Knowledge Discovery Metamodel (KDM)

The Knowledge Discovery Metamodel (KDM) is a publicly available Object Management Group (OMG) specification. KDM is a common intermediate representation for existing software systems and their operating environments, that defines common metadata required for deep semantic integration of application life cycle management tools.

Purpose and Role: KDM was developed to provide a standard way to represent the various elements of legacy software systems for use in software analysis and modernization. It provides an ontology for the key concepts of software engineering and can be used to perform knowledge discovery processes to extract higher-level information from source code.

Components: The KDM specification is organized into several packages representing different software system aspects. These include the code itself, the data it manipulates, the platform it runs on, etc. Each of these packages includes models and entities related to the corresponding aspect of the software system.

Importance: KDM is important because it provides a standardized way to represent and manipulate the knowledge embedded in legacy software systems. This can be essential for tasks like software modernization, where understanding the existing system is a key first step.

History: The KDM specification was developed by the OMG, which is an international, open membership, not-for-profit technology standards consortium. The OMG has been responsible for several widely used specifications, including the Unified Modeling Language (UML).

Benefits: KDM can make it easier to analyze and modernize these systems by providing a standardized way to represent software systems. It can also facilitate interoperability between different tools and systems.

Pros and Cons:

  1. Pros: KDM provides a standard, language-independent representation of a software system. This can facilitate more effective analysis and modernization of legacy systems.
  2. Cons: Using KDM requires a certain level of expertise, and it may not be suitable for all types of software systems. Additionally, while KDM is a standard, its adoption in the industry is not universal, which can limit its effectiveness in some contexts.

Examples: A software analysis tool might use KDM to generate a model of a legacy system. This model could then be used to identify potential issues or areas for improvement, such as overly complex code or not being used.

See Also

The Knowledge Discovery Metamodel (KDM) is a comprehensive standard framework to ensure interoperability and consistency across software analysis and modernization efforts. By offering a meta-model for representing the various aspects of software systems, KDM facilitates software understanding, measurement, and transformation, thereby supporting the lifecycle management of legacy systems and the integration of emerging technologies. To fully appreciate the scope and utility of KDM in the broader context of software engineering, exploring the following topics related to software modernization, Software Architecture, and system analysis is highly beneficial:

  • Software Modernization Strategies: This chapter examines approaches for updating and transforming legacy software systems to align with current and future business needs, including re-platforming, refactoring, rearchitecting, and replacing systems.
  • Architecture Reconstruction and Analysis: Discusses methods for recovering the architecture of a software system from its existing code base and documentation, enabling the analysis and understanding of its structure and behavior.
  • Model Driven Engineering (MDE): Explores the principles and practices of MDE, emphasizing the use of models as primary artifacts in the software development process, facilitating automation and reducing errors.
  • Software Metrics and Quality Assessment: This covers the measurement of software attributes and quality, using metrics to assess maintainability, performance, reliability, and other critical factors in software modernization.
  • Enterprise Architecture (EA) Frameworks: Reviews frameworks such as TOGAF, Zachman, and FEAF that guide the organization, documentation, and strategic alignment of business processes and IT infrastructure, highlighting how KDM can support EA initiatives.
  • Software Asset Management (SAM): Examines the processes for managing and optimizing the purchase, deployment, maintenance, utilization, and disposal of software applications within an organization.
  • Reverse Engineering and Code Analysis: Discusses techniques for analyzing a software system to identify its components and interrelationships and to create representations of the system at a higher level of abstraction.
  • Interoperability and Data Exchange Standards: Explores standards and protocols that facilitate the exchange of information between disparate systems, ensuring compatibility and cohesiveness in software ecosystems.
  • Legacy System Integration: Covers strategies for integrating legacy systems with modern applications and infrastructures, addressing challenges such as data compatibility, communication protocols, and user interface adaptation.
  • Application Portfolio Management (APM): Discusses the process of managing the IT software applications and software-based services in an organization, including decision-making regarding investment, optimization, and divestment.
  • Semantic Web and Ontologies in Software Engineering: Explores the use of semantic web technologies and ontologies to enhance the representation, discovery, and integration of software artifacts, potentially leveraging KDM for richer semantic annotation.
  • Governance, Risk And Compliance (GRC) in Software Development: Reviews the importance of adhering to legal, regulatory, and policy requirements in software development, including how KDM can support compliance efforts through better documentation and system understanding.
  • Emerging Trends in Software Analysis and Modernization: This chapter highlights new developments and technologies in software engineering that impact software analysis, modernization, and maintenance, considering how KDM can evolve to address these trends.

Delving into these topics provides a solid foundation for understanding the Knowledge Discovery Metamodel's role in facilitating effective software analysis, modernization, and maintenance. This exploration underscores the importance of KDM in enhancing the adaptability, sustainability, and strategic alignment of software systems within the fast-evolving technological landscape.