Biblio

The C preprocessor is used in many C projects to support variability and portability. However, researchers and practitioners criticize the C preprocessor because of its negative effect on code understanding and maintainability and its error proneness. More importantly, the use of the preprocessor hinders the development of tool support that is standard in other languages, such as automated refactoring. Developers aggravate these problems when using the preprocessor in undisciplined ways (e.g., conditional blocks that do not align with the syntactic structure of the code). In this article, we proposed a catalogue of refactorings and we evaluated the number of application possibilities of the refactorings in practice, the opinion of developers about the usefulness of the refactorings, and whether the refactorings preserve behavior. Overall, we found 5670 application possibilities for the refactorings in 63 real-world C projects. In addition, we performed an online survey among 246 developers, and we submitted 28 patches to convert undisciplined directives into disciplined ones. According to our results, 63% of developers prefer to use the refactored (i.e., disciplined) version of the code instead of the original code with undisciplined preprocessor usage. To verify that the refactorings are indeed behavior preserving, we applied them to more than 36 thousand programs generated automatically using a model of a subset of the C language, running the same test cases in the original and refactored programs. Furthermore, we applied the refactorings to three real-world projects: BusyBox, OpenSSL, and SQLite. This way, we detected and fixed a few behavioral changes, 62% caused by unspecified behavior in the C language.

Almost every software system provides configuration options to tailor the system to the target platform and application scenario. Often, this configurability renders the analysis of every individual system configuration infeasible. To address this problem, researchers have proposed a diverse set of sampling algorithms. We present a comparative study of 10 state-of-the-art sampling algorithms regarding their fault-detection capability and size of sample sets. The former is important to improve software quality and the latter to reduce the time of analysis. In a nutshell, we found that sampling algorithms with larger sample sets are able to detect higher numbers of faults, but simple algorithms with small sample sets, such as most-enabled-disabled, are the most efficient in most contexts. Furthermore, we observed that the limiting assumptions made in previous work influence the number of detected faults, the size of sample sets, and the ranking of algorithms. Finally, we have identified a number of technical challenges when trying to avoid the limiting assumptions, which questions the practicality of certain sampling algorithms.