{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/predicting-rankings-of-software-verification","title":"Predicting Rankings of Software Verification Competitions","arxiv_id":"1703.00757","date":"2017-03-02","proceeding":null,"authors":["Mike Czech","Eyke Hüllermeier","Marie-Christine Jakobs","Heike Wehrheim"],"abstract":"Software verification competitions, such as the annual SV-COMP, evaluate\nsoftware verification tools with respect to their effectivity and efficiency.\nTypically, the outcome of a competition is a (possibly category-specific)\nranking of the tools. For many applications, such as building portfolio\nsolvers, it would be desirable to have an idea of the (relative) performance of\nverification tools on a given verification task beforehand, i.e., prior to\nactually running all tools on the task.\n  In this paper, we present a machine learning approach to predicting rankings\nof tools on verification tasks. The method builds upon so-called label ranking\nalgorithms, which we complement with appropriate kernels providing a similarity\nmeasure for verification tasks. Our kernels employ a graph representation for\nsoftware source code that mixes elements of control flow and program dependence\ngraphs with abstract syntax trees. Using data sets from SV-COMP, we demonstrate\nour rank prediction technique to generalize well and achieve a rather high\npredictive accuracy. In particular, our method outperforms a recently proposed\nfeature-based approach of Demyanova et al. (when applied to rank predictions).","url_abs":"http://arxiv.org/abs/1703.00757v1","url_pdf":"http://arxiv.org/pdf/1703.00757v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"predicting-rankings-of-software-verification","repo_url":"https://github.com/zenscr/PyPRSVT","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"predicting-rankings-of-software-verification","repo_url":"https://github.com/mikeczech/PyVRank","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}