Support for Development and Maintenance of Generic Libraries

Software libraries have emerged as one of the most common ways of providingreusable, organized collections of software. Practically every softwareproject nowadays uses a number of software libraries for the most basic tasksas well as for domain-specific programming tasks. As the number, complexity,and importance of libraries increases, the need for support in development andmaintenance becomes apparent. Yet, only little assistance exists for thedevelopers and maintainers of libraries.In this thesis we are concerned with the large class of libraries known asgeneric libraries, that is, libraries in the philosophy of the StandardTemplate Library (STL). Specifically, we tackle two issues, one related tothe development and one related to the use of generic libraries.The first issue, particularly important for a library developer, isdetermining the impact of a change. Since STL, generic libraries in C++rely on concepts to precisely specify the requirements of genericalgorithms (function templates) on their parameters (template arguments).Modifying the definition of a concept even slightly, can have a potentiallylarge impact on the (interfaces of the) entire library. In particular thenon-local effects of a change, however, make its impact difficult to determineby hand. In this thesis we propose a conceptual change impactanalysis (CCIA), which determines the impact of changes of the conceptualspecification of a generic library. In a case study, we apply CCIA to a newproposal for STL iterator concepts, which is under review by the C++standardization committee, and show a number of unexpected incompatibilities.The second issue, component composition, is important for the user of alibrary. A generic library typically provides multiple implementations thatfulfill the requirements of the same concept, but differ with respect toproperties that a concept does not completely determine, for example, theperformance of an implementation component. Therefore, there exists often aquite large number of ways to (type-) correctly combine a set of librariescomponents. At the same time, it is non-trivial to see how to combine them"best." We present a component composition tool, which, based on accumulatedempirical data, automatically selects and composes the combination of librarycomponents that performs best for a specific task in a specific environment.Performance prediction uses an off-the-shelf, external machine learningalgorithm, which is trained on the empirical data, combined with a declarativedescription of performance factors. Using trait classes and metaprogrammingtechniques, the prediction is then mapped to a correctly configured, finalcomponent