International Journal of Software Engineering and Knowledge Engineering

The Composition relationship or "black diamond" in the Unified Modeling Language (UML) is recognized for a long time as a key modeling construct. While prior research works demonstrated that this modeling operator had a dubious semantics, new expectations in the field of Component-Based Software Engineering especially, lead to revisit this operator so that it supports dynamical composition, and more precisely, composition modeling expressions and implementations that safely refer to states of software components. At first, the revision of the UML Composition must rely on well-formalized static dependencies between components as, for instance, the property of lifetime dependency between compound and constituent entities. This papers offers a UML metamodel and some associated logical assertions to strengthen the Composition relationship in UML 2. Despite the presence of new diagram types dedicated to composition (i.e., Composite Structure Diagrams), the UML 2 composition model is not yet sufficiently exhaustive and formal for dealing with all cases of composition, including those relating to agent, component or service computing and modeling.

The application of specification-based program verification techniques (e.g., black-box testing, formal proof) faces strong challenges in practice when the gap between the structure of a specification and that of its program is large. This paper describes a view-based program review approach to addressing these challenges. The essential idea of the approach is first to derive comparable views from the specification and program, and then detect and eliminate the violations of structural consistency in the program views on the basis of a set of criteria. We also developed a prototype tool to support the review approach, and conducted a case study to assess the effectiveness of the approach.

A software system evolves as changes are made to accommodate new features and repair defects. Software components are frequently interdependent, so changes made to one component can result in changes having to be made to other components to ensure that the system remains consistent; this is called change propagation. Accurate detection of change propagation is essential for software maintenance, which can be aided by accurate prediction of change propagation. In this paper, we study change propagation in three leading open-source software products: Linux, FreeBSD, and Apache HTTP Server. We use association rules-based data-mining techniques to detect change-propagation rules from the product version history. These rules are evaluated with respect to different training data sets and different test data sets. We discuss the applicability of using association-rule mining for change propagation, and several related issues. We find that a challenging issue in association-rule mining, concept drift, exists in software systems. Concept drift complicates the task of change-propagation prediction and requires special approaches, different from currently-used techniques for predicting change propagation.

Automatic reusability appraisal is helpful in evaluating the quality of developed or developing reusable software components and in identification of reusable components from existing legacy systems; that can save cost of developing the software from scratch. But the issue of how to identify reusable components from existing systems has remained relatively unexplored. In this paper, we mention a two-tier approach by studying the structural attributes as well as usability or relevancy of the component to a particular domain. We evaluate Probabilistic Latent Semantic Analysis (PLSA) approach, LSA's Singular Value Decomposition (SVD) technique, LSA's Semi-Discrete Matrix Decomposition (SDD) technique and Naïve Bayes approach to determine the Domain Relevancy of software components. It exploits the fact that Feature Vector codes can be seen as documents containing terms — the identifiers present in the components — and so text modeling methods that capture co-occurrence information in low-dimensional spaces can be used. In this research work, structural attributes of software components are explored using software metrics and quality of the software is inferred by Neuro-Fuzzy (NF) Inference engine, taking the metric values as input. The influence of different factors on the reusability is studied and the condition for the optimum reusability index is derived using Taguchi Analysis. The NF system is optimized by selecting initial rule-base through modified ID3 decision tree algorithm in combination with the results of Taguchi Analysis. The calculated reusability value enables to identify a good quality code automatically. It is found that the reusability value determined is close to the manual analysis used to be performed by the programmers or repository managers. So, the system developed can be used to enhance the productivity and quality of software development.

In this paper, a high-efficiency knowledge management system, HDIA_KMS, based on habitual domains and intelligent agents is proposed. The design concept of the system is to use an agent community to deal with the activities of knowledge management effectively, that is, knowledge sharing, knowledge classification, knowledge creation, and knowledge recovery. The features of this system are: collecting the surfing habits of each user and finding hidden patterns of departments to recommend articles by data mining; classifying the knowledge articles in accordance with departments and therefore rapidly finding out suitable documents for users; providing personal service through personal profiles; creating new knowledge documents by discussing among users; accepting and reclassifying improperly classified articles found by users; taking the abilities of collaboration, independence and automation of agents to help users use and improve the effects of HDIA_KMS. The PASSI methodology is adopted to analyze and design the system, and agents protocols follow the FIPA specifications.

Domain engineering can simplify the development of software systems in specific domains. During domain analysis, the first step of domain engineering, the domain is modeled at an abstract level, providing guidelines for modeling applications within that domain. Drawbacks of existing domain analysis approaches include poor guidelines for domain-specific application modeling, insufficient validations capability, and limited usability. In this paper we apply the Application-based Domain Modeling (ADOM) to the Object-Process Methodology (OPM). This application requires the extension of OPM with a classification mechanism. Showing that the ADOM-OPM approach overcomes limitations of existing approaches, we further verify experimentally that the level of correctness of an ADOM extended OPM model is higher than that achieved without the extension. Finally, we ensure that the proposed extension does not degrade the generic vanilla form of OPM.