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Ontology-supported Design Parameter Management for Change Impact Analysis

Jan Novacek, Ali Ahari, Alessandro Cornaglia, Frederik Haxel, Alexander Viehl, Oliver Bringmann, Wolfgang Rosenstiel

arXiv:2608.21949v1cs.SE

TL;DR

Engineering requirements change in ways whose consequences are difficult to foresee, while operative relationships among design parameters are often unknown. The paper proposes an ontology-based, knowledge-supported approach integrating heterogeneous models, requirements traceability, and Mission Profile Aware Design. Two use cases demonstrate identifying affected parameters and deriving lower-level real-time requirements.

  • Problem

    Requirement changes can have unforeseen consequences, while unknown operative relationships and heterogeneous models complicate Change Impact Analysis.

    Source: §I · p. 1

  • Method

    The approach integrates heterogeneous data through ontology-based system models, OWL representations, trace links, expert knowledge, and Mission Profile Aware Design.

    Source: §I · pp. 1–2, A. Overview · p. 3

  • Results

    Two use cases demonstrate the approach: detecting affected design parameters after a frequency change and deriving a memory write-latency requirement from real-time processing requirements.

    Source: §IV · p. 5, B. Use case: Power consumption · p. 5, C. Use case: Real-time application · p. 6

  • Takeaways & Limitations

    The approach supports decision-making by connecting requirements with design parameters and using formalized knowledge for system-model reasoning.

    Source: §I · p. 1, A. Overview · p. 3

Abstract

from arXiv · show

This paper presents an ontology-supported approach to the management of design parameters in engineering. This approach aims specifically at enabling Change Impact Analysis through Requirements Traceability and acquainted expert knowledge of design parameters. The approach is suitable for both software and hardware designs. The activities and features are mainly obtained by (1) the application of an ontology-based universal system modeling procedure proposal for model integration, (2) the utilization of a knowledge base for capturing expert knowledge and (3) a semantic Mission Profile Aware Design platform. OWL is used to represent information and the underlying data model can improve knowledge transfer among heterogeneous systems which are common in complex engineering projects. At the same time, effort to perform reasoning on such models can be reduced. A demonstration and hands-on description of two illustrative use cases complements the paper.

I. INTRODUCTION

The paper addresses Change Impact Analysis for evolving requirements by linking requirements, design parameters, and expert knowledge across heterogeneous engineering models. Its ontology-based approach supports traceability, reasoning, and decision-making, with Mission Profile Aware Design included.

  • Changing requirements can create unforeseen consequences, making monitoring, traceability, and Change Management mechanisms necessary, especially in safety-critical development.Source: §I · p. 1
  • The approach formalizes design-parameter knowledge for reasoning on system models and incorporates Mission Profiles to enable Mission Profile Aware Design.Supported model operations include validation, transformation, and exchange.Source: §I · p. 1
  • Because operative relationships are often unknown, requirement changes may cause undesired side effects or ripple effects and make Change Impact Analysis complex and effort-intensive.Source: §I · p. 1
  • The system aims to identify which design parameters are affected when a requirement changes by integrating heterogeneous data sources.Source: §I · p. 1
  • The claimed benefits include fewer errors, lower Change Impact Analysis costs, and improved understanding of designs and requirement–parameter relations.Source: §I · p. 1
  • The approach integrates heterogeneous data sources through an ontology-based system model and represents links between design parameters, requirements, and traceability information.OWL and Linked Data principles support sharing and processing of system models.Source: §I · pp. 1–2

II. BACKGROUND AND RELATED WORK

The background situates the approach within Mission Profile Aware Design, knowledge-based systems, and ontology-supported systems engineering. Prior work provides related modeling and integration ideas, but the cited approaches differ in their support for MPAD or Change Impact Analysis.

  • Mission Profiles capture environmental conditions and stress factors across a component’s life cycle, including usage, environmental profiles, requirements, and stress-test scenarios.Source: A. Mission Profiles · p. 2
  • Mission Profile Aware Design remains largely manual in prior work, motivating tool support for easier adoption and automation.Source: B. Mission Profile Aware Design · p. 2
  • Prior research reduced Mission Profile stressors to effective stress level and effective stress time, with experiments fitting theoretical predictions within statistical variations.Source: B. Mission Profile Aware Design · p. 2
  • Knowledge-based systems explicitly represent domain knowledge and exploit it through reasoning mechanisms.Source: C. Knowledge- and Model-based Systems… · p. 2
  • Existing systems-engineering ontology work models processes or integrates disciplinary data, but cited approaches do not directly support MPAD or implement Change Impact Analysis.Source: C. Knowledge- and Model-based Systems… · p. 2

D. Ontologies in Requirements Engineering

The paper situates its ontology-based approach within requirements engineering, where ontologies formalize requirements, domain knowledge, and relationships for traceability and reasoning. It addresses the limitation that many CIA approaches focus on functional changes, while this approach covers functional and non-functional changes.

