Requirement Engineering for E-Coaching in Higher Education

This paper presents a requirement engineering for developing an e-coaching environment in the higher education sector. This research demonstrates that IT experts encounter challenges in establishing a system that matches a university’s expectations, as they are usually uncertain about its goals and system requirements. The paper illustrates a business goal-focused requirement induction technique, which encompasses demonstrating the business procedures through Business Process Modelling Notation (BPMN), assessing the university goals via the tree diagram, and drawing out the system requirements from the university objectives through UML state diagrams. A case study of supporting the development of a new IT course is used as a case study and applied using BPMN.

Quintessence of Traditional and Agile Requirement Engineering

Requirement gathering for software development project is the most crucial stage and thus requirement engineering (RE) occupies the chief position in the software development. Countless techniques concerning the RE processes exist to make sure the requirements are coherent, compact and complete in all respects. In this way different aspects of RE are dissected and detailed upon. A comparison of RE in Agile and RE in Waterfall is expatiated and on the basis of the literature survey the overall Agile RE process is accumulated. Agile being a technique produces high quality software in relatively less time as compared to the conventional waterfall methodology. The paramount objective of this study is to take lessons from RE that Agile method may consider, if quality being the cardinal concern. The study is patterned on the survey of the previous research reported in the coexisting literature and the practices which are being pursued in the area.

Classification and Comprehension of Software Requirements Using Ensemble Learning

The software development process mostly depends on accurately identifying both essential and optional features.Initially,user needs are typically expressed in free-form language,requiring significant time and human resources to translate these into clear functional and non-functional requirements.To address this challenge,various machine learning(ML)methods have been explored to automate the understanding of these requirements,aiming to reduce time and human effort.However,existing techniques often struggle with complex instructions and large-scale projects.In our study,we introduce an innovative approach known as the Functional and Non-functional Requirements Classifier(FNRC).By combining the traditional random forest algorithm with the Accuracy Sliding Window(ASW)technique,we develop optimal sub-ensembles that surpass the initial classifier’s accuracy while using fewer trees.Experimental results demonstrate that our FNRC methodology performs robustly across different datasets,achieving a balanced Precision of 75%on the PROMISE dataset and an impressive Recall of 85%on the CCHIT dataset.Both datasets consistently maintain an F-measure around 64%,highlighting FNRC’s ability to effectively balance precision and recall in diverse scenarios.These findings contribute to more accurate and efficient software development processes,increasing the probability of achieving successful project outcomes.

Crowdsourced Requirements Engineering Challenges and Solutions:A Software Industry Perspective

Software crowdsourcing(SW CS)is an evolving software development paradigm,in which crowds of people are asked to solve various problems through an open call(with the encouragement of prizes for the top solutions).Because of its dynamic nature,SW CS has been progressively accepted and adopted in the software industry.However,issues pertinent to the understanding of requirements among crowds of people and requirements engineers are yet to be clarified and explained.If the requirements are not clear to the development team,it has a significant effect on the quality of the software product.This study aims to identify the potential challenges faced by requirements engineers when conducting the SW–CS based requirements engineering(RE)process.Moreover,solutions to overcome these challenges are also identified.Qualitative data analysis is performed on the interview data collected from software industry professionals.Consequently,20 SW–CS based RE challenges and their subsequent proposed solutions are devised,which are further grouped under seven categories.This study is beneficial for academicians,researchers and practitioners by providing detailed SW–CS based RE challenges and subsequent solutions that could eventually guide them to understand and effectively implement RE in SW CS.

Effective Hybrid Content-Based Collaborative Filtering Approach for Requirements Engineering

Requirements engineering(RE)is among the most valuable and critical processes in software development.The quality of this process significantly affects the success of a software project.An important step in RE is requirements elicitation,which involves collecting project-related requirements from different sources.Repositories of reusable requirements are typically important sources of an increasing number of reusable software requirements.However,the process of searching such repositories to collect valuable project-related requirements is time-consuming and difficult to perform accurately.Recommender systems have been widely recognized as an effective solution to such problem.Accordingly,this study proposes an effective hybrid content-based collaborative filtering recommendation approach.The proposed approach will support project stake-holders in mitigating the risk of missing requirements during requirements elicitation by identifying related requirements from software requirement repositories.The experimental results on the RALIC dataset demonstrate that the proposed approach considerably outperforms baseline collaborative filtering-based recom-mendation methods in terms of prediction accuracy and coverage in addition to mitigating the data sparsity and cold-start item problems.

