Automated error analysis for the agilization of feature modeling

Software Product Lines (SPL) and agile methods share the common goal of rapidly developing high-quality software. Although they follow different approaches to achieve it, some synergies can be found between them by (i) applying agile techniques to SPL activities so SPL development becomes more agile; and (ii) tailoring agile methodologies to support the development of SPL. Both options require an intensive use of feature models, which are usually strongly affected by changes on requirements. Changing large-scale feature models as a consequence of changes on requirements is a well-known error-prone activity. Since one of the objectives of agile methods is a rapid response to changes in requirements, it is essential an automated error analysis support in order to make SPL development more agile and to produce error-free feature models.

As a contribution to find the intended synergies, this article sets the basis to provide an automated support to feature model error analysis by means of a framework which is organized in three levels: a feature model level, where the problem of error treatment is described; a diagnosis level, where an abstract solution that relies on Reiter’s theory of diagnosis is proposed; and an implementation level, where the abstract solution is implemented by using Constraint Satisfaction Problems (CSP).

To show an application of our proposal, a real case study is presented where the Feature-Driven Development (FDD) methodology is adapted to develop an SPL. Current proposals on error analysis are also studied and a comparison among them and our proposal is provided. Lastly, the support of new kinds of errors and different implementation levels for the proposed framework are proposed as the focus of our future work.