Empirical Investigations of Model Size, Complexity and Effort in a Large Scale, Distributed Model Driven Development Process

Abstract

Model driven development (MDD) is a software engineering practice that is gaining in popularity. We aim to investigate to what extend it is effective. There is a lack of empirical data to verify the pay-offs of employing MDD tools and techniques. In order to increase the knowledge we have of the impact of MDD in large scale industrial projects, we investigate the project characteristics of a large software development project in which MDD is used in a pure form. This study focuses on analyzing model size and complexity and metrics related to model quality and effort. Furthermore, project team members were asked to elaborate on their views on the impact of using MDD. Our findings include that larger models are more complex, contain more diagrams, are changed more often and worked on longer but do not necessarily contain more defects. However, models that are changed often do contain more defects. Benefits mentioned by team members were an increase in productivity, benefits from a consistent implementation and their perception of improvement of overall quality. Also, a reduction in complexity was attributed to the use of MDD techniques. We could confirm the perceived increase in the quality of the product in that the average amount of defects found is significantly lower than in similar size projects in which MDD was not employed.

MDD vs. traditional software development: A practitioner’s subjective perspective

“…This assumption has been reliably supported by different studies that show MDD economical benefits and advantages over both MBD approaches and traditional, code-centric, approaches [18,19]. Among these advantages we can cite lower entire product life cycle and maintenance costs, higher end-user satisfaction [20], shorter time-to-market and less human resources, short and long term productivity gains, improved project communication and software quality improvements or defect and rework reduction [21,22,23] . Also, these advantages are justified in literature by the higher level of compatibility between systems, the simplified design process, and the better communication between individuals and teams working on the system that the MDD paradigm fosters [24,25].…”

An empirical analysis on distribution patterns of software maintenance effort

“…Distribution of effort in software engineering process has been the basis for facilitating more reasonable software project planning [1][2][3][4]. In practice, however, many practitioners are still facing insufficient planning support when handling a particular project conditions, and solely relying on Rule of Thumb or expert judgment as this is only feasible approach available to them [6].…”

“…While traditional estimation and planning methods are more focused on the improvement of general estimation and planning capabilities for software development activities [1,3,[5][6], there is very limited research towards analyzing and understanding variations in effort distribution patterns for software maintenance activities.…”

Profiling and Framing Structures for Pervasive Information Systems Development

“…The development of large software systems is another area of interest that has been, for decades, subject of research work; several topics can be pointed out such as the exploration of issues related to the management of large scale software development [6,7], software architecture [8][9][10], model-driven development [11,12], among others. Not directly related with large projects, Medvidovic [13] points the relevance of software architecture in leveraging the pervasive and ubiquitous area.…”