Icons:Visual Representation to Enrich Requirements Engineering Work

Adapting icons in requirements engineering can support the multifaceted needs of stakeholders. Conventional approaches to RE are mainly highlighted in diagrams. This paper introduces icon-based information as a way to represent ideas and concepts in the requirements engineering domain. We report on icon artifacts that support requirements engineering work such as priority types, status states and stakeholder kinds. We evaluate how users interpret meanings of icons and the efficacy of icon prototypes shaped to represent those requirements attributes. Our hypothesis is whether practitioners can recognize the icons’ meaning in terms of their functional representation. According to the empirical data from 45 participants, the findings demonstrate the probability of providing users with icons and their intended functions that correspond to RE artifacts in a novel yet effective manner. Based on these findings, we suggest that icons could enrich stakeholders’ perception of the RE process as a whole;however, meaningful interpretation of an icon is subject to the user’s prior knowledge and experience.

On the Assessment of Generative AI in Requirements Analysis and Modeling Tasks with UML:An Exploratory Study

Generative AI is rapidly employed by software developers to generate code or other software artifacts.However,the analysis and assessment of generative AI with respect to requirements analysis and modeling tasks,especially with UML,has received little attention.This paper investigates the capabilities of generative AI to aid in the creation of three types of UML models:UML use case models,class diagrams,and sequence diagrams.For this purpose,we designed an AI-aided UML modeling task in our course on software requirements modeling.50 undergraduates who majored in Software Engineering at Wuhan University completed the modeling task and the corresponding online survey.Our findings show that generative AI can help create these three types of UML models,but its performance is limited to identifying essential modeling elements of these UML models.

An Approach towards Goal-Oriented Requirements Ontology: Consistency and Completeness Based Requirements Analysis

The paper presents a new approach to managing software requirement elicitation techniques with a high level of analyses based on domain ontology techniques, where we established a mapping between user scenario, structured requirement, and domain ontology techniques to improve many attributes such as requirement consistency, completeness and eliminating duplicate requirements to reduce risk of overrun time and budgets. One of the main targets of requirement engineering is to develop a requirement document with high quality. So, we proposed a user interface to collect all vital information about the project directly from the regular user and requirement engineering;After that, the proposal will generate an ontology based on semantic relations and rules. Requirements Engineering tries to keep requirements throughout a project’s life cycle consistent necessities clear, and up to date. This prototype allows mapping requirement scenarios into ontology elements for semantically interrupted. The general points of our prototype are to guarantee the identification requirements and improved nature of the Software Requirements Specification (SRS) by solving incomplete and conflicting information in the requirements specification.

Fine-Tuning Cyber Security Defenses: Evaluating Supervised Machine Learning Classifiers for Windows Malware Detection

