International Journal of Software Engineering and Knowledge Engineering

A technique for creating analysis models of a web application from a business model is proposed for easy and effective development. Moreover, a technique for generating test cases from the sequence diagrams for a web application is proposed. The use case diagram and web page list are generated from the business model which is depicted using the notations of the UML (Unified Modeling Language) activity diagram. The page diagram and logical/physical database models are created based on the web page list and extracted data fields. Test cases for the web application are generated from call messages (including self-call messages) of the UML sequence diagram. The effectiveness of these techniques is shown using a practical case study which is a development project of a web application for RMS (Research material Management System).

In a totally formalized, iterative and incremental software process, each iteration usually implies identifying new requirements, adding them to the current model of the system, checking again the consistency and, in many cases, modifying the model to satisfy all the functional requirements. In this context, the ARIFS (Approximate Retrieval of Incomplete and Formal Specifications) methodology provides a suitable reuse environment (1) to classify, retrieve and adapt formal and incomplete requirements specifications and (2) to reuse the formal verification results linked to them. In this paper, we focus on the first goal describing the classification and retrieval tasks, which are based on functional similarities according to structural and semantic closeness. To this effect, we define four partial ordering relations among reusable components and several measures to quantify functional differences among them. By using these measures, we are able to offer an approximate and efficient retrieval, without formal proofs, and to predict adaptation efforts to satisfy the required functionality.

Data mining techniques provide people with new power to research and manipulate the existing large volume of data. A data mining process discovers interesting information from the hidden data that can either be used for future prediction and/or intelligently summarising the details of the data. There are many achievements of applying data mining techniques to various areas such as marketing, medical, and financial, although few of them can be currently seen in software engineering domain. In this paper, a proposed data mining application in software engineering domain is explained and experimented. The empirical results demonstrate the capability of data mining techniques in software engineering domain and the potential benefits in applying data mining to this area.

Evolutionary computation has been successfully applied in a variety of problem domains and applications. In this paper we discuss the use of a specific form of evolutionary computation known as Cultural Algorithms to improve the efficiency of the subsumption algorithm in semantic networks. We identify two complementary methods of using Cultural Algorithms to solve the problem of re-engineering large-scale dynamic semantic networks in order to optimize the efficiency of subsumption: top-down and bottom-up.

The top-down re-engineering approach improves subsumption efficiency by reducing the number of attributes that need to be compared for every node without impacting the results. We demonstrate that a Cultural Algorithm approach can be used to identify these defining attributes that are most significant for node retrieval. These results are then utilized within an existing vehicle assembly process planning application that utilizes a semantic network based knowledge base to improve the performance and reduce complexity of the network. It is shown that the results obtained by Cultural Algorithms are at least as good, and in most cases better, than those obtained by the human developers. The advantage of Cultural Algorithms is especially pronounced for those classes in the network that are more complex.

The goal of bottom-up approach is to classify the input concepts into new clusters that are most efficient for subsumption and classification. While the resultant subsumption efficiency for the bottom-up approach exceeds that for the top-down approach, it does so by removing structural relationships that made the network understandable to human observers. Like a Rete network in expert systems, it is a compilation of only those relationships that impact subsumption. A direct comparison of the two approaches shows that bottom-up semantic network re-engineering creates a semantic network that is approximately 5 times more efficient than the top-down approach in terms of the cost of subsumption. In conclusion, we will discuss these results and show that some knowledge that is useful to the system users is lost during the bottom-up re-engineering process and that the best approach for re-engineering a semantic network requires a combination of both of these approaches.

In software architecture design, we explore design alternatives and make decisions about adoption or rejection of a design from a web of complex and often uncertain information. Different architectural design decisions may lead to systems that satisfy the same set of functional requirements but differ in certain quality attributes. In this paper, we propose a Bayesian Network based approach to rational architectural design. Our Bayesian Network helps software architects record and make design decisions. We can perform both qualitative and quantitative analysis over the Bayesian Network to understand how the design decisions influence system quality attributes, and to reason about rational design decisions. We use the KWIC (Key Word In Context) example to illustrate the principles of our approach.

Security modeling for agents has been one of the most challenging issues in developing practical mobile agent software systems. In the past, researchers have developed mobile agent systems with emphasis either on protecting mobile agents from malicious hosts or protecting hosts from malicious agents. In this paper, we propose a security based mobile agent system architecture that provides a general solution to protecting both mobile agents and agent hosts in terms of agent communication and agent migration. We present a facilitator agent model that serves as a middleware for secure agent communication and agent migration. The facilitator agent model, as well as the mobile agent model, is based on agent-oriented G-nets — a high level Petri net formalism. To illustrate our formal modeling technique for mobile agent systems, we provide an example of agent migration to show how a design error can be detected.