WebJun 1, 2024 · This paper explores the opportunities and challenges of an alternative way of creating static bug detectors: neural bug finding. The basic idea is to formulate bug detection as a classification problem, and to address this problem with neural networks trained on examples of buggy and non-buggy code. WebSep 6, 2024 · Static bug finders have been widely-adopted by developers to find bugs in real world software projects. They leverage predefined heuristic static analysis rules to scan source code or binary code of a software project, and report violations to these rules as warnings to be verified.
Google Bug Hunters
WebDec 10, 2024 · The group claims to have found 19 previously unknown bugs in open-source Python packages from PyPI as detailed in the paper, Self-Supervised Bug Detection and Repair, presented at the recent... WebMay 1, 2024 · Context Static bug detection techniques are commonly used to automatically detect software bugs. The biggest obstacle to the wider adoption of static bug detection tools is false... meat markets under a microscope
Find bugs in static bug finders Proceedings of the 30th …
WebStatic bug finders have been widely-adopted by developers to find bugs in real world software projects. They leverage predefined heuristic static analysis rules to scan source code or... WebJul 12, 2024 · We compare NeurSA against several static analyzers (e.g. Facebook Infer and Pinpoint) on a set of null pointer dereference bugs. Results show that NeurSA is more precise in catching the real bugs and suppressing the spurious warnings. WebThey find bugs in embedded software and use proof-based techniques such as abstract interpretation to prove that the software is safe. Polyspace Bug Finder™ identifies run-time errors, data flow problems, and other defects in C and C++ embedded software. Using static analysis, Polyspace Bug Finder analyzes software control, data flow, meat markets that sell chitterlings