PhD ceremony: M. Sinnema, 16.15 uur, Academiegebouw, Broerstraat 5
Thesis: Managing the complexity of variability in software product families
Promotor(s): prof. J. Bosch
Faculty: Mathematics and Natural Sciences
Since the 1960s, reuse has been the long-standing notion to solve the cost, quality and time-to-market issues associated with development of software applications. A major addition to existing reuse approaches since the 1990s are software product families. Although the adoption of this approach in industry clearly showed its benefits, several major challenges remain. One of these main challenges is reducing the high cost of product derivation; it is expensive, takes too long, and requires too much involvement of experts.
The goal of the work we present in this thesis is to define and validate techniques that are useful in industry, and that reduce the high cost of product derivation. To this extent, this thesis starts with a study on product derivation in practice. It provides (1) a framework of concepts that explains product derivation, (2) a description of case studies in terms of this framework, and (3) the identification of the underlying causes of the high cost of product derivation.
Making variability explicit in a model addresses a large number of the underlying causes. Based on the results of the product derivation study, this thesis therefore compares and classifies variability modeling techniques that are found in literature. This classification analyses the modeling concepts and tools, and identifies similarities, differences, and open issues.
Finally, this thesis describes our variability management framework COVAMOF. This framework addresses the main challenges of product derivation, as well as the open issues of variability modeling techniques. The core of COVAMOF consists of a variability modeling language and a tool-suite. On top of this core, we defined a derivation process and a variability assessment technique. In the last parts of this thesis, we illustrate and validate COVAMOF by presenting the results of the application of COVAMOF in industry. These results show that COVAMOF reduces product derivation cost in terms of expert dependency, effort, and time-to-market.
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