considerable attention has been devoted to addressing the problem, existing approaches often overlook the heterogeneous nature of reviewer–product interactions. Specifically, the correlation between review text (comments) and overall ratings, which provides various latent rich information to expose fake reviews, remains inadequately explored. Current methodologies focus on limited interactions, such as reviewer–review, product–review, or reviewer–product interactions, while neglecting significant aspects like reviewer–review–product and reviewer–rating–product interactions, leading to inadequate classifier performance. Motivated by this observation, this study proposes a novel Deep Feature Interaction and Fusion Model (DFIFM) whose ideas are five-folds: (a) constructing a reviewer–product interaction bipartite graph that represents heterogeneous feature node interactions through review text and overall rating values; (b) recognizing the existing mutual interactive relationship between review text and overall rating features, we construct a unified GCN to gain additional insights into feature relationships and capture mutual heterogeneous interactions between nodes; (c) to handle the encoding of unstructured review text features as edge attributes, we adopt a convolutional neural network (CNN); (d) attention mechanisms and fusion techniques are employed to capture interdependencies among reviewer–product latent features; and (e) a Multilayer Perceptron (MLP) utilizes the resulting latent feature representation for review classification. Experimental results on three publicly available datasets demonstrate its superiority over state-of-the-art baselines.Item Revolutionizing decision support: a systematic literature review of contextual implementation models for electronic health records systems(Emerald, 2024) Mwogosi, A.; Shao, D.; Kibusi, S; Kapologwe, N.Purpose – This study aims to assess previously developed Electronic Health Records System (EHRS) implementation models and identify successful models for decision support. Design/methodology/approach – A systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The data sources used were Scopus, PubMed and Google Scholar. The review identified peer-reviewed papers published in the English Language from January 2010 to April 2023, targeting well-defined implementation of EHRS with decision-support capabilities in healthcare. To comprehensively address the research question, we ensured that all potential sources of evidence were considered, and quantitative and qualitative studies reporting primary data and systematic review studies that directly addressed the research question were included in the review. By including these studies in our analysis, we aimed to provide a more thorough and reliable evaluation of the available evidence. Findings – The findings suggest that the success of EHRS implementation is determined by organizational and human factors rather than technical factors alone. Successful implementation is dependent on a suitable implementation framework and management of EHRS. The review identified the capabilities of Clinical Decision Support (CDS) tools as essential in the effectiveness of EHRS in supporting decision-making. Originality/value – This study contributes to the existing literature on EHRS implementation models and identifies successful models for decision support. The findings can inform future implementations and guide decision-making in healthcare facilities.Item Nexus of Big Data and Big Data Analytics for Managerial Business Decision-Making: A Systematic Review Analysis(University of Dar es Salaam, 2024) Didas, M; Chali, F; Elisa, N.The growing usage of big data and big data analytics in business has prompted academics and professionals to widen their examination of their implications in business decision-making procedures. Until now, academics and business leaders have concentrated solely on the technical components of big data and analytics, ignoring the impact they have on the effectiveness of commercial decision-making systems. To begin, this paper intends to review the literature on the study of the relationship between the use of big data and big data analytics for its effectiveness in business industrial decision-making systems. Second, it gives important facts to assess whether big data and big data analytics catalyze the deployment of sophisticated business intelligence and informed decision-making representations. In this regard, the paper identifies the essential concerns that underpin the business-driven decision-making processes such as efficiency, and preciseness among others. Fundamentally, the current work contributes to the literature on big data and big data analytics for business-driven decision-making in both a theoretical fashion and provides a shot for future agenda possibilities to develop knowledge in this area.Item Exploring the non-linear trajectories of technology adoption in the digital age(Emerald, 2024) Mambile, C.; Ishengoma, F.Purpose – The objective of this research is to examine the accelerated adoption mechanisms of emerging technologies in information systems. Its goal is to comprehend the drivers behind the prompt assimilation oftechnology trends such as TikTok, ChatGPT, mobile payment schemes, cryptocurrency and VR. Design/methodology/approach – The study follows the systematic literature review methodology (using the PRISMA protocol to guide the selection of scholarly materials from Google