{"paper":{"title":"Sample-Free Learning of Input Grammars for Comprehensive Software Fuzzing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.PL"],"primary_cat":"cs.SE","authors_text":"Alexander Kampmann, Andreas Zeller, Bj\\\"orn Mathis, Mathias H\\\"oschele, Rahul Gopinath","submitted_at":"2018-10-18T22:12:26Z","abstract_excerpt":"Generating valid test inputs for a program is much easier if one knows the input language. We present first successes for a technique that, given a program P without any input samples or models, learns an input grammar that represents the syntactically valid inputs for P -- a grammar which can then be used for highly effective test generation for P . To this end, we introduce a test generator targeted at input parsers that systematically explores parsing alternatives based on dynamic tracking of constraints; the resulting inputs go into a grammar learner producing a grammar that can then be us"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.08289","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}