A Fuzzy Case-Based Reasoning Model for Software Requirements Specifications Quality Assessment

Different software Quality Assurance (SQA) audit techniques are applied in the literature to determine whether the required standards and procedures within the Software Requirements Specification (SRS) phase are adhered to. The inspection of the Software Requirements Specification (iSRS) system is an analytical assurance tool which is proposed to strengthen the ability to scrutinize how to optimally create high-quality SRSs. The iSRS utilizes a Case-Based Reasoning (CBR) model in carrying out the SRS quality analysis based on the experience of the previously analyzed cases. This paper presents the contribution of integrating fuzzy Logic technique in the CBR steps to form a Fuzzy Case-Based Reasoning (FCBR) model for improving the reasoning and accuracy of the iSRS system. Additionally, for efficient cases retrieval in the CBR, relevant cases selection and nearest cases selection heuristic search algorithms are used in the system. Basically, the input to the relevant cases algorithm is the available cases in the system case base and the output is the relevant cases. The input to the nearest cases algorithm is the relevant cases and the output is the nearest cases. The fuzzy Logic technique works on the selected nearest cases and it utilizes similarity measurement methods to classify the cases into no-match, partial-match and complete-match cases. The features matching results assist the revised step of the CBR to generate a new solution. The implementation of the new FCBR model shows that converting numerical representation to qualitative terms simplifies the matching process and improves the decision-making of the system

A Softcomputing Knowledge Areas Model

Recently, ten knowledge areas (KAs) of project management have been published by the PMBOK® Guide. They comprise specific skills and experiences to ensure accomplishing project goals, and include management of: integration, scope, cost, time, quality, communications, procurement, risk, human resources and stakeholders. This research paper focuses on the ten required KAs for a project manager or a project to be successful. It aims at applying the Softcomputing modeling techniques to describe the relations between the 47 processes and the KAs. Such a model will enable users to predict the overall competencies of the project management. Thus, it provides an assessment tool to envisage, visualize and indicate the overall performance and competency of a project. The proposed Softcomputing Knowledge Areas Model (SKAM) is a two-stage model. The first stage involves ten models. Each model describes relations between a specific KA and its related processes. The outputs of these ten models will feed into the second stage that will represent the relationship between all the ten KAs and the overall predicted competencies of a project. A combination of Subtractive Clustering and Neurofuzzy modeling techniques are used. Three measures are used to validate the adequacy of the models: the mean average percentage errors, the correlation coefficient and the maximum percentage errors. The highest achieved values for these measures are 0.5751, 0.9999 and 4.7283, respectively. However, although the preliminary findings of the proposed SKAM model are promising, more testing is still required before declaring the adequacy of applying the Softcomputing modeling approach in the project management field

Audit of Financial Information Systems: a risk-based approach and fuzzy logic

Nowadays, business is exposed to information system risks and threats. This justifies the growing inquiry, of investors and shareholders, on their business security. Information systems auditing has strong tools and techniques, which can assist organizations in minimizing these risks and threats. But the fast-changing and growth of information systems makes the audit missions more complex and surrounded by uncertainty, related to audit quality parameters like experience, knowledge, and others. In line with this, the auditors may be faced with discrepancies during auditing, with each anomaly typically triggering a binary evaluation of significance. In this paper, we develop a fuzzy expert system framework, which evaluates the level of significance in the audit by allowing a discrepancy to have a level between 0 and 1. Such a framework enables the auditor to have increased accuracy and more flexibility in evaluating the appropriate level of significance, and provides a better understanding of the scope of subsequent audits and examinations. As results, we show that a fuzzy expert system has the potential to assist the auditor in the process of including qualitative information in the frivolous level and identifying the anomalies that may be most worthy of investigation. The fuzzy expert system standardizes the process of auditing by providing a formal model structure. This may facilitate reporting within the audit organization and improve the coherence of the audit process between auditors, missions over time.   JEL Classification: C67, M15, M42 Paper type: Empirical researchNowadays, business is exposed to information system risks and threats. This justifies the growing inquiry, of investors and shareholders, on their business security. Information systems auditing has strong tools and techniques, which can assist organizations in minimizing these risks and threats. But the fast-changing and growth of information systems makes the audit missions more complex and surrounded by uncertainty, related to audit quality parameters like experience, knowledge, and others. In line with this, the auditors may be faced with discrepancies during auditing, with each anomaly typically triggering a binary evaluation of significance. In this paper, we develop a fuzzy expert system framework, which evaluates the level of significance in the audit by allowing a discrepancy to have a level between 0 and 1. Such a framework enables the auditor to have increased accuracy and more flexibility in evaluating the appropriate level of significance, and provides a better understanding of the scope of subsequent audits and examinations. As results, we show that a fuzzy expert system has the potential to assist the auditor in the process of including qualitative information in the frivolous level and identifying the anomalies that may be most worthy of investigation. The fuzzy expert system standardizes the process of auditing by providing a formal model structure. This may facilitate reporting within the audit organization and improve the coherence of the audit process between auditors, missions over time.   JEL Classification: C67, M15, M42 Paper type: Empirical researc