Scholar, Scopus and Springer). Specifically, the research draws on identified literature on the adoption trajectories of technologies (ChatGPT, TikTok, cryptocurrency, mobile payment systems, and virtual reality) to systematically assess pertinent insights, and draws on theoretical lenses of Disruptive Innovation Theory to reach interpretations. Findings –The study indicates that the prompt assimilation of technology is shaped by several variables such as user-centered design, network effects, content powered through algorithms, viral trends, ease-of-use and accessibility features, engagement levels and retention rates. Research limitations/implications – The selection of specific platforms may limit the generalizability of findings. Social implications – The emergence of new technologies is causing a shift in societal behaviors and norms, which has significant social implications. While platforms such as TikTok offer opportunities for communitybuilding, there are concerns regarding digital divide and privacy issues that need to be addressed. So understanding the impact of these changes becomes vital for achieving fairness in access and making technology’s potential transformation practicalized effectively. Originality/value – This research enhances the current body of literature by presenting a thorough examination of the non-linear patterns involved in adopting advanced technologies. By combining knowledge from numerous fields, this study delivers an integrated comprehension regarding what factors prompt swift adoption.Item Fake review detection techniques, issues, and future research directions: a literature review(Springer Science and Business Media LLC, 2024) Duma, R. A.; Niu, Z.; Nyamawe, A.S.; Tchaye-Kondi, J.; Jingili, N; Yusuf, A. A.; Deve, A. FRefactoring is one of the techniques mostly employed by software developers to improve the quality attributes of their systems. However, little has been done to investigate how refactoring operations specifically aimed at improving the internal structure of software can impact its security. Refactoring usually entails different code change operations including the decomposition of classes, methods, and the reallocation of code elements. While this refinement aims to improve the internal design of a system, it might inadvertently disperse security-critical code elements throughout the codebase. Consequently, such dispersion could contribute to an increase in the software attack surface. To this end, this paper presents an empirical study conducted on 30 open-source software systems that were developed in Python, C, and Java. The study scrutinized two subsequent versions of each subject application to uncover the refactoring operations applied and the trend of security vulnerabilities. Specifically, the study focused on the injection or removal of bugs, code smells and other vulnerabilities aiming to discern the impact of refactorings on the software attack surface. Data was collected using well-known tools, namely SonarQube, RefDiff, and PyReff. The findings suggest that refactorings can have multiple effects (i.e., positive, negative, or neutral) on bugs, code smells, and vulnerabilities. The findings further confirm that developers must be aware of the combination or sequence of refactoring operations that can improve software quality without compromising its security.Item Application of fuzzy Delphi technique to identify analytical lenses for determining the preparation of free and open source software projects for user experience maturity(Elsevier BV, 2024) Namayala, P. P.; Kondo, T. S.User eXperience (UX) significantly influences the success of free and open source software (FOSS) projects and is measured using UX capability maturity models (UXCMMs). Every organization desires higher levels of UX maturity; however, it requires upfront preparations and process quality control. Harmonizing processes and analytical lenses for determining preparation for UX maturity are still challenging, and studies to create them are limited. The analysis is ad hoc and based on the actors’ will and experiences. This study proposes and validates analytical lenses. Findings show that UX experts agreed that the lenses could be used with a consensus percentage of 81 %, the threshold value (d) = 0.112, and crisp values greater than α-cut = 0.5. On validation, 47.57 % of stakeholders agreed, and 52.43 % strongly agreed they were relevant. Results help evaluate the status quo and change culture and policies toward ideal preparation. Two areas are suggested for future research.Item On the Impact of Refactorings on Software Attack Surface(Institute of Electrical and Electronics Engineers (IEEE), 2024) Edward, E.; Nyamawe, A. S.; Elisa, N.Refactoring is one of the techniques mostly employed by software developers to improve thequality attributes of their systems. However, little has been done to investigate how refactoring operations specifically aimed at improving the internal structure of software can impact its security. Refactoring usually entails different code change operations including the decomposition of classes, methods, and the reallocation of code elements. While this refinement aims to improve the internal design of a system, it might inadvertently disperse security-critical code elements throughout the codebase. Consequently, such dispersion could affect the software attack surface. To this end, this paper presents an empirical study of 30 open-source software systems