Open-source software (OSS) is widely reused as it provides convenience and efficiency in software development. Despite evident benefits, unmanaged OSS components can introduce threats, such as vulnerability propagation and license violation. Unfortunately, however, identifying reused OSS components is a challenge as the reused OSS is predominantly modified and nested. In this paper, we propose CENTRIS, a precise and scalable approach for identifying modified OSS reuse. By segmenting an OSS code base and detecting the reuse of a unique part of the OSS only, CENTRIS is capable of precisely identifying modified OSS reuse in the presence of nested OSS components. For scalability, CENTRIS eliminates redundant code comparisons and accelerates the search using hash functions. When we applied CENTRIS on 10,241 widely-employed GitHub projects, comprising 229,326 versions and 80 billion lines of code, we observed that modified OSS reuse is a norm in software development, occurring 20 times more frequently than exact reuse. Nonetheless, CENTRIS identified reused OSS components with 91% precision and 94% recall in less than a minute per application on average, whereas a recent clone detection technique, which does not take into account modified and nested OSS reuse, hardly reached 10% precision and 40% recall.

File systems are too large to be bug free. Although handwritten test suites have been widely used to stress file systems, they can hardly keep up with the rapid increase in file system size and complexity, leading to new bugs being introduced and reported regularly. These bugs come in various flavors: simple buffer overflows to sophisticated semantic bugs. Although bug-specific checkers exist, they generally lack a way to explore file system states thoroughly. More importantly, no turnkey solution exists that unifies the checking effort of various aspects of a file system under one umbrella. In this paper, we highlight the potential of applying fuzzing to find not just memory errors but, in theory, any type of file system bugs with an extensible fuzzing framework: Hydra. Hydra provides building blocks for file system fuzzing, including input mutators, feedback engines, a libOS-based executor, and a bug reproducer with test case minimization. As a result, developers only need to focus on building the core logic for finding bugs of their own interests. We showcase the effectiveness of Hydra with four checkers that hunt crash inconsistency, POSIX violations, logic assertion failures, and memory errors. So far, Hydra has discovered 91 new bugs in Linux file systems, including one in a verified file system (FSCQ), as well as four POSIX violations. CCS Concepts • Software and its engineering → Software testing and debugging; File systems management;

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File systems are too large to be bug free. Although handwritten test suites have been widely used to stress file systems, they can hardly keep up with the rapid increase in file system size and complexity, leading to new bugs being introduced. These bugs come in various flavors: buffer overflows to complicated semantic bugs. Although bug-specific checkers exist, they generally lack a way to explore file system states thoroughly. More importantly, no turnkey solution exists that unifies the checking effort of various aspects of a file system under one umbrella. In this article, to highlight the potential of applying fuzzing to find any type of file system bugs in a generic way, we propose H ydra , an extensible fuzzing framework. H ydra provides building blocks for file system fuzzing, including input mutators, feedback engines, test executors, and bug post-processors. As a result, developers only need to focus on building the core logic for finding bugs of their interests. We showcase the effectiveness of H ydra with four checkers that hunt crash inconsistency, POSIX violations, logic assertion failures, and memory errors. So far, H ydra has discovered 157 new bugs in Linux file systems, including three in verified file systems (FSCQ and Yxv6).