Malware attacks on Windows machines pose significant cybersecurity threats,necessitating effective detection and prevention mechanisms.Supervised machine learning classifiers have emerged as promising tools for malware detection.However,there remains a need for comprehensive studies that compare the performance of different classifiers specifically for Windows malware detection.Addressing this gap can provide valuable insights for enhancing cybersecurity strategies.While numerous studies have explored malware detection using machine learning techniques,there is a lack of systematic comparison of supervised classifiers for Windows malware detection.Understanding the relative effectiveness of these classifiers can inform the selection of optimal detection methods and improve overall security measures.This study aims to bridge the research gap by conducting a comparative analysis of supervised machine learning classifiers for detecting malware on Windows systems.The objectives include Investigating the performance of various classifiers,such as Gaussian Naïve Bayes,K Nearest Neighbors(KNN),Stochastic Gradient Descent Classifier(SGDC),and Decision Tree,in detecting Windows malware.Evaluating the accuracy,efficiency,and suitability of each classifier for real-world malware detection scenarios.Identifying the strengths and limitations of different classifiers to provide insights for cybersecurity practitioners and researchers.Offering recommendations for selecting the most effective classifier for Windows malware detection based on empirical evidence.The study employs a structured methodology consisting of several phases:exploratory data analysis,data preprocessing,model training,and evaluation.Exploratory data analysis involves understanding the dataset’s characteristics and identifying preprocessing requirements.Data preprocessing includes cleaning,feature encoding,dimensionality reduction,and optimization to prepare the data for training.Model training utilizes various supervised classifiers,and their performance is evaluated using metrics such as accuracy,precision,recall,and F1 score.The study’s outcomes comprise a comparative analysis of supervised machine learning classifiers for Windows malware detection.Results reveal the effectiveness and efficiency of each classifier in detecting different types of malware.Additionally,insights into their strengths and limitations provide practical guidance for enhancing cybersecurity defenses.Overall,this research contributes to advancing malware detection techniques and bolstering the security posture of Windows systems against evolving cyber threats.

A Formal Method to Model Early Requirement of Multi-Agent System

A formal specification language iFL based on i* framework is presented in this paper to formally specify and analyze the early requirment of multi-agent system. It is a branching temporal logic which defines the concepts and models in i* framework in a rigorous way. The method to transform the i* models to iFL formal specification is also put forward. Key words Agent - early requirement - logic - requirement engineering CLC number TP 18 Foundation item: Supported by the National Natural Science Foundation of China (60373022)Biography: MAO Xin-jun (1970-), male, Ph. D, Associate professor, research direction: object-agent software engineering, Agent theory.

Usability Evaluation Through Fuzzy AHP-TOPSIS Approach: Security Requirement Perspective

Most of the security strategies today are primarily designed to provide security protection,rather than to solve one of the basic security issues related to adequate software product architecture.Several models,frameworks and methodologies have been introduced by the researchers for a secure and sustainable software development life cycle.Therefore it is important to assess the usability of the popular security requirements engineering(SRE)approaches.A significant factor in the management and handling of successful security requirements is the assessment of security requirements engineering method performance.This assessment will allow changes to the engineering process of security requirements.The consistency of security requirements depends heavily on the usability of security requirements engineering.Several SRE approaches are available for use and each approach takes into account several factors of usability but does not cover every element of usability.There seems to be no realistic implementation of such models because the concept of usability is not specific.This paper aims at specifying the different taxonomy of usability and design hierarchical usability model.The taxonomy takes into account the common quality assessment parameters that combine variables,attributes,and characteristics identified in different approaches used for security requirements engineering.The multiple-criteria decision-making(MCDM)model used in this paper for usability evaluation is called the fuzzy AHP-TOPSIS model which can conveniently be incorporated into the current approach of software engineering.Five significant usability criteria are identified and used to evaluate the six different alternatives.Such strategies are graded as per their expected values of usability.

Economical Requirements Elicitation Techniques During COVID-19: A Systematic Literature Review

Requirements elicitation is a fundamental phase of software development in which an analyst discovers the needs of different stakeholders and transforms them into requirements.This phase is cost-and time-intensive,and a project may fail if there are excessive costs and schedule overruns.COVID-19 has affected the software industry by reducing interactions between developers and customers.Such a lack of interaction is a key reason for the failure of software projects.Projects can also fail when customers do not know precisely what they want.Furthermore,selecting the unsuitable elicitation technique can also cause project failure.The present study,therefore,aimed to identify which requirements elicitation technique is the most cost-effective for large-scale projects when time to market is a critical issue or when the customer is not available.To that end,we conducted a systematic literature review on requirements elicitation techniques.Most primary studies identified introspection as the best technique,followed by survey and brainstorming.This finding suggests that introspection should be the first choice of elicitation technique,especially when the customer is not available or the project has strict time and cost constraints.Moreover,introspection should also be used as the starting point in the elicitation process of a large-scale project,and all known requirements should be elicited using this technique.

