Abstract Automating the quality assessment of Software Requirement Specification poses major challenges related to the need for advanced algorithms to extract the SRS quality features, interpret the context of the features, formulate accurate assessment metrics, and document the shortcomings as well as possible improvements. In the existing methods, such as Reconstructed Automated Requirement Measurement, and Rendex, some major processes are still handled offline by humans (semi‐automated) or encompass automating the measurement of a few quality attributes due to the mentioned challenges. This paper addressed this gap and proposed an Automated Quality Assessment of SRS (AQA‐SRS) framework to assess the SRS documents by automatically extracting features related to 11 quality attributes through a deep analysis of the SRS textual content. Also, it constructs a flexible platform that is able to minimize the human expert’s role in the SRS assessment. The AQA‐SRS framework integrates Natural Language Processing, K‐means, Multi‐agent, and Case‐Based Reasoning. The AQA‐SRS framework is evaluated by processing two standard SRS datasets and comparing the results with state‐of‐the‐art methods and analysis by software engineering experts. The results show that the AQA‐SRS framework effectively assesses the tested SRS documents and achieves a 78% total agreement with the tested methods and software engineering experts.

Abstract Requirement's elicitation is the process of gathering requirements from users, customers, and stakeholders using traditional or collaborative elicitation techniques. Software requirement gathering is a challenging task particularly in a Global Software Development (GSD) paradigm due to geographical distance, limited face to face meetings, time zone differences, and language and cultural barriers. As more companies begin to adopt GSD in order to save costs, the success factors need to be identified and evaluated in order to ensure a successful elicitation process. This paper offers an in‐depth analysis on success factors for requirement elicitation within GSD environment. First, all possible success factors are identified from the literature via a Systematic Literature Review (SLR). Next, these factors are evaluated by software industries via a questionnaire survey across different types and levels of experts, size of organizations, and from the client–vendor perspective. The relationships between the success factors and the survey results were evaluated using the Spearman's correlation coefficient. The results produced a 0.835 Spearman's correlation coefficient at significance level ρ = 0.000, which showed a strong positive correlation between the outcome of SLR and survey with no significant difference.

Currently, online organizational resources and assets are potential targets of several types of attack, the most common being flooding attacks. We consider the Distributed Denial of Service (DDoS) as the most dangerous type of flooding attack that could target those resources. The DDoS attack consumes network available resources such as bandwidth, processing power, and memory, thereby limiting or withholding accessibility to users. The Flash Crowd (FC) is quite similar to the DDoS attack whereby many legitimate users concurrently access a particular service, the number of which results in the denial of service. Researchers have proposed many different models to eliminate the risk of DDoS attacks, but only few efforts have been made to differentiate it from FC flooding as FC flooding also causes the denial of service and usually misleads the detection of the DDoS attacks. In this paper, an adaptive agent-based model, known as an Adaptive Protection of Flooding Attacks (APFA) model, is proposed to protect the Network Application Layer (NAL) against DDoS flooding attacks and FC flooding traffics. The APFA model, with the aid of an adaptive analyst agent, distinguishes between DDoS and FC abnormal traffics. It then separates DDoS botnet from Demons and Zombies to apply suitable attack handling methodology. There are three parameters on which the agent relies, normal traffic intensity, traffic attack behavior, and IP address history log, to decide on the operation of two traffic filters. We test and evaluate the APFA model via a simulation system using CIDDS as a standard dataset. The model successfully adapts to the simulated attack scenarios’ changes and determines 303,024 request conditions for the tested 135,583 IP addresses. It achieves an accuracy of 0.9964, a precision of 0.9962, and a sensitivity of 0.9996, and outperforms three tested similar models. In addition, the APFA model contributes to identifying and handling the actual trigger of DDoS attack and differentiates it from FC flooding, which is rarely implemented in one model.

In Multi-agent Systems (MAS), agents perform a variety of actions to autonomously complete a number of tasks. In this paper, we describe a mechanism to measure a task's deliberation intensity and apply the mechanism in the Layered Adjustable Autonomy (LAA) model. Basically, the number of actions that the agents need to do to complete a particular task determines the task's deliberation intensity. Consequently, each of the actions deliberation intensity determines its complexity-level. Actions complexity levels are categorized as high-level if the action is deliberative, intermediate-level if the action pseudo-deliberative and low-level if the action is non-deliberative. Ultimately, the deliberation intensity measure of a task and its actions identify different aspects of the agents' and the actions' parameters including the deliberation length and the autonomy configuration of the LAA model.

