| Abstract | A software product line is a unified representation of a set of conceptually similar software systems that share many common features and satisfy the requirements of a particular domain. Within the context of software product lines, feature models are tree-like structures that are widely used for modeling and representing the inherent commonality and variability of software product lines. Given the fact that many different software systems can be spawned from a single software product line, it can be anticipated that a low-quality design can ripple through to many spawned software systems. Therefore, the need for early indicators of external quality attributes is recognized in order to avoid the implications of defective and low-quality design during the late stages of production. In this paper, we propose a set of structural metrics for software product line feature models and theoretically validate them using valid measurement-theoretic principles. Further, we investigate through controlled experimentation whether these structural metrics can be good predictors (early indicators) of the three main subcharacteristics of maintainability: analyzability, changeability, and understandability. More specifically, a four-step analysis is conducted: (1) investigating whether feature model structural metrics are correlated with feature model maintainability through the employment of classical statistical correlation techniques; (2) understanding how well each of the structural metrics can serve as discriminatory references for maintainability; (3) identifying the sufficient set of structural metrics for evaluating each of the subcharacteristics of maintainability; and (4) evaluating how well different prediction models based on the proposed structural metrics can perform in indicating the maintainability of a feature model. Results obtained from the controlled experiment support the idea that useful prediction models can be built for the purpose of evaluating feature model maintainability using early structural metrics. Some of the structural metrics show significant correlation with the subjective perception of the subjects about the maintainability of the feature models. © 2010 Springer Science+Business Media, LLC. |
|---|
