International Journal of Software Engineering and Knowledge Engineering

The classical optimization algorithms are not efficient in solving complex search and optimization problems. Thus, some heuristic optimization algorithms have been proposed. In this paper, exploration of association rules within numerical databases with Gravitational Search Algorithm (GSA) has been firstly performed. GSA has been designed as search method for quantitative association rules from the databases which can be regarded as search space. Furthermore, determining the minimum values of confidence and support for every database which is a hard job has been eliminated by GSA. Apart from this, the fitness function used for GSA is very flexible. According to the interested problem, some parameters can be removed from or added to the fitness function. The range values of the attributes have been automatically adjusted during the time of mining of the rules. That is why there is not any requirements for the pre-processing of the data. Attributes interaction problem has also been eliminated with the designed GSA. GSA has been tested with four real databases and promising results have been obtained. GSA seems an effective search method for complex numerical sequential patterns mining, numerical classification rules mining, and clustering rules mining tasks of data mining.

Software evolution has been a necessary part of the software development, while software architecture evolution is an important issue of software evolution. Software architecture evolution is generally realized through some evolution operations such as adding components or connectors, removing components or connectors. However, when some evolution operations are applied to the same software architecture in parallel, they sometimes make conflicting modifications, which will hinder the correctness of software architecture evolution. Although different approaches have been proposed to describe and analyze software architecture evolution, little work has been made to address evolution conflicts of software architectures. Focusing on parallel evolution conflicts of software architectures, firstly the paper establishes definitions and characterizations of parallel evolution conflicts of software architectures based on hypergraph morphisms and set theories, and describes parallel evolution conflicts of software architectures through these definitions and characterizations. Secondly the paper constructs the critical pair definition of parallel evolution conflicts of software architectures based on hypergraphs and hypergraph morphisms, analyzes the completeness of the critical pair, designs and optimizes an algorithm to detect efficiently parallel evolution conflicts of software architectures using the critical pair. Finally, a tool support is used to evaluate the effectiveness of the proposed method.

Spatiotemporal data mining studies the field of discovering interesting patterns from large spatiotemporal databases. Although these databases generate a huge volume of data daily from satellite images and mobile sensors like GPS, among these data we find first spatiotemporal and geographical data; secondly, the trajectories browsed by moving objects in some time intervals. Combination of these types of data leads to producing semantic trajectory data. Enriching trajectories with semantic geographical information leads to ease queries, analysis, and mining, in order to give more meaning to behaviors potentially extracted from trajectories. Therefore, applying mining techniques on semantic trajectories continue to prove to be a success story in discovering useful and nontrivial behavioral patterns of moving objects. The purpose of this paper is to make an overview of spatiotemporal knowledge discovery (STKD) and techniques recently used to extract knowledge from spatiotemporal data based on analysis of recent literature. Then leading towards a deeper analysis about semantic trajectory knowledge discovery as a specified field from STKD that integrates trajectory sample points with geographical data before applying mining techniques in order to extract behavioral knowledge from semantic trajectories which can be more useful and significant for the application users.

Among the fault localization methods of the coverage-based regression test cases, the ranking of suspiciousness degree is seriously impacted due to the existence of similar test cases, the existence of isolated treatment of each program entity and the coincidental correctness test cases as well as the poor quality of regression test cases, which reduces the efficiency of fault localization. This paper proposes a method that combines the control dependence and data dependence, test cases covering table and test result with vector angular cosine weight to solve the aforesaid problems. Experimental results show that the efficiency of the proposed fault localization of regression test cases is better than that of Tarantula, Jaccard, Ochiai, PPDG and CP methods.

Recently, the controller using wide-area measurement systems (WAMS) signals has been proposed by researchers. But, an unavoidable delay before the wide-area signals exists which is received by the controller. Accordingly, a delay-independent robust control problem of large interconnected power systems is presented based on the hybrid fuzzy controller for wide area measurement. A fuzzy controller is a nonlinear controller and it is not so sensitive to system topology, parameter and operating condition changes as the conventional ones. According to wide area power system complexity and variation of the loads and network conditions, FPSS has been considered in this paper. Furthermore, the proposed controller has been improved by chaotic version of shark smell optimization algorithm (CSSO) as an optimization problem. Effectiveness of the proposed method has been applied over the 10 machine 39 bus New England power system. Finally, the effectiveness of the proposed controller design methodology is demonstrated through simulation example. Obtained results demonstrate the superiority of the proposed strategy.

Software architects cannot avoid the consideration of quality attributes when designing software architecture. Architectural styles such as Layers and Client-Server are often used by architects to describe the overall structure and behavior of software. Although an architectural style affects the achievement of quality attributes, these quality attributes are directly performed by design decisions called architectural tactics. While the implementation of an architectural tactic supports a specific quality attribute, it often enhances or hurts other quality attributes in the software. In this paper, a framework for selecting the most appropriate architectural tactics according to their best achievement of the required levels of quality attributes when developing transaction processing systems is proposed. The proposed framework is based on fuzzy measures using Choquet Integral approach and takes into account the impact of architectural tactics on quality attributes, the preferences of quality attributes and the interactions between them. It can also be used to compare different potential architectures in terms of their supporting of quality attributes. The abilities and the advantages of the proposed framework are clarified via practical experiments using a case study.

In recent years, data on the Internet has grown exponentially, attaining enormous dimensions. This situation makes it difficult to obtain useful information from such data. Web mining is the process of using data mining techniques such as association rules, classification, clustering, and statistics to discover and extract information from Web documents. Optimization algorithms play an important role in such techniques. In this work, the parliamentary optimization algorithm (POA), which is one of the latest social-based metaheuristic algorithms, has been adopted for Web page classification. Two different data sets (Course and Student) were selected for experimental evaluation, and HTML tags were used as features. The data sets were tested using different classification algorithms implemented in WEKA, and the results were compared with those of the POA. The POA was found to yield promising results compared to the other algorithms. This study is the first to propose the POA for effective Web page classification.