Since the emergence of ontologies in the field of computer science a number of approaches for developing ontologies have been proposed. Different approaches focus on distinct aspects of the ontology development process and make use of various techniques for developing ontologies. It is an accepted fact that the ontology development process relies on human feedback and decisions during various phases of ontology development. However, most of the existing approaches neglect this aspect of ontology development. In order to bridge this gap, this paper proposes an ontology development approach using the concept mapping technique, which is human centered in nature. This approach eases the overall ontology development process. It allows developing ontologies which involve human feedback during the evolution of the ontology design.

Collaborative Filtering (CF) is the most successful technology for recommender systems. The technology does not rely on actual content of the items, but instead requires users to indicate preferences, most commonly in the form of ratings. While CF is known for its traditional problems such as cold-start, sparsity and modest accuracy, a trust-based CF has been previously proposed to solve such issues by focusing on trust values among the users. Nonetheless, all existing trust-based approaches use trust as a factor independent from scope, whether explicit or implicit. We argue that trustworthiness should not be the same across all conditions; hence the trust values should change to suit certain scope or focused area. To validate the proposed temporal-focused trustworthiness in this paper, we propose a novel pheromone-based approach to calculate trustworthiness by focusing on time factor. Implementation of the proposed approach is hoped to reduce cold-start and sparsity as well as improve accuracy of the recommendation results.

With the growth of Web 2.0, online communication and social networks are emerging. This alternation helps users to share their information and collaborate with each other easily. In addition, these internet services help establish new connections between persons or reinforce existing ones. However, they can also lead to misbehaviors or cyber criminal acts for example, cyberbullying. At the same time, it can make children and adolescents to use the technologies for the intention of harming another person. Due to the negative effect of cyberbullying, some techniques and methods are proposed to overcome this problem. This paper illustrates a survey covering some methods and challenges in cyberbullying. Next, we offer suggestions for continued research in this area.

Umrah as a pilgrimage to Mecca is obligatory in Islam madhab. Pilgrims can gain their knowledge about the requirements of Umrah using books, expert and existent question answering systems but still they suffer from the lack of complexity and natural language patterns. This paper proposes the semantic based question answering system to able pilgrims to compose any question about Umrah in natural language format. The proposed system used ontology to represent the knowledge about ritual of Umrah and Pilgrims. The question complexity for question in natural language is observed since it needs to be map with the contents in the ontology. An Umrah Knowledge module in this system covers all the rules and fact in the ontology format and the Educational modules is responsible for question answering interaction.

Currently, research in normative multi-agent systems focus on how a visitor or new agent detects and updates its host norms autonomously without being explicitly given by the host system. In this paper, we present our proposed algorithm to detect the obligation and prohibition norms which we called the Obligation and Prohibition Norms Mining algorithm (OPNM). The algorithm exploits the resources of the host system, implements data formatting, filtering, and extracting the exceptional events, i.e. those that entail rewards and penalties of the obligation and prohibition norms and identifies the ensuing normative protocol. In this work, we assume that an agent is aware of its environment and is able to reason about its surrounding events. We then demonstrate the operation of the algorithm by applying it on a typical scenario and analyzing the results.

Nowadays, many users use web search engines to find and gather information. User faces an increasing amount of various web pages information sources. The issue of correlating, integrating and presenting related information to users becomes important. When a user uses a search engine such as Yahoo and Google to seek a specific information, the results are not only information about the availability of the desired information, but also information about other pages on which the desired information is mentioned. Extracting information from the web pages also becomes very important because the massive and increasing amount of diverse web pages information sources in the Internet that are available to users, and the variety of web pages making the process of information extraction from web a challenging problem. This paper proposes an approach for extracting information from web tables based on standard classifications. The proposed approach consists of four main phases, namely: (i) pre-processing, (ii) extraction, (iii) classification, and (iv) simplification. The proposed approach is evaluated by conducting experiments on a number of web pages from the Nokia products domain, as to the best of our knowledge this is the only product that has complete and complex standard classifiers.