Building measure-based prediction models for UML class diagram maintainability

Abstract

The usefulness of measures for the analysis and design of object oriented (OO) software is increasingly being recognized in the field of software engineering research. In particular, recognition of the need for early indicators of external quality attributes is increasing. We investigate through experimentation whether a collection of UML class diagram measures could be good predictors of two main subcharacteristics of the maintainability of class diagrams: understandability and modifiability. Results obtained from a controlled experiment and a replica support the idea that useful prediction models for class diagrams understandability and modifiability can be built on the basis of early measures, in particular, measures that capture structural complexity through associations and generalizations. Moreover, these measures seem to be correlated with the subjective perception of the subjects about the complexity of the diagrams. This fact shows, to some extent, that the objective measures capture the same aspects as the subjective ones. However, despite our encouraging findings, further empirical studies, especially using data Empir Software Eng (taken from real projects performed in industrial settings, are needed. Such further study will yield a comprehensive body of knowledge and experience about building prediction models for understandability and modifiability.

Rethinking Experiments in a Socio-Technical Perspective: The Case of Software Engineering

“…The choice to focus the experimental endeavor specifically on the class diagram out of nine diagrams defined in the UML standard is motivated by the fact that surveys in the industry show that "[the class diagram] is perceived by practitioners as the most important diagram type." [10] (p. 518). Moreover, by looking for good predictors of class diagram maintainability, the proponents of the Genero experiment aim to "allow [object-oriented] software designers to make better decisions early in the software development life cycle, thus contributing to the development of better quality [object-oriented] software."…”

“…Moreover, by looking for good predictors of class diagram maintainability, the proponents of the Genero experiment aim to "allow [object-oriented] software designers to make better decisions early in the software development life cycle, thus contributing to the development of better quality [object-oriented] software." [10] (p. 519). The Vokác experiment replicated a previous experiment aimed at verifying the beneficial effects of design patterns (i.e., proven solutions to design problems organized into reusable software modules).…”

“…The proponents of the Genero experiment have "used material that is more representative of real cases" [10] (p. 523) compared to previous endeavors of theirs. Moreover, they "improved the experimental tasks by trying to specify [tasks] that are similar to those required in real projects."…”

“…Moreover, they "improved the experimental tasks by trying to specify [tasks] that are similar to those required in real projects." [10] (p. 523).…”

“…Our focus is on those endeavors that adopted the most rigorous methodology from the scientific tradition, namely the controlled experiment, so that we are able to observe the widest range of instruments for scientific investigation put to the test in the context of SE. Some of these papers [9][10][11][12] explicitly refer to the presented work as a "controlled experiment"; others [13][14][15] only speak of an "experiment", but due to the rigorous procedures adopted, can be considered equivalent from a methodological point of view. In particular, we selected these works because they present the results of controlled experiments performed with human subjects, which makes them examples of one of the most challenging methodological endeavors, where the most rigorous (and traditional) experimental procedures are applied in a context where the human factor plays a primary role and brings in its typical unforeseeable and disruptive effects.…”