Abstract
Model transformation by example is a novel trend in model-driven software engineering. The rationale behind this is to utilize existing knowledge represented by source and target models of previously developed systems; such as requirements analysis and software design models, respectively. Such knowledge can be utilized to derive transformation rules to be applied in future system developments. To achieve this goal, machine learning techniques can assist in discovering and formalizing desired transformation rules. Inductive Logic Programming (ILP) represents a highly applicable machine learning technique in this context. Given a set of examples and background knowledge encoded as a set of first-order logic descriptions, an ILP system attempts to derive rules describing different transformation steps in a purely declarative way. The induced rules follow the same logical description as the given examples and background knowledge. The objective of this work is to introduce initial setup of an ILP system that can be utilized to derive analysis-design transformation rules from a set of examples that represent pairs of analysis-design models.
Original language | English |
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Title of host publication | TENCON 2014 - 2014 IEEE Region 10 Conference |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9781479940752 |
DOIs | |
State | Published - 26 Jan 2015 |
Publication series
Name | IEEE Region 10 Annual International Conference, Proceedings/TENCON |
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Volume | 2015-January |
ISSN (Print) | 2159-3442 |
ISSN (Electronic) | 2159-3450 |
Bibliographical note
Publisher Copyright:© 2014 IEEE.
Keywords
- Inductive Logic Programming
- Model Driven Development
- Model Transformation
- Requirement Analysis
- Software Design
ASJC Scopus subject areas
- Computer Science Applications
- Electrical and Electronic Engineering