Abdelrahman Osman Elfaki

Modeling Variability in Software Product Line Using First Order Logic

Software Product Line (SPL) is a new methodology that develops software products by configuring a software product from artifacts repository. Variability is one of the important issues in designing SPL. It reflects the diversity and commonality of the artifacts in a product line. The success of SPL is basically dependent on model’s variability. SPL contains three main issues: variability modeling, configuration of new software and the analysis of SPL. Validation is a vital operation among all these issues. In the literature, many methods of modeling variability are proposed but none of them focuses on the validation. In this paper, an intelligent method for validating SPL is introduced. The proposed method is based on two layers. The higher layer is a graphical representation (satisfying visualization condition), and the lower layer is logical representation of the variability using first order logic (FOL). The way that the proposed method can be used to model variability, and support the analysis and validation of SPL is described. Later a new operation in SPL validation issue is presented. Finally, the implementation of the two basic operations in the analysis of SPL is illustrated

A rule-based approach to detect and prevent inconsistency in the domain-engineering process

A medium-sized domain-engineering process can contain thousands of features that all have constraint dependency rules between them. Therefore, the validation of the content of domain-engineering process is vital to produce high-quality software products. However, it is not feasible to do this manually. This paper aims to improve the quality of the software products generated by the domain-engineering process by ensuring the validity of the results of that process. We propose rules for two operations: inconsistency detection and inconsistency prevention. We introduce first-order logic FOL rules to detect three types of inconsistency and prevent the direct inconsistency in the domain-engineering process. Developing FOL rules to detect and prevent inconsistency in the domain-engineering process directly without the need to the configuration process is our main contribution. We performed some experiments to test the scalability and applicability of our approach on domain-engineered software product lines containing 1000 assets to 20000 assets. The results show that our approach is scalable and could be utilized to improve the domain-engineering process.

Modeling Value Evaluation of Semantics Aided Secondary Language Acquisition as Model Driven Knowledge Management

Many theories and solutions have been proposed for the improvement of the learning efficiency of secondary language(L2) learning. However neither an unified view on the functionality of semantics in aiding learning nor an objective measure of the efficiency improvement at theoretical level has been presented in existing literature. This situation hinders the efficient adoption of the semantics aided learning and the explicit planning of a semantics aided learning process. We aim to fill these gaps by adopting an evolutionary strategy towards approaching a holistic solution. Firstly we model the general learning process from cognitive linguistic perspective at the memory level. Then we justify the functionality of semantics aided approach according to specific conditions. Thereafter we propose the quantity measure for the improvement of learning efficiency in terms of reuse level for semantics aided secondary language learning from the perspective of value based analysis.

Investigating Inconsistency Detection as a Validation Operation in Software Product Line

Software product Line (SPL) is an emerging methodology for developing software products. A successful software product is highly dependent on the validity of a SPL. Therefore, validation is a significant process within SPL. In this paper, inconsistency detection is investigated as operation for validating SPL. Intelligent rules are formulated detecting inconsistency based on deducing the results from predefined cases. First, variability is modeled using First Order Logic (FOL) predicates as a prerequisite for inconsistency detection. Later, inconsistency is categorized in three groups. For each group a general form is formulated that can coffer all possible cases. Finally, an intelligent rule (based on FOL) is illustrated for implementing each possibility. As results, all cases of inconsistency in the domain-engineering process are defined.

Optimizing the Multilayer Feed-Forward Artificial Neural Networks Architecture and Training Parameters using Genetic Algorithm

ABSTRACT Determination of optimum feed forward artificial neural network (ANN) design and training parameters is an extremely important mission. It is a challenging and daunting task to find an ANN design, which is effective and accurate. This paper presents a new methodology for the optimization of ANN parameters as it introduces a process of training ANN which is effective and less human-dependent. The derived ANN achieves satisfactory performance and solves the time-consuming task of training process. A Genetic Algorithm (GA) has been used to optimize training algorithms, network architecture (i.e. number of hidden layer and neurons per layer), activation functions, initial weight, learning rate, momentum rate, and number of iterations. The preliminary result of the proposed approach has indicated that the new methodology can optimize designing and training parameters precisely, and resulting in ANN where satisfactory performance.

Characterizing E-service Economics based on E-contract and driven by E-value

E-Service related technologies have a profound and revolutionary impact on many traditional areas. We try to outline its influence on economics which we call E-service Economics at fundamental level. We model the new characteristics of this trend along two themes: value driven decision making which covers both reuse based value added analysis and service value broker (SVB) pattern which embodies the integration of information technology and business wisdoms, and E-contract based realization which equally supports collaborative and competitive business activities. An initial case based on E-contract to show the attained flexibility, dynamic and consistency of automation is demonstrated.

Towards detecting redundancy in domain engineering process using first order logic rules

Software product line SPL is an emerging methodology for developing software products. SPL consists of two processes: domain-engineering and application-engineering. Successful software product is highly dependent on the validity of a domain engineering process. Therefore, validation is a significant process within the domain-engineering. Anomalies such as dead feature, redundancy, and wrong-cardinality are well-known problems in SPL. In the literature, redundancy did not take the signs of attentions as a dead feature and wrong-cardinality. The maturity of the SPL can be enhanced by detecting and removing the redundancy from the domain engineering. This paper proposes first order logic FOL rules for detecting the redundancy in domain-engineering process. Detecting redundancy in the domain engineering direct is our contribution. Our methodology comprised of three steps: 1 variability is modelled in the form of predicates as a prerequisite; 2 for each type of the redundancy, a general form is formulated to swathe all possible cases; 3 FOL rules are illustrated to implement each possibility based on deducing the results from predefined cases. As a result, all forms of redundancies in the domain-engineering process are amorphous. Finally, experiments are conducted to attest the scalability of our method.

Defining Variability in DSS: An Intelligent Method for Knowledge Representation and Validation

Managing knowledge is both a challenging and complex task. There is a number techniques for making decisions in todays knowledge-based economies. Decision support systems (DSS) have been developed to aid the decision-making process to find solutions for multiple problems. One aspect that hinders successful decision making is the issue of variability definition. The aim of this paper is to define variability by proposing an intelligent method. Knowledge representation is based in two layer:1) the upper layer i.e. graphical representation and 2) the lower layer i.e. a mathematical algorithm. We present a method that defines and provides auto-support for five operations in knowledge validation particularly dependency constraint rules, propagation, delete-cascade, logical inconsistency and dead choice detection.

Towards Measuring of E-Learning Usability through User Interface

Education and learning is one of the worlds largest markets for internet applications. Learning and training at work is probably the fastest growing sector. Before measuring the usability of these e-learning applications, users have to face unknown risks of failure of applications. This paper represents five metrics to measure the usability of E-Learning systems through user interface. These metrics are: Time of user feedback, average of using help methods, average of using undo, average time spent in any page, and average of using e-learning systems search engine. We focused to measure the using of e-systems rather than the content of the systems. These metrics cover the three parts of usability (ease of use, ease to learning, and task matching). In addition, these metrics satisfy the definition of usability for both human computer interaction and software engineering.

Automated Verification of Variability Model Using First-Order Logic

Verification of the domain engineering is motivated by two reasons: (1) the huge size of the software assets and (2) the possibility of changes in business rules or in stakeholders’ needs which affect the structure of the domain engineering. To solve this problem of verifying software product line (SPL), we propose set of rules to verify four operations: inconsistency detection, inconsistency prevention, dead feature detection, and false-optional feature detection. Scalability is a key factor in measuring the applicability of the methods dealing with the domain engineering. We generated experiments for testing the scalability of our approach. Our experiments results show that our approach is scalable.