Rejuvenating C++ Programs through Demacrofictation

As we migrate software to new versions of programming languages, we would like to improve the style of its design and implementation by replacing brittle idioms and abstractions with the more robust features of the language and its libraries. This process is called source code rejuvenation. In this context, we are interested in replacing C preprocessor macros in C++ programs with C++11 declarations. The kinds of problems engendered by the C preprocessor are many and well known. Because the C preprocessor operates on the token stream independently from the host language’s syntax, its extensive use can lead to hard-to-debug semantic errors. In C++11, the use of generalized constant expressions, type deduction, perfect forwarding, lambda expressions, and alias templates eliminate the need for many previous preprocessor-based idioms and solutions. Additionally, these features can be used to replace macros from legacy code providing better type safety and reducing software-maintenance efforts. In order to remove the macros, we have established a correspondence between different kinds of macros and the C++11 declarations to which they could be trans- formed. We have also developed a set of tools to automate the task of demacrofying C++ programs. One of the tools suggest a one-to-one mapping between a macro and its corresponding C++11 declaration. Other tools assist in carrying out iterative application of refactorings into a software build and generating rejuvenated programs. We have applied the tools to seven C++ libraries to assess the extent to which these libraries might be improved by demacrofication. Results indicate that between 52% and 98% of potentially refactorable macros could be transformed into C++11 declarations

A Framework for Preprocessor-Aware C Source Code Analyses

Analyses of C source code usually ignore the C preprocessor because of its complexity. Instead, these analyses either define their own approximate parser (or scanner) or else they require that their input already be preprocessed. Neither approach is entirely satisfactory: the first gives up accuracy (or incurs large implementation costs), while the second loses the preprocessor-based abstractions. We describe a framework that permits analyses to be expressed in terms of both preprocessing and parsing actions, allowing the implementer to focus on the analysis. We discuss an implementation of such a framework that embeds a C preprocessor, a parser, and a Perl interpreter for the action "hooks." Many common software engineering analyses can be written surprisingly easily using our implementation, replacing numerous ad-hoc tools. The framework's integration of the preprocessor and the parser further enables some analyses that otherwise would be especially difficult

Analysis and Transformation of Configurable Systems

Static analysis tools and transformation engines for source code belong to the standard equipment of a software developer. Their use simplifies a developer's everyday work of maintaining and evolving software systems significantly and, hence, accounts for much of a developer's programming efficiency and programming productivity. This is also beneficial from a financial point of view, as programming errors are early detected and avoided in the the development process, thus the use of static analysis tools reduces the overall software-development costs considerably. In practice, software systems are often developed as configurable systems to account for different requirements of application scenarios and use cases. To implement configurable systems, developers often use compile-time implementation techniques, such as preprocessors, by using #ifdef directives. Configuration options control the inclusion and exclusion of #ifdef-annotated source code and their selection/deselection serve as an input for generating tailor-made system variants on demand. Existing configurable systems, such as the linux kernel, often provide thousands of configuration options, forming a huge configuration space with billions of system variants. Unfortunately, existing tool support cannot handle the myriads of system variants that can typically be derived from a configurable system. Analysis and transformation tools are not prepared for variability in source code, and, hence, they may process it incorrectly with the result of an incomplete and often broken tool support. We challenge the way configurable systems are analyzed and transformed by introducing variability-aware static analysis tools and a variability-aware transformation engine for configurable systems' development. The main idea of such tool support is to exploit commonalities between system variants, reducing the effort of analyzing and transforming a configurable system. In particular, we develop novel analysis approaches for analyzing the myriads of system variants and compare them to state-of-the-art analysis approaches (namely sampling). The comparison shows that variability-aware analysis is complete (with respect to covering the whole configuration space), efficient (it outperforms some of the sampling heuristics), and scales even to large software systems. We demonstrate that variability-aware analysis is even practical when using it with non-trivial case studies, such as the linux kernel. On top of variability-aware analysis, we develop a transformation engine for C, which respects variability induced by the preprocessor. The engine provides three common refactorings (rename identifier, extract function, and inline function) and overcomes shortcomings (completeness, use of heuristics, and scalability issues) of existing engines, while still being semantics-preserving with respect to all variants and being fast, providing an instantaneous user experience. To validate semantics preservation, we extend a standard testing approach for refactoring engines with variability and show in real-world case studies the effectiveness and scalability of our engine. In the end, our analysis and transformation techniques show that configurable systems can efficiently be analyzed and transformed (even for large-scale systems), providing the same guarantees for configurable systems as for standard systems in terms of detecting and avoiding programming errors