Enhancing model transformation synthesis using natural language processing

TY - CHAP

T1 - Enhancing model transformation synthesis using natural language processing

AU - Lano, Kevin

AU - Umar, Aminu

PY - 2020/10/16

Y1 - 2020/10/16

N2 - In this paper we examine how model transformation specifications can be derived from requirements and examples, using a combination of natural language processing (NLP), machine learning (ML) and inductive logic programming (ILP) techniques, together with search-based software engineering (SBSE) for metamodel matching. The AI techniques are employed in order to improve the performance and accuracy of the base SBSE approach, and enable this to be used for a wider range of transformation cases.We propose a specific approach for the co-use of the techniques, and evaluate this on a range of transformation examples from different sources.

AB - In this paper we examine how model transformation specifications can be derived from requirements and examples, using a combination of natural language processing (NLP), machine learning (ML) and inductive logic programming (ILP) techniques, together with search-based software engineering (SBSE) for metamodel matching. The AI techniques are employed in order to improve the performance and accuracy of the base SBSE approach, and enable this to be used for a wider range of transformation cases.We propose a specific approach for the co-use of the techniques, and evaluate this on a range of transformation examples from different sources.

UR - http://www.scopus.com/inward/record.url?scp=85096766157&partnerID=8YFLogxK

U2 - 10.1145/3417990.3421386

DO - 10.1145/3417990.3421386

M3 - Conference paper

T3 - Proceedings - 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS-C 2020 - Companion Proceedings

SP - 277

EP - 286

BT - Proceedings - 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS-C 2020 - Companion Proceedings

ER -