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• W2891688103 abstract "Prevalent software engineering practices such as code reuse and the one-size-fits-all methodology have contributed to significant and widespread increases in the size and complexity of software. The resulting software bloat has led to decreased performance and increased security vulnerabilities. We propose a system called Chisel to enable programmers to effectively customize and debloat programs. Chisel takes as input a program to be debloated and a high-level specification of its desired functionality. The output is a reduced version of the program that is correct with respect to the specification. Chisel significantly improves upon existing program reduction systems by using a novel reinforcement learning-based approach to accelerate the search for the reduced program and scale to large programs. Our evaluation on a suite of 10 widely used UNIX utility programs each comprising 13-90 KLOC of C source code demonstrates that Chisel is able to successfully remove all unwanted functionalities and reduce attack surfaces. Compared to two state-of-the-art program reducers C-Reduce and Perses, which time out on 6 programs and 2 programs espectively in 12 hours, Chisel runs up to 7.1x and 3.7x faster and finishes on all programs." @default.
• W2891688103 created "2018-09-27" @default.
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• W2891688103 date "2018-10-15" @default.
• W2891688103 modified "2023-10-14" @default.
• W2891688103 title "Effective Program Debloating via Reinforcement Learning" @default.
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• W2891688103 doi "https://doi.org/10.1145/3243734.3243838" @default.
• W2891688103 hasPublicationYear "2018" @default.
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