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- W2968504444 abstract "Many critical software systems developed in C utilize compile-time configurability. The many possible configurations of this software make bug detection through static analysis difficult. While variability-aware static analyses have been developed, there remains a gap between those and state-of-the-art static bug detection tools. In order to collect data on how such tools may perform and to develop real-world benchmarks, we present a way to leverage configuration sampling, off-the-shelf “variability-oblivious” bug detectors, and automatic feature identification techniques to simulate a variability-aware analysis. We instantiate our approach using four popular static analysis tools on three highly configurable, real-world C projects, obtaining 36,061 warnings, 80% of which are variability warnings. We analyze the warnings we collect from these experiments, finding that most results are variability warnings of a variety of kinds such as NULL dereference. We then manually investigate these warnings to produce a benchmark of 77 confirmed true bugs (52 of which are variability bugs) useful for future development of variability-aware analyses." @default.
- W2968504444 created "2019-08-22" @default.
- W2968504444 creator A5016175378 @default.
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- W2968504444 date "2019-08-12" @default.
- W2968504444 modified "2023-10-12" @default.
- W2968504444 title "An empirical study of real-world variability bugs detected by variability-oblivious tools" @default.
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- W2968504444 doi "https://doi.org/10.1145/3338906.3338967" @default.
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