Volume 3, Issue 2

Managing embedded system complexity and scalability is one of the most important problems in software development. To better address this problem, it is very recommended to have an abstraction level high enough to model such systems. Architectural description languages (ADLs) intend to model complex systems and manage their structure at a high abstraction level. Traditional ADLs do not normally provide appropriate formalisms to separate any kind of crosscutting concerns. This frequently results in poor descriptions of the software architectures and a tedious adaptation to constantly changing user requirements and specifications. AOSD (Aspect Oriented Software Development) deals with these problems by considering crosscutting concerns in software development. The effectiveness of considering an aspect-oriented architectural design appears when aspect concepts are taken into account early in the software’s life-cycle. In this paper, we propose a new aspect language called AO4AADL that adequately manipulates aspect oriented concepts at architecture level in order to master complexity and ensure scalability. The abstract nature of our proposed language allows the generation of aspect code for several programming languages and platforms.

Clustering, an extremely important technique in Data Mining is an automatic learning technique aimed at grouping a set of objects into subsets or clusters. The goal is to create clusters that are coherent internally, but substantially different from each other. Text Document Clustering refers to the clustering of related text documents into groups based upon their content. It is a fundamental operation used in unsupervised document organization, text data mining, automatic topic extraction, and information retrieval. Fast and high-quality document clustering algorithms play an important role in effectively navigating, summarizing, and organizing information. The documents to be clustered can be web news articles, abstracts of research papers etc. This paper proposes two techniques for efficient document clustering involving the application of soft computing approach as an intelligent hybrid approach PSO algorithm. The proposed approach involves partitioning Fuzzy C-Means algorithm and K-Means algorithm each hybridized with Particle Swarm Optimization (PSO). The performance of these hybrid algorithms has been evaluated against traditional partitioning techniques (K-Means and Fuzzy C Means).

Finite state machines have been used in many contexts, e.g., test generation, system modeling, language processing. In these applications, the size of the machine has a great impact on the complexity and quality of the results. Thus, it is desirable to obtain a machine without redundant states, i.e., to minimize the machine. However, the minimization of partially specified finite state machines is a known NP-complete problem, and many heuristics to obtain feasible solutions have been proposed. In this paper, we propose an approach to minimize partial finite state machines based on the selection of compatible states using maximum cliques on the distinction graph. We experimented with our method comparing it with previous methods and the data suggest that our approach is more efficient, generating good results in less time.