Intelligent Identification and Resolution of Software Requirement Conflicts:Assessment and Evaluation

Considerable research has demonstrated how effective requirements engineering is critical for the success of software projects.Requirements engineering has been established and recognized as one of the most important aspects of software engineering as of late.It is noteworthy to mention that requirement consistency is a critical factor in project success,and conflicts in requirements lead to waste of cost,time,and effort.A considerable number of research studies have shown the risks and problems caused by working with requirements that are in conflict with other requirements.These risks include running overtime or over budget,which may lead to project failure.At the very least,it would result in the extra expended effort.Various studies have also stated that failure in managing requirement conflicts is one of the main reasons for unsuccessful software projects due to high cost and insufficient time.Many prior research studies have proposed manual techniques to detect conflicts,whereas other research recommends automated approaches based on human analysis.Moreover,there are different resolutions for conflicting requirements.Our previous work proposed a scheme for dealing with this problem using a novel intelligent method to detect conflicts and resolve them.A rule-based system was proposed to identify conflicts in requirements,and a genetic algorithm(GA)was used to resolve conflicts.The objective of this work is to assess and evaluate the implementation of the method of minimizing the number of conflicts in the requirements.The methodology implemented comprises two different stages.The first stage,detecting conflicts using a rule-based system,demonstrated a correct result with 100% accuracy.The evaluation of using the GA to resolve and reduce conflicts in the second stage also displayed a good result and achieved the desired goal as well as the main objective of the research.

A Defect Detection Method for the Primary Stage of Software Development

In the early stage of software development,a software requirements specification(SRS)is essential,and whether the requirements are clear and explicit is the key.However,due to various reasons,there may be a large number of misunderstandings.To generate high-quality software requirements specifications,numerous researchers have developed a variety of ways to improve the quality of SRS.In this paper,we propose a questions extraction method based on SRS elements decomposition,which evaluates the quality of SRS in the form of numerical indicators.The proposed method not only evaluates the quality of SRSs but also helps in the detection of defects,especially the description problem and omission defects in SRSs.To verify the effectiveness of the proposed method,we conducted a controlled experiment to compare the ability of checklist-based review(CBR)and the proposed method in the SRS review.The CBR is a classicmethod of reviewing SRS defects.After a lot of practice and improvement for a long time,CBR has excellent review ability in improving the quality of software requirements specifications.The experimental results with 40 graduate studentsmajoring in software engineering confirmed the effectiveness and advantages of the proposed method.However,the shortcomings and deficiencies of the proposed method are also observed through the experiment.Furthermore,the proposed method has been tried out by engineers with practical work experience in software development industry and received good feedback.

Developing architecture of a traveler information system for dynamic equilibrium in traffic networks

Having criticized the current architecture of Advanced Traveler Information Systems （ATISs）, this work discusses a new base of requirements to develop a new paradigm for traffic information systems. It mainly integrates three dimensions within a traffic system： drivers＇ pattern of behavior and preferences, urban traffic desires, and capabilities of traffic information service providers. Based on the above, functional segments from several related backgrounds are brought together to structure a new architecture, called Interactive Traveler Information System （ITIS）. The main interactive feature of this new architecture is a two-way communication track between drivers and the traffic information system provider-in fact, a decision on choosing a road at a particular time for an individual will be made based on the utility of both sides. This new configuration consists of driver-side smartphone application, centric traffic prediction, and decision-maker units, which will shape a new approach of decision-making processes. These all work together to satisfy the designated goal of ITIS, which is preserving the Wardrop equilibrium condition in the traffic network level. Finally, we concentrate on a comparison study, which shows a differentiation between performance of the proposed ITIS and the current ATIS model in a real situation. This has been done with simulations of analogical scenarios. The most noticeable advantage of the proposed architecture is not being limited to a saturation limit, and the positive effect of increasing system penetration in the performance of the newly introduced information system. In conclusion, new research subjects are suggested to be carried out.

