Empirical Evaluation of Effort on Composing Design Models

TL;DR

This paper presents an empirical study on the effort involved in composing design models, introducing a quality model and practical insights to evaluate and reduce composition effort in software engineering.

Contribution

It offers a new quality model for evaluating composition effort, empirical data on influential factors, and guidance on assessing and managing effort in model composition.

Findings

Identified key factors influencing composition effort.

Developed a quality model for effort evaluation.

Provided empirical insights to improve composition practices.

Abstract

Model composition plays a central role in many software engineering activities such as evolving models to add new features and reconciling conflicting design models developed in parallel by different development teams. As model composition is usually an error-prone and effort-consuming task, its potential benefits, such as gains in productivity can be compromised. However, there is no empirical knowledge nowadays about the effort required to compose design models. Only feedbacks of model composition evangelists are available, and they often diverge. Consequently, developers are unable to conduct any cost-effectiveness analysis as well as identify, predict, or reduce composition effort. The inability of evaluating composition effort is due to three key problems. First, the current evaluation frameworks do not consider fundamental concepts in model composition such as conflicts and inconsistencies. Second, researchers and developers do not know what factors can influence the composition effort in practice. Third, practical knowledge about how such influential factors may affect the developers' effort is severely lacking. In this context, the contributions of this thesis are threefold: (i) a quality model for supporting the evaluation of model composition effort, (ii) practical knowledge, derived from a family of quantitative and qualitative empirical studies, about model composition effort and its influential factors, and (iii) insight about how to evaluate model composition efforts and tame the side effects of such influential factors.