Metric suite to Evaluate Reusability of Software Product Line

Metrics have long been used to measure and evaluate  software products and processes. Software product line architecture is a field in which few metrics have been applied, a surprising fact given the important role of software product line architecture in software product line development. Recently, Some metrics have been developed to assess software product line architecture. These metrics are useful but have not been widely used in industry. In this paper, some new metrics are provided to assess reusability of Software product line architecture. Our metrics are evaluated in action.DOI:http://dx.doi.org/10.11591/ijece.v4i2.513

Extractability Effectiveness on Software Product Line

A software product line consists of a family of software systems. Most of quality attributes are defined for single systems. When we are facing a family of products instead of a single system, some aspects of architecture evaluation, such as cost, time, and reusability of available assets, become more highlighted. In this paper a new quality attribute for software product line, which we called it extractability, is introduced. Also extractability measuring method and relationship between extractability with some quality attributes is presented. At the end, Extractability Effectiveness on Software Product Line is evaluated in practice.DOI:http://dx.doi.org/10.11591/ijece.v4i1.410

A Systematic Method for Identification of Anti-patterns in Service Oriented System Development

Service-Oriented Architecture is one of the popular software architecture's patterns used for developing lots of modern systems. However, it has been involved in many failures. Anti-patterns are solutions which have good view, but in fact they are wrong solutions that cause failure of systems. There are a lot of anti-patterns for SOA and new anti-patterns are revealed every day. Anti-patterns have their own reasons for being formed and also they are appeared in special area of the problem. As human's mind is restricted and it can process a limited number of states (piece of information) therefore identification of anti-patterns will be difficult for architects. In this paper, we propose a systematic method based on repository of anti-patterns along with a check list to identify anti-patterns of SOA. This method will assist architects to easily detect and avoid anti-patterns in development process and so escape from risks which related to anti-patterns. Furthermore, in this paper, we present a repository of forty five general anti-patterns in SOA. Reviewing these anti-patterns will help developers to work with clear understanding of patterns in phases of software development and so avoid from many potential problems. Also, our method is evaluated in action.DOI:http://dx.doi.org/10.11591/ijece.v4i1.409

Proportionate Adaptive Graph Signal Recovery

This paper generalizes the proportionate-type adaptive algorithm to the graph signal processing and proposes two proportionate-type adaptive graph signal recovery algorithms. The gain matrix of the proportionate algorithm leads to faster convergence than least mean squares (LMS) algorithm. In this paper, the gain matrix is obtained in a closed-form by minimizing the gradient of the mean-square deviation (GMSD). The first algorithm is the Proportionate-type Graph LMS (Pt-GLMS) algorithm which simply uses a gain matrix in the recursion process of the LMS algorithm and accelerates the convergence of the Pt-GLMS algorithm compared to the LMS algorithm. The second algorithm is the Proportionate-type Graph Extended LMS (Pt-GELMS) algorithm, which uses the previous signal vectors alongside the signal of the current iteration. The Pt-GELMS algorithm utilizes two gain matrices to control the effect of the signal of the previous iterations. The stability analyses of the algorithms are also provided. Simulation results demonstrate the efficacy of the two proposed proportionate-type LMS algorithms

Robust Adaptive Generalized Correntropy-based Smoothed Graph Signal Recovery with a Kernel Width Learning

This paper proposes a robust adaptive algorithm for smooth graph signal recovery which is based on generalized correntropy. A proper cost function is defined for this purpose. The proposed algorithm is derived and a kernel width learning-based version of the algorithm is suggested which the simulation results show the superiority of it to the fixed correntropy kernel version of the algorithm. Moreover, some theoretical analysis of the proposed algorithm are provided. In this regard, firstly, the convexity analysis of the cost function is discussed. Secondly, the uniform stability of the algorithm is investigated. Thirdly, the mean convergence analysis is also added. Finally, the complexity analysis of the algorithm is incorporated. In addition, some synthetic and real-world experiments show the advantage of the proposed algorithm in comparison to some other adaptive algorithms in the literature of adaptive graph signal recovery

Multi-Agent Approach for facing challenges in Ultra-Large Scale systems

The primary characteristics of ULS systems are ultra-large size, number of lines of code (LOC); number of people employing the systems; amount of data stored, accessed, manipulated, and refined; number of connections and interdependencies among software components; number of hardware elements. These characteristics of ULS systems make it impossible to rely on our current knowledge and techniques of software development. We will face fundamental challenges in the design and evolution, orchestration and control, and monitoring and assessment of ULS systems. In this paper we try to make a combination between ULS systems and Multi Agent Systems and to use Multi Agent System strengths to help tackling some challenges found in ultra-large scale systems. Banking system is an instance of ULS systems. As a case study, in a real project we have designed Automated Teller Machine (ATM) combination with biometric sensors based on multi-agent architecture.DOI:http://dx.doi.org/10.11591/ijece.v4i2.4102