With the advancement of miniature sensors, wireless networking and context awareness, the importance of data-intensive computing is on the rise, with practical applications such as web categorization and data mining. One of the critical challenges in data-intensive computing is data clustering, as effective clustering algorithm will enable researchers and automated systems to analyze and organize massive amount of data much more efficiently. Many data clustering algorithms already exist, but most require a priori knowledge on the number of classes to guide the clustering process. We propose Auto_Ant_TMs_Shape, a two-phase algorithm, for automatically forming optimal number of clusters with arbitrary shapes. The first phase uses the hybrid approach of K-means and enhanced Ant-based template mechanism to generate small seed clusters with high purity in each cluster. In the second phase, small clusters are iteratively merged to obtain the final clusters using a merging algorithm. We apply Auto_Ant_TMs_Shape to 8 widely-used datasets, and compare the clustering results with two approaches based on density-based algorithm (DBSCAN) and Particle Swarm Optimization (PSO). The results show that Auto_Ant_TMs_Shape is very effective and thus achieve good clustering results in near optimal number of clusters without knowing the number of classes in advance.

Service-oriented architecture (SOA) provides a scalable and flexible framework for service composition. Service composition algorithms play an important role in selecting services from different providers to reach desirable QoS levels according to the performance requirements of composite services, and improve customer satisfaction. This paper proposes a novel QoS-based dynamic service composition technique for web services with Ant Colony Optimization (ACO) in an optimization approach. The novelty of this work lies with our multi-objective optimal-path selection modeling for QoS-based dynamic web service composition and a new version of ACO algorithm that is proposed to solve this multi-objective optimization problem. The experiments show that the new version of ACO algorithm is very efficient in solving such a problem.

Data clustering algorithms play an important role in effective analysis and organization of massive amounts of information. The K-means algorithm is the most commonly used partitional data clustering algorithm because of its simplicity in implementation and its high convergence rate. However, it suffers from the inability to always converge to the global optima, depending on how the data items are distributed initially. Ant-based Template Mechanism (Ant_TM) is another frequently used clustering algorithm, but it exhibits two major weaknesses in convergence rate and data purity of clustering results. In this paper, we first present a modification to the original Ant_TM to encourage formation of new cluster regions that enables the clustering result to move away from local optima. Second, we present two hybrid clustering algorithms based on the enhanced Ant-based Template Mechanism (Ant_TM) and the K-means algorithms. The rationale is that the integration of the K-means algorithm can speed up the convergence process and provide a perturbance to break free from local optimum clustering. We conduct experiments to compare the performance of our hybrid algorithms, against the enhanced Ant TM and the K-means algorithm, as well as the PSO+K and GA. The result shows that our algorithms outperform the original Ant_TM, K-means, and PSO+K, and is competitive against the GA in terms of the more compact and better separated clusters.

Evolvability is essential for computer systems to adapt to the dynamic and changing requirements in response to instant or delayed feedback from a service environment that nowadays is becoming more and more context aware; however, current context-aware service-centric models largely lack the capability to continuously explore human intentions that often drive system evolution. To support service requirements analysis of real-world applications for services computing, this paper presents a situation-theoretic approach to human-intention-driven service evolution in context-aware service environments. In this study, we give situation a definition that is rich in semantics and useful for modeling and reasoning human intentions, whereas the definition of intention is based on the observations of situations. A novel computational framework is described that allows us to model and infer human intentions by detecting the desires of an individual as well as capturing the corresponding context values through observations. An inference process based on Hidden Markov Model makes instant definition of individualized services at runtime possible, and significantly, shortens service evolution cycle. We illustrate the possible applications of this framework through a smart home example aimed at supporting independent living of elderly people.

This paper proposes an Input Adjustable Tree Algorithm for the flag problems of evolutionary testing (ET). With the algorithm, the dependencies of input and internal/flag variables can be determined. Based on that, ET guides the search efficiently with the presence of flag variables in the source code.

This paper aims to identify the missing links from theory of Genetic Algorithms (GAs) to application of GAs.

Maintaining traceability links among software artifacts is particularly important for many software engineering tasks. Even though automatic traceability link recovery tools are successful in identifying the semantic connections among software artifacts produced during software development, no existing traceability link management approach can effectively and automatically deal with software evolution. We propose a technique to automatically manage traceability link evolution and update the links in evolving software. Our novel technique, called incremental latent semantic indexing (iLSI), allows for the fast and low-cost LSI computation for the update of traceability links by analyzing the changes to software artifacts and by reusing the result from the previous LSI computation before the changes. We present our iLSI technique, and describe a complete automatic traceability link evolution management tool, TLEM, that is capable of interactively and quickly updating traceability links in the presence of evolving software artifacts. We report on our empirical evaluation with various experimental studies to assess the performance and usefulness of our approach.

Many problems in software engineering (SE) can be for mulated as optimization problems. Genetic algorithm (GA) is one of the more effective tools for solving such opti mization problems and has attracted the attention of SE re searchers in recent years. However, there is a general lack of sound support theory to help SE researchers investigate the applicability of GA to certain classes of SE problems. Without such a theory, numerous attempts to conduct a wide spectrum of experiments for solution validation appear to be ad hoc and the results are often difficult to generalize. This paper reports a foundational study to develop such a support theory. Some preliminary results are also given.

Personnel risk is an issue which has not been researched well but plays an important role to determine whether a software project succeeds or fails. Most existing research workfocuses on subjective expertise while an objective view is lacking. Furthermore, to the best of our knowledge, the demand for an automatic tool to support risk management has not been answered yet. In this research, based on objective historical data, we extend our earlier model, cability- based scheduling framework, by including risk analysis. Hence we provide a novel approach to mitigate personnel risks while achieving a project schedule with minimum cost.

As dynamic as software development, software artifacts are also constantly in evolution. As a result, traceability links among them are also changing over time. Even though traceability link recovery (TLR) tools have been successful in generating traceability relations among documentation and source code, they work on a snapshot of the artifacts at a particular time. Traceability link evolution has not been well-addressed. This hinders developers from having good understanding of the evolution of software. In this paper, we describe our incremental approach to traceability link recovery and management with the latent semantic indexing method. To complement with that approach, we suggest the use of the versioned hypermedia technology (VH). Traceability links can be consistently stored, versioned, and managed across different types of software artifacts.

This paper briefly discusses our current research direction about the theoretical view of applying genetic algorithms (GAs) to various software engineering problems