developed in Python, C, and Javascript. The study scrutinized two subsequent versions of each subject application to uncover the refactoring operations applied and the trend of the software attack surface. Specifically, the study focused on the injection or removal of bugs, code smells and other vulnerabilities aiming to discern the impact of refactorings on the software attack surface. Data was collected using wellknown tools, namely SonarQube, RefDiff, and PyReff. The findings suggest that refactorings can have multiple impacts (i.e., positive, negative, or neutral) on bugs, code smells, and vulnerabilities. The findings further confirm that developers must be aware of the combination or sequence of refactoring operations that can improve software quality without compromising its security.Item SES-Net: A Novel Multi-Task Deep Neural Network Model for Analyzing E-learning Users’ Satisfaction via Sentiment, Emotion, and Semantic(Informa UK Limited, 2024) Sandiwarno, S.; Niu, Z.; Nyamawe, A. S.Understanding users’ satisfaction is fundamental for enhancing the effectiveness and usability of e-learning platforms. The existing approaches for analyzing users’ satisfaction leverage word embedding vectors to represent sentiment information, but they often fail to fully address the complex relationship between emotional and semantic information. Additionally, several emotional and semantic word embedding models are proposed, but they require sentiment information. In this study, we propose a novel multi-task deep neural model, called Sentiment-Emotion-Semantic Network (SES-Net), capable of learning sentiment, emotion, and semantic information simultaneously. The proposed model comprises three main sub-neural tasks: Bidirectional Long Short-Term Memory (BiLSTM) to capture sentiment, BiLSTM to extract semantics, and Convolutional Neural Networks (CNN) to learn emotional features. Experimental results reveal that, SES-Net outperforms the previous approaches by achieving an average F1-score of 90.59%.Item A deep feature interaction and fusion model for fake review detection: Advocating heterogeneous graph convolutional network(Elsevier BV, 2024) Duma, R. A.; Niu, Z.; Nyamawe, A. S.; Manjotho, A. A.In contemporary real-world scenarios, opinion spammers are hired to fabricate reviews that unfairly promote or demote particular products or services for personal gain. Although considerable attention has been devoted to addressing the problem, existing approaches often overlook the heterogeneous nature of reviewer–product interactions. Specifically, the correlation between review text (comments) and overall ratings, which provides various latent rich information to expose fake reviews, remains inadequately explored. Current methodologies focus on limited interactions, such as reviewer–review, product–review, or reviewer–product interactions, while neglecting significant aspects like reviewer–review–product and reviewer–rating–product interactions, leading to inadequate classifier performance. Motivated by this observation, this study proposes a novel Deep Feature Interaction and Fusion Model (DFIFM) whose ideas are five-folds: (a) constructing a reviewer–product interaction bipartite graph that represents heterogeneous feature node interactions through review text and overall rating values; (b) recognizing the existing mutual interactive relationship between review text and overall rating features, we construct a unified GCN to gain additional insights into feature relationships and capture mutual heterogeneous interactions between nodes; (c) to handle the encoding of unstructured review text features as edge attributes, we adopt a convolutional neural network (CNN); (d) attention mechanisms and fusion techniques are employed to capture interdependencies among reviewer–product latent features; and (e) a Multilayer Perceptron (MLP) utilizes the resulting latent feature representation for review classification. Experimental results on three publicly available datasets demonstrate its superiority over state-of-the-art baselines.Item Quantifying Vulnerabilities: A Systematic Review of the State-of-the-Art Web-Based Systems(University of Dar es Salaam, 2024) Masue, W. G.; Ngondya, D.; Kondo, T. S.Web-based Systems Vulnerabilities (WSVs) have been existing over a long time in all Open System Interconnection (OSI) layers. WSV tends to affect online business operations by letting attackers to gain unauthorized access. Different researchers have been publishing common WSVs regularly. From the published vulnerabilities, it can be noted that the ranking of vulnerabilities is not static. Prevalence of common vulnerabilities tends to vary with time. Moreover, ranking of vulnerabilities from various practitioners, such as OWASP and CWE, at a particular point in time tends to be different because of different approaches and sources. This work sought to come up with an objective way of establishing the latest ranking of common WSV by conducting a Systematic Literature Review from scholarly sources. This study extracted 127 publications from Scholarly Databases: Association of Computing Machineries, ScienceDirect, Springer, IEEE, and Google scholar. After the review, only 62 articles were considered based on five inclusion and exclusion criteria. The review reveals that cross site script, structured query language injection, broken authentication and session management, operating system command injection and file inclusion are the most common WSV.