Recent Advances in Computer Science and Communications - Volume 16, Issue 3, 2023

Background and Objectives: A Geographic Information System (GIS) is a system designed to capture, store, analyze, and manage all types of geographical data. Mobile GIS has the ability to deliver functionalities, data and services without necessarily requiring a fixed location or wired connection. This paper analyzes the functionalities and potential of free mobile GIS applications available in Google Play Store and Apple App Store. Methods: A well-known Systematic Literature Review (SLR) protocol was carried out to study free mobile GIS apps functionalities. A quality assessment questionnaire was developed for this purpose to be applied to the selected mobile GIS apps. Results: A total of 42 mobile GIS apps were selected from Apple App Store and Google Play Store. The results showed that the majority of mobile GIS apps support Global Positioning System (GPS) and were designed to be mainly used in geography, topography, Geo-Positioning, and transport domains. The search also showed that the Open Geospatial Consortium (OGC) protocols for web services remain less integrated in the apps. Conclusion: The features of mobile GIS were discussed and a set of recommendations to improve the development of mobile GIS applications was proposed.

Background: Software maintenance is known as a laborious activity in the software lifecycle and is often considered more expensive than other activities. Open-Source Software (OSS) has gained considerable acceptance in the industry recently, and the Maintenance Effort Estimation (MEE) of such software has emerged as an important research topic. In this context, researchers have conducted a number of open-source software maintenance effort estimation (OMEE) studies based on statistical as well as machine learning techniques for better estimation. Objective: The objective of this study is to perform a systematic literature review (SLR) to analyze and summarize the empirical evidence of O-MEE ML techniques in current research through a set of five Research Questions (RQs) related to several criteria (e.g. data pre-processing tasks, data mining tasks, tuning parameter methods, accuracy criteria and statistical tests, as well as ML techniques reported in the literature that outperformed). Methods: We performed a systematic literature review of 36 primary empirical studies published from 2000 to June 2020, selected based on an automated search of six digital databases. Results: The findings show that Bayesian networks, decision tree, support vector machines and instance-based reasoning were the ML techniques most used; few studies opted for ensemble or hybrid techniques. Researchers have paid less attention to O-MEE data pre-processing in terms of feature selection, methods that handle missing values and imbalanced datasets, and tuning parameters of ML techniques. Classification data mining is the task most addressed using different accuracy criteria such as Precision, Recall, and Accuracy, as well as Wilcoxon and Mann-Whitney statistical tests. Conclusion: This SLR identifies a number of gaps in the current research and suggests areas for further investigation. For instance, since OSS includes different data source formats, researchers should pay more attention to data pre-processing and develop new models using ensemble techniques since they have proved to perform better.

Background: The design of Autonomous Systems must consider multiple elements of the system, such as agents, physical objects and their software counterparts, control mechanisms, sensors, actuators, and other components. All these distributed elements in the environment make the necessity of creating multiple views for design, including system coordination, development, structural, and behavior views. Therefore, Software/Systems Architectures have been recognized as an important element in process development to manage the systems' complexity. Objective: The objective of this article is to describe a review of architecture characteristics, approaches, styles, and standards that are commonly considered for the development of autonomous systems. Methods: First, we describe important elements of software architecture, as well as the standards used in this field. Then, we describe the types of approaches for architecture design. In addition, we provide a classification of software/systems architectures for autonomous systems. Results: As a result, we present a review on the Software/Systems Architecture description for Autonomous Systems. We also find that formal architecture standards are rarely considered in practice, and a large variety of nonfunctional requirements is mentioned. Conclusion: As autonomous systems deal with many components interacting with the real world, under certain quality constraints, considering trade-offs and decisions, software/system architectures are highly relevant for managing all this complexity. A list of main challenges for autonomous systems is described and then discussed according to a review of the literature. This review can be useful for professionals and researchers in identifying software/systems architecture as an important technical element for developing autonomous systems.

Background: The popularity of DevSecOps is on the rise because it promises to integrate a greater degree of security into software delivery pipelines. However, there is also an unacceptable risk related to safety that cannot be overlooked, given the importance of this aspect in many industries. Objective: The objective of this study is to provide an overview of the safety aspects reported in the literature on DevSecOps. This study also characterizes such aspects and identifies the gaps that may lead to future research work. Methods: A systematic literature review was conducted using five well-known academic databases. The search was executed in September 2021 and March 2022 to identify relevant studies. Results: The search returned 114 academic studies. After the screening process, five primary studies published between 2019 and 2021 were selected. These studies were analyzed thoroughly to identify the safety aspects. Then, we categorized them into three main groups: (i) risk-related safety aspects, (ii) human-related aspects, and (iii) management aspects. Conclusion: Safety is an important characteristic that is becoming more critical as the number of critical systems grows. This review reveals that only a scarce number of studies are focusing on safety in DevSecOps. However, those studies gave us some insights into this topic. Therefore, our main observation is that this topic has not yet been completely explored in the academic literature. This review can encourage reflection and discussion between the safety and security communities.

Background: In contrast to the high-interest rate in Artificial Intelligence (AI) for business, AI adoption is much lower. It has been found that a lack of consumer trust would adversely influence consumers’ evaluations of information given by AI. Hence the need for explanations in model results. Methods: This is especially the case in clinical practice and juridical enforcement, where improvements in prediction and interpretation are crucial. Bio-signals analysis, such as EEG diagnosis, usually involves complex learning models, which are difficult to explain. Therefore, the explanatory module is imperative if the results are released to the general public. This research shows a systematic review of explainable artificial intelligence (XAI) advancement in the research community. Recent XAI efforts on bio-signals analysis were reviewed. The explanatory models favor the interpretable model approach due to the popularity of deep learning models in many use cases. Results: The verification and validation of explanatory models appear to be one of the crucial gaps in XAI bio-signals research. Currently, human expert evaluation is the easiest validation approach. Although the bio-signals community highly trusts the human-directed approach, it suffers from persona and social bias issues. Conclusion: Hence, future research should investigate more objective evaluation measurements towards achieving the characteristics of inclusiveness, reliability, transparency, and consistency in the XAI framework.

Aims: This paper aims to conduct a Systematic Literature Review (SLR) of the relative applications of text mining in cybersecurity. Objectives: The amount of data generated worldwide has been attributed to a change in different activities associated with cyber security, and demands a high automation level. Methods: In the cyber security domain, text mining is an alternative for improving the usefulness of various activities that entail unstructured data. This study searched databases of 516 papers from 2015 to 2021. Out of which, 75 papers are selected for analysis. A detailed evaluation of the selected studies employs sources, techniques, and information extraction on cyber security applications. Results: This study extends gaps for future studies, such as text processing, availability of datasets, innovative methods, and intelligent text mining. Conclusion: This study concludes with interesting findings of employing text mining in cybersecurity applications; the researchers need to exploit all related techniques and algorithms in text mining to detect and protect the organization from Cybersecurity applications.