III. APPROACH

The approach integrates expert knowledge, requirements, and mission-profile data through ontological representations and links between design parameters and requirements. Once established, the system model supports graph-based identification of parameters affected by changes.

III. APPROACH

  • The approach lifts expert knowledge and requirements documents into ontologies, then inserts links between design parameters and requirements semi-automatically.Source: A. Overview · p. 3
  • Standard graph analysis identifies affected parameters by collecting nodes on paths from a root node to leaf nodes after the system model and requirement links are established.Source: A. Overview · p. 3
  • The semantic MPAD platform is intended for use in other settings beyond the presented system.Source: B. Framework design · p. 3
  • The approach uses a semantic Mission Profile Aware Design platform to support Change Impact Analysis across heterogeneous data sources.Source: B. Framework design · p. 3

1) Architecture and implementation:

The architecture combines a Mission Profile Framework, an extensible semantic platform, and an OWL-based system model. These components integrate mission profiles and requirements while representing relationships among systems, components, functions, design parameters, and requirements.

Architecture and implementation

  • The Mission Profile Framework manages mission-profile documents, while the platform implements specialized Mission Profile Aware Design tasks.Source: 1) Architecture and implementation · p. 3
  • Input documents are mapped into ontological representations that can define relationships between objects and support semantic processing.Source: 2) Core functionality · p. 3
  • The platform can recognize changes in ontological requirements specifications and automatically react or trigger actions based on change type.Source: 2) Core functionality · p. 3
  • A RESTful API and web-based interface provide access to platform features and allow custom client applications to perform additional tasks.Source: 3) Interfaces · p. 3
  • The OWL system model represents relationships between components, functions, design parameters, Mission Profiles, systems, and requirements.Source: C. System model · p. 3
  • The general approach lifts expert parameter relationships and requirements into ontologies, links them, determines affected parameters, and passes relevant mission-profile data to the analyst.Source: Figure 2 · p. 4, Figure 3 · p. 4

1) Supporting Change Impact Analysis:

Change Impact Analysis is enabled by representing relationships between requirements and design parameters as Trace Links in the system model. The approach proposes semi-automatic link collection in which users accept or decline suggested candidates.

Supporting Change Impact Analysis

3) Integrating Mission Profiles:

Mission Profile data is integrated into the system model through links to component and document representations, enabling queries over specific mission-profile data portions.

D. Linking requirements to design parameters

The approach represents requirements, design parameters, and their links as ontology-based resources, choosing link individuals because they can carry additional information more easily.

2) Suggestion of links:

The system suggests requirement–parameter links using natural-language processing and WordNet noun-synonym relationships, while the evaluation describes two illustrative use cases.

2) Suggestion of links:

  • Users can accept or decline link candidates suggested from natural-language requirement and design-parameter descriptions.Source: 2) Suggestion of links · p. 5
  • The link-suggestion method uses Stanford CoreNLP for natural-language processing tasks.Source: 2) Suggestion of links · p. 5
  • WordNet identifies related noun synonyms to generate candidate links between requirements and design parameters.Source: 2) Suggestion of links · p. 5
  • The paper demonstrates the approach in two use cases: power-consumption analysis and transformation of real-time requirements into technology-level requirements.Source: §IV · p. 5

A. Capturing expert knowledge

CTEF captures expert knowledge about parameter relationships, exports it for ontology-based integration and reasoning, and supports a power-consumption use case whose change analysis identifies affected parameters.

A. Capturing expert knowledge

B. Use case: Power consumption

C. Use case: Real-time application

The real-time use case translates a frame-processing requirement into a memory write-latency constraint and links that requirement to corresponding design parameters. A learned performance model estimates that, at 600 MHz, write latency must not exceed 31 cycles.

  • The traffic sign recognition application processes 24 frames per second, imposing a 42 ms processing requirement per frame.Source: C. Use case: Real-time application · p. 6
  • A machine-learning model maps performance counters and main-memory write latency to single-frame processing time.The model was trained using benchmarks measuring cache misses, predictable branches, and data read accesses on an ARM-based Zynq-7000 platform.Source: C. Use case: Real-time application · p. 6
  • 31 cycles is the maximum write latency at 600 MHz for meeting the 42 ms-per-frame processing requirement.Source: C. Use case: Real-time application · p. 6
  • The proposed system links the real-time requirement to corresponding design parameters and supports specifying dependencies between requirements.Source: C. Use case: Real-time application · p. 6
  • The approach remains limited by its expectation that inputs use natural-language representations, with ontology mapping or entity matching identified as an improvement for practical applicability.Source: §V · p. 6
  • Further research could use reasoning to check consistency or identify links, while lifting natural-language requirements to formal specifications remains a prerequisite.Source: §V · p. 6
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