Highly-configurable software systems are pervasive, although configuration options and their interactions raise complexity of the program and increase maintenance effort. Especially load-time configuration options, such as parameters from command-line options or configuration files, are used with standard programming constructs such as variables and if statements intermixed with the program's implementation; manually tracking configuration options from the time they are loaded to the point where they may influence controlflow decisions is tedious and error prone. We design and implement Lotrack, an extended static taint analysis to automatically track configuration options. Lotrack derives a configuration map that explains for each code fragment under which configurations it may be executed. An evaluation on Android applications shows that Lotrack yields high accuracy with reasonable performance. We use Lotrack to empirically characterize how much of the implementation of Android apps depends on the platform's configuration options or interactions of these options.

Highly-configurable software systems are pervasive, although configuration options and their interactions raise complexity of the program and increase maintenance effort. Especially load-time configuration options, such as parameters from command-line options or configuration files, are used with standard programming constructs such as variables and if statements intermixed with the program's implementation; manually tracking configuration options from the time they are loaded to the point where they may influence controlflow decisions is tedious and error prone. We design and implement Lotrack, an extended static taint analysis to automatically track configuration options. Lotrack derives a configuration map that explains for each code fragment under which configurations it may be executed. An evaluation on Android applications shows that Lotrack yields high accuracy with reasonable performance. We use Lotrack to empirically characterize how much of the implementation of Android apps depends on the platform's configuration options or interactions of these options.

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Macro-based generators are in use for more than 40 years to generate Cobol source code and implement variability. Over the course of time, the systems were extended with many similar functionalities by copying and adapting existing pieces of code. The resulting generators are hard to understand and difficult to maintain. Clone detection can identify similar pieces of code which is a prerequisite to extract common features thus enabling a move to a featureoriented product line. This paper presents Hanni, a tool that combines clone detection of the input and output of generators to improve detection quality. Hanni uses standard textual clone detection tools on macro-based generators to detect clones in the macros and the generated Cobol. A mapping of the clones from the two sources is used to verify the detected clones and even suggest possible semantic clones. We are using generator examples from different industries based on the ADS generator framework to evaluate our tool. The results show that code clones are very common in these generators and possible problems in the detection can be identified using the generated files.

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