A semantic rule based digital fraud detection

Digital fraud has immensely affected ordinary consumers and the finance industry. Our dependence on internet banking has made digital fraud a substantial problem. Financial institutions across the globe are trying to improve their digital fraud detection and deterrence capabilities. Fraud detection is a reactive process, and it usually incurs a cost to save the system from an ongoing malicious activity. Fraud deterrence is the capability of a system to withstand any fraudulent attempts. Fraud deterrence is a challenging task and researchers across the globe are proposing new solutions to improve deterrence capabilities. In this work, we focus on the very important problem of fraud deterrence. Our proposed work uses an Intimation Rule Based (IRB) alert generation algorithm. These IRB alerts are classified based on severity levels. Our proposed solution uses a richer domain knowledge base and rule-based reasoning. In this work, we propose an ontology-based financial fraud detection and deterrence model

Decision Support Systems

Decision support systems (DSS) have evolved over the past four decades from theoretical concepts into real world computerized applications. DSS architecture contains three key components: knowledge base, computerized model, and user interface. DSS simulate cognitive decision-making functions of humans based on artificial intelligence methodologies (including expert systems, data mining, machine learning, connectionism, logistical reasoning, etc.) in order to perform decision support functions. The applications of DSS cover many domains, ranging from aviation monitoring, transportation safety, clinical diagnosis, weather forecast, business management to internet search strategy. By combining knowledge bases with inference rules, DSS are able to provide suggestions to end users to improve decisions and outcomes. This book is written as a textbook so that it can be used in formal courses examining decision support systems. It may be used by both undergraduate and graduate students from diverse computer-related fields. It will also be of value to established professionals as a text for self-study or for reference

Efficient Decision Support Systems

This series is directed to diverse managerial professionals who are leading the transformation of individual domains by using expert information and domain knowledge to drive decision support systems (DSSs). The series offers a broad range of subjects addressed in specific areas such as health care, business management, banking, agriculture, environmental improvement, natural resource and spatial management, aviation administration, and hybrid applications of information technology aimed to interdisciplinary issues. This book series is composed of three volumes: Volume 1 consists of general concepts and methodology of DSSs; Volume 2 consists of applications of DSSs in the biomedical domain; Volume 3 consists of hybrid applications of DSSs in multidisciplinary domains. The book is shaped upon decision support strategies in the new infrastructure that assists the readers in full use of the creative technology to manipulate input data and to transform information into useful decisions for decision makers

Fuzzy Logic Based Software Reliability Quantification Framework: Early Stage Perspective (FLSRQF)

AbstractToday, the influence of information technology has been spreading exponentially, from high level research going on in top labs of the world to the home appliances. Such a huge demand is compelling developers to develop more software to meet the user expectations. As a result reliability has come up as a critical quality factor that cannot be compromised. Therefore, researchers are continuously making efforts to meet this challenge. With this spirit, authors of the paper have proposed a highly structured framework that guides the process of quantifying software reliability, before the coding of the software start. Before presenting the framework, to realize its need and significance, the paper has presented the state-of-the-art on software reliability quantification. The strength of fuzzy set theory has been utilized to prevail over the limitation of subjectivity of requirements stage measures. Salient features of the framework are also highlighted at the end of the paper

Validating digital forensic evidence

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.This dissertation focuses on the forensic validation of computer evidence. It is a burgeoning field, by necessity, and there have been significant advances in the detection and gathering of evidence related to electronic crimes. What makes the computer forensics field similar to other forensic fields is that considerable emphasis is placed on the validity of the digital evidence. It is not just the methods used to collect the evidence that is a concern. What is also a problem is that perpetrators of digital crimes may be engaged in what is called anti-forensics. Digital forensic evidence techniques are deliberately thwarted and corrupted by those under investigation. In traditional forensics the link between evidence and perpetrator's actions is often straightforward: a fingerprint on an object indicates that someone has touched the object. Anti-forensic activity would be the equivalent of having the ability to change the nature of the fingerprint before, or during the investigation, thus making the forensic evidence collected invalid or less reliable. This thesis reviews the existing security models and digital forensics, paying particular attention to anti-forensic activity that affects the validity of data collected in the form of digital evidence. This thesis will build on the current models in this field and suggest a tentative first step model to manage and detect possibility of anti-forensic activity. The model is concerned with stopping anti-forensic activity, and thus is not a forensic model in the normal sense, it is what will be called a “meta-forensic” model. A meta-forensic approach is an approach intended to stop attempts to invalidate digital forensic evidence. This thesis proposes a formal procedure and guides forensic examiners to look at evidence in a meta-forensic way