Optimizing Software Clustering using Hybrid Bee Colony Approach

Maintenance of software is the most expensive and complicated phase of the software development lifecycle. It becomes more cumbersome if the architecture of the software system is not available. Search-based optimization is found to be a technique very efficient in recovering the architecture of such a system. In this paper, we propose a technique which is based on a combination of artificial honey bee swarm intelligent algorithm and genetic algorithm to recover this architecture. In this way, it will be very helpful to software maintainers for efficient and effective software maintenance. In order to evaluate the success of this approach, it has been applied to a few real-world module clustering problems. The results we obtained support our claim that this approach produces architecture significantly better than the existing approaches. Keywords: Artificial bee colony algorithm, Genetic algorithm, Software clustering, Software Modularization

Identifying Similar Pages in Web Applications using a Competitive Clustering Algorithm

We present an approach based on Winner Takes All (WTA), a competitive clustering algorithm, to support the comprehension of static and dynamic Web applications during Web application reengineering. This approach adopts a process that first computes the distance between Web pages and then identifies and groups similar pages using the considered clustering algorithm. We present an instance of application of the clustering process to identify similar pages at the structural level. The page structure is encoded into a string of HTML tags and then the distance between Web pages at the structural level is computed using the Levenshtein string edit distance algorithm. A prototype to automate the clustering process has been implemented that can be extended to other instances of the process, such as the identification of groups of similar pages at content level. The approach and the tool have been evaluated in two case studies. The results have shown that the WTA clustering algorithm suggests heuristics to easily identify the best partition of Web pages into clusters among the possible partitions

Modelo de un sistema manejador de comunidades autorganizativo para un sistema operativo web multiagente

A model of the simulation and functional evaluation for a Community Manager System (CMS), on a Multiagent Web Operating System (MWOS) is presented. Following a reference design and concepts associated with swarm intelligence, the evaluation focused on auto-organization and emergent community management requirements using a gathering algorithm based on the behavioral emergent patterns of ant colonies. Both, Intra and Inter-wise CMS resources were searched using a dynamic auto-organizative management approach according to CMS requirements and the SWOM’s objectives.Este artículo describe la simulación y evaluación funcional de un Sistema Manejador de Comunidades (SMC) para un Sistema Operativo WEB Multiagente (SOWM), siguiendo un diseño de referencia y los conceptos asociados al área de Inteligencia de Enjambre (“Swarm Intelligence”) evaluando así, la autorganización y emergencia en la gestión de comunidades que requiere este tipo de sistemas. Para ello, se ha empleado un algoritmo de agrupamiento basado en el comportamiento emergente de las hormigas, para recoger y depositar cadáveres y formar pilas de ellos. Siguiendo un esquema de gestión de comunidades dinámica, autorganizativa y emergente, se realizó la búsqueda de servicios y recursos a nivel Intra e Inter en el SMC de acuerdo con los requerimientos y objetivos del SOWM y sus subsistemas

Architectural Layer Recovery for Software System Understanding and Evolution

This paper presents an approach to identify software layers for the understanding and evolution of software systems implemented with any object-oriented programming language. The approach first identifies relations between the classes of a software system and then uses a link analysis algorithm (i.e. the Kleinberg algorithm) to group them into layers. Additionally to assess the approach and the underlying techniques, the paper also presents a prototype of a supporting tool and the results from a case study

Dimensionality Reduction of Quality Objectives for Web Services Design Modularization

With the increasing use of service-oriented Architecture (SOA) in new software development, there is a growing and urgent need to improve current practice in service-oriented design. To improve the design of Web services, the search for Web services interface modularization solutions deals, in general, with a large set of conflicting quality metrics. Deciding about which and how the quality metrics are used to evaluate generated solutions are always left to the designer. Some of these objectives could be correlated or conflicting. In this paper, we propose a dimensionality reduction approach based on Non-dominated Sorting Genetic Algorithm (NSGA-II) to address the Web services re-modularization problem. Our approach aims at finding the best-reduced set of objectives (e.g. quality metrics) that can generate near optimal Web services modularization solutions to fix quality issues in Web services interface. The algorithm starts with a large number of interface design quality metrics as objectives (e.g. coupling, cohesion, number of ports, number of port types, and number of antipatterns) that are reduced based on the nonlinear correlation information entropy (NCIE).The statistical analysis of our results, based on a set of 22 real world Web services provided by Amazon and Yahoo, confirms that our dimensionality reduction Web services interface modularization approach reduced significantly the number of objectives on several case studies to a minimum of 2 objectives and performed significantly better than the state-of-the-art modularization techniques in terms of generating well-designed Web services interface for users.Master of ScienceSoftware Engineering, College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/145687/1/Thesis Report_Hussein Skaf.pdfDescription of Thesis Report_Hussein Skaf.pdf : Thesi

Reengineering Standalone C++ Legacy Systems into the J2EE Partition Distributed Environment

ABSTRACT Many enterprise systems are developed in C++ language and most of them are standalone. Because the standalone software can not follow the new market environment, reengineering the standalone legacy systems into distributed environment becomes a critical problem. Some methods have been proposed on related topics such as design recovery, the identification of the component, modeling the interfaces of components and components allocation. Up to now, there does not exist a reengineering process for partition distributed environment, which will offer distinct advantages on horizontal scalability and performance over normal distributed solutions. This paper presents a new process to reengineer C++ legacy systems into the J2EE partition distributed environment. The process consists of four steps: translation from C++ to Java code; extraction of components using the cluster technology; modeling component interfaces and partition of the components in J2EE distribute environment. It has been applied to a large equity-trading legacy system which has proved to be successful

Identifying Cloned Navigational Patterns in Web Applications

Web Applications are subject to continuous and rapid evolution. Often programmers indiscriminately duplicate Web pages without considering systematic development and maintenance methods. This practice creates code clones that make Web Applications hard to maintain and reuse. We present an approach to identify duplicated functionalities in Web Applications through cloned navigational pattern analysis. Cloned patterns can be generalized in a reengineering process, thus to simplify the structure and future maintenance of the Web Applications. The proposed method first identifies pairs of cloned pages by analyzing similarity at structure, content, and scripting code. Two pages are considered clones if their similarity is greater than a given threshold. Cloned pages are then grouped into clusters and the links connecting pages of two clusters are grouped too. An interconnection metric has been defined on the links between two clusters to express the effort required to reengineer them as well as to select the patterns of interest. To further reduce the comprehension effort, we filter out links and nodes of the clustered navigational schema that do not contribute to the identification of cloned navigational patterns. A tool supporting the proposed approach has been developed and validated in a case study