On the Power of Abstraction: a Model-Driven Co-evolution Approach of Software Code

被引:9
|
作者
Khelladi, Djamel Eddine [1 ]
Combemale, Benoit [2 ]
Acher, Mathieu [3 ]
Barais, Olivier [3 ]
机构
[1] Univ Rennes, IRISA, CNRS, Rennes, France
[2] Univ Toulouse & Inria Rennes, Rennes, France
[3] Univ Rennes, INRIA, IRISA, Rennes, France
来源
2020 IEEE/ACM 42ND INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING: NEW IDEAS AND EMERGING RESULTS (ICSE-NIER 2020) | 2020年
关键词
METAMODELS;
D O I
10.1145/3377816.3381727
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
Model-driven software engineering fosters abstraction through the use of models and then automation by transforming them into various artefacts, in particular to code, for example: 1) from architectural models to code, 2) from metamodels to API code (with EMF in Eclipse), 3) from entity models to front-end and back-end code in Web stack application (with JHispter), etc. In all these examples, the generated code is usually enriched by developers with additional code implementing advanced functionalities (e.g., checkers, recommenders, etc.) to build a full coherent system. When the system must evolve, so are the models to re-generate the code. As a result, the developers' enriched code may be impacted and thus need to co-evolve accordingly. Many approaches support the co-evolution of various artifacts, but not the co-evolution of code. This paper sheds light on this issue and envisions to fill this gap. We formulate the hypothesis that the code co-evolution can be driven by the model changes by means of change propagation. To investigate this hypothesis, we implemented a prototype for the case of metamodels and their accompanying code in EMF Eclipse. As a preliminary evaluation, we considered the case of the OCL Pivot metamodel evolution and its code co-evolution in two projects from version 3.2.2 to 3.4.4. Preliminary results confirms our hypothesis that model-driven evolution changes can effectively drive the code co-evolution. On 562 impacts in two projects' code by 221 metamodel changes, our approach was able to reach the average of 89% and 92,5% respectively of precision and recall.
引用
收藏
页码:85 / 88
页数:4
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