Evaluation of MDE Tools from a Metamodeling Perspective

Ever since the introduction of computers into society, researchers have been trying to raise the abstraction level at which we build software programs. We are currently adopting an abstraction level based on graphical models instead of source code: MDE. MDE is the driving force for some recent modeling languages and approaches, such as OMG’s UML or Domain-Specific Modeling. All these approaches are founded on metamodeling: defining languages that represent a problem-domain. A key factor for the success of any approach is appropriate tool support. However, only recently have tool creators started considering metamodeling as an important issue in their list of concerns. In this paper, we evaluate a small set of MDE tools from the perspective of the metamodeling activity, focusing on both architectural and practical aspects. Then, using the results of this evaluation, we discuss open research issues for MDE-based software development tools.

Bug Fixing Practices within Free/Libre Open Source Software Development Teams

Free/Libre open source software (FLOSS, e.g., Linux or Apache) is primarily developed by distributed teams. Developers contribute from around the world and coordinate their activity almost exclusively by means of email and bulletin boards, yet some how profit from the advantages and evade the challenges of distributed software development. In this article we investigate the structure and the coordination practices adopted by development teams during the bug-fixing process, which is considered one of main areas of FLOSS project success. In particular, based on a codification of the messages recorded in the bug tracking system of four projects, we identify the accomplished tasks, the adopted coordination mechanisms, and the role undertaken by both the FLOSS development team and the FLOSS community. We conclude with suggestions for further research.

Web Services, Service-Oriented Computing, and Service-Oriented Architecture

Service-oriented architecture (SOA), Web services, and service-oriented computing (SOC) have become the buzz words of the day for many in the business world. It seems that virtually every company has implemented, is in the midst of implementing, or is seriously considering SOA projects, Web services projects, or service-oriented computing. A problem many organizations face when entering the SOA world is that there are nearly as many definitions of SOA as there are organizations adopting it. Further complicating the issue is an unclear picture of the value added from adopting the SOA or Web services paradigm. This article attempts to shed some light on the definition of SOA and the difficulties of assessing the value of SOA or Web services via return on investment (ROI) or nontraditional approaches, examines the scant body of evidence empirical that exists on the topic of SOA, and highlights potential research directions in the area.

The Impact of Ideology on the Organizational Adoption of Open Source Software

Previous research has shown that the open source movement shares a common ideology. Employees belonging to the open source movement often advocate the use of open source software within their organization. Hence, their belief in the underlying open source software ideology may influence the decision making on the adoption of open source software. This may result in an ideological—rather than pragmatic—decision. A recent study has shown that American organizations are quite pragmatic in their adoption decision. We argue that there may be circumstances in which there is more opportunity for ideological behavior. We therefore investigated the organizational adoption decision in Belgian organizations. Our results indicate that most organizations are pragmatic in their decision making. However, we have found evidence that suggests that the influence of ideology should not be completely disregarded in small organizations.

A Multiple-Bits Watermark for Relational Data

At the heart of the information economy, commercially and publicly useful databases must be sufficiently protected from pirated copying. Complementary to the Database Protection Act, database watermarking techniques are designed to thwart pirated copying by embedding owner-specific information into databases so that the ownership of pirated copies of protected databases can be claimed if the embedded information is detected. This article presents a robust watermarking scheme for embedding a multiplebits watermark to numerical attributes in database relations. The scheme is robust in the sense that it provides an upper bound for the probability that a valid watermark is detected from unmarked data, or a fictitious secret key is discovered from pirated data. This upper bound is independent of the size of the data. The scheme is extended to database relations without primary-key attributes to thwart attributerelated attacks. The scheme is also extended to multiple watermarks for defending additive attacks and for proving joint ownership.

Exploring the Effects of Process Characteristics on Product Quality in Open Source Software Development

There has been considerable discussion on the possible impacts of open source software development practices, especially in regard to the quality of the resulting software product. Recent studies have shown that analyzing data from source code repositories is an efficient way to gather information about project characteristics and programmers, showing that OSS projects are very heterogeneous in their team structures and software processes. However, one problem is that the resulting process metrics measuring attributes of the development process and of the development environment do not give any hints about the quality, complexity, or structure of the resulting software. Therefore, we expanded the analysis by calculating several product metrics, most of them specifically tailored to object-oriented software. We then analyzed the relationship between these product metrics and process metrics derived from a CVS repository. The aim was to establish whether different variants of open source development processes have a significant impact on the resulting software products. In particular we analyzed the impact on quality and design associated with the numbers of contributors and the amount of their work, using the GINI coefficient as a measure of inequality within the developer group.

BROOD

A critical success factor for information systems is their ability to evolve as their environment changes. There is compelling evidence that the management of change in business policy can have a profound effect on an information system’s ability to evolve effectively and efficiently. For this to be successful, there is a need to represent business rules from the early requirements stage, expressed in user-understandable terms, to downstream system design components and maintain these throughout the lifecycle of the system. Any user-oriented changes could then be traced and if necessary propagated from requirements to design specifications and evaluated by both end-users and developers about their impact on the system. The BROOD approach, discussed in this article, aims to provide seamless traceability between requirements and system designs through the modelling of business rules and the successive transformations, using UML as the modelling framework.

Conflicts, Compromises, and Political Decisions

This article describes the architecture development process in an international ICT company, which is building a comprehensive e-business system for its customers. The implementation includes the integration of data and legacy systems from independent business units and the construction of a uniform Webbased customer interface. We followed the early process of architecture analysis and definition over a year. The research focuses on the creation of e-business architecture and observes that instead of guided by a prescribed method, the architecture emerges through somewhat non-deliberate actions obliged by the situation and its constraints, conflicts, compromises, and political decisions. The interview-based qualitative data is analyzed using grounded theory and a coherent story explaining the situation and its forces is extracted. Conclusions are drawn from the observations and possibilities and weaknesses of the support that UML and RUP provide for the process are pointed out.

The Application-Based Domain Modeling Approach

Domain analysis provides guidelines and validation aids for specifying families of applications and capturing their terminology. Thus, domain analysis can be considered as an important type of reuse, validation, and knowledge representation. Metamodeling techniques, feature-oriented approaches, and architectural-based methods are used for analyzing domains and creating application artifacts in these domains. These works mainly focus on representing the domain knowledge and creating applications. However, they provide insufficient guidelines (if any) for creating complete application artifacts that satisfy the application requirements on one hand and the domain rules and constraints on the other hand. This chapter claims that domain artifacts may assist in creating complete and valid application artifacts and presents a general approach, called Application-based DOmain Modeling (ADOM), for this purpose. ADOM enables specifying domains and applications similarly, (re)using domain knowledge in applications, and validating applications against the relevant domain models and artifacts. The authors demonstrate the approach, which is supported by a CASE tool, on the standard modeling language, UML, and report experimental results which advocate that the availability of domain models may help achieve more complete application models without reducing the comprehension of these models.

The Use of Ontology for Data Mining with Incomplete Data

Ontology has recently received considerable attention. Based on a domain analysis of knowledge representations in data mining, this chapter presents a structure of ontology for data mining as well as the unique resources for data mining with incomplete data. This chapter demonstrates the effectiveness of ontology for data mining with incomplete data through an experiment.