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Generative programming - principles and techniques of software engineering based on automated configuration and fragment-based component models

Current object-oriented (OO) and component technologies suffer from several problems such as the lack of analysis and design methods for the development for reuse, lack of effective techniques for dealing with many variants of components, loss of design knowledge due to the semantic gap between domain abstractions and programming language features, and runtime performance penalties for clean and flexible design. This thesis proposes Generative Programming (GP) as a comprehensive software development paradigm to achieving high intentionality, reusability, and adaptability without the need to compromise the runtime performance and computing resources of the produced software. In the area of analysis and design for GP, we investigate Domain Engineering (DE) methods and their integration with OO analysis and design (OOA/D) methods. The main difference between DE methods and OOA/D methods is that the first are geared towards developing whole families of systems while the latter focus on developing single systems. We identify feature modeling as the main contribution of DE to OOA/D. Feature models represent the configurability aspect of reusable software at an abstract level, i.e. without committing to any particular implementation technique such as inheritance, aggregation, or parameterized classes. We give a precise and extended formulation of the feature diagram notation and investigate the relationship between feature modeling, OO modeling and AspectOriented Programming. In the area of implementation for GP, we study various metaprogramming technologies. We identify modularly extensible programming environments as the ideal programming platform for GP, which allows implementing domain-specific optimizations, domain-specific displaying and editing, domain-specific debugging and code analysis, new composition mechanisms, etc., in a scalable way. We also propose new implementation techniques such as configuration generators based on mixin models with automatic configuration and configuration repositories and make several contribution to template metaprogramming. Based on the analysis of the areas mentioned above, we propose a new Domain Engineering method for the development of algorithmic reusable libraries (DEMRAL), which integrates various DE, OO, and AOP concepts. We validate the method by applying it to the domain of matrix computations, which results in the development of the Generative Matrix Computation Library (GMCL). We provide two implementation GMCL, one using generative programming techniques in C++ and another one in Intentional Programming (an modularly extendible programming environment). In addition to validating the usefulness of DEMRAL, the GMCL case study provides a concrete comparison of two generative implementation technologies. The C++ implementation of the matrix component (which is a part of C++ GMCL) comprises only 7500 lines of C++ code, but it is capable of generating more than 1840 different kinds of matrices. Despite the large number of provided matrix variants, the performance of the generated code is comparable with the performance of manually coded variants. The application of template metaprogramming allowed a highly intentional library API and a highly efficient library implementation at the same time. The implementation of GMCL within the Intentional Programming system (IP) demonstrates the advantages of IP, particularly in the area of debugging and displaying.