Sports video summarization and adaptation for application in mobile communication

Sports video appeals to large audiences due to its high commercial potentials. Automatically extracting useful semantic information and generating highlight summary from sports video to facilitate users' accessing requirements is an important problem, especially in the forthcoming broadband mobile communication and the need for users to access their multimedia information of interest from anywhere at anytime with their most convenient digital equipments. A system to generate highlight summaries oriented for mobile applications is introduced, which includes highlight extraction and video adaptation. In this system, several highlight extraction techniques are provided for field sports video and racket sports video by using multi-modal information. To enhance users' viewing experience and save bandwidth, 3D animation from highlight segment is also generated. As an important procedure to make video analysis results universally applicable, video transcoding techniques are applied to adapt the video for mobile communication environment and user preference. Experimental results are encouraging and show the advantage and feasibility of the system for multimedia content personalization, enhancement and adaptation to meet different user preference and network/device requirements.

A CONTRIBUTION TO THE SCIENTIFIC FOUNDATIONS OF SYSTEMS ENGINEERING： SOLUTION SPACES AND REQUIREMENTS

Some researchers have suggested that scientific foundations expressed in a mathematical form are needed to thrust the success of systems engineering as a discipline on its own merit. In order to contribute the development of such systems science, this paper investigates from a foundational standpoint the relationships between stakeholder needs, system requirements, and sets of systems. Various theorems and corollaries are proposed and mathematically proven. The theoretical elements are presented as a foundation for the development of a science for requirements engineering. The proposed foundations are finally tested to mathematically describe, in a rigorous and precise manner, qualities of good requirements, which are otherwise traditionally defined using vague narrative. By showcasing practical examples of the theoretical aspects, the paper is intended to serve as a bridge between practitioners and theorists.

Automatic Semantic Analysis of Software Requirements Through Machine Learning and Ontology Approach

Nowadays,software requirements are still mainly analyzed manually,which has many drawbacks(such as a large amount of labor consumption,inefficiency,and even inaccuracy of the results).The problems are even worse in domain analysis scenarios because a large number of requirements from many users need to be analyzed.In this sense,automatic analysis of software requirements can bring benefits to software companies.For this purpose,we proposed an approach to automatically analyze software requirement specifications(SRSs) and extract the semantic information.In this approach,a machine learning and ontology based semantic role labeling(SRL) method was used.First of all,some common verbs were calculated from SRS documents in the E-commerce domain,and then semantic frames were designed for those verbs.Based on the frames,sentences from SRSs were selected and labeled manually,and the labeled sentences were used as training examples in the machine learning stage.Besides the training examples labeled with semantic roles,external ontology knowledge was used to relieve the data sparsity problem and obtain reliable results.Based on the Sem Cor and Word Net corpus,the senses of nouns and verbs were identified in a sequential manner through the K-nearest neighbor approach.Then the senses of the verbs were used to identify the frame types.After that,we trained the SRL labeling classifier with the maximum entropy method,in which we added some new features based on word sense,such as the hypernyms and hyponyms of the word senses in the ontology.Experimental results show that this new approach for automatic functional requirements analysis is effective.

SYSTEMS ENGINEERING RESEARCH

In this paper, we propose selected research topics that are believed central to progress and growth in the application of systems engineering （SE）. As a professional activity, and as an intellectual activity, systems engineering has strong links to such associated disciplines as decision analysis, operation research, project management, quality management, and systems design. When focussing on systems engineering research, we should distinguish between subjects that are of systems engineering essence and others that more closely correspond to those that are more relevant for related disciplines.

Automated Analysis of the SCR-StyleRequirements Specifications

The SCR (Software Cost Reduction) requirements method is aneffective method for specifying software system requirements. This paper presents aformal model analyzing SCR-style requirements. The analysis model mainly appliesstate translation rules, semantic computing rules and attributes to define formal se-mantics of a tabular notation in the SCR requirements method, and may be used toanalyze requirements specifications to be specified by the SCR requirements method.Using a simple example, this paper introduces how to analyze consistency and com-pleteness of requirements specifications.