{"author":[{"first_name":"Mike","last_name":"Czech","full_name":"Czech, Mike"},{"first_name":"Eyke","id":"48129","last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke"},{"first_name":"Marie-Christine","last_name":"Jakobs","full_name":"Jakobs, Marie-Christine"},{"id":"573","first_name":"Heike","last_name":"Wehrheim","full_name":"Wehrheim, Heike"}],"year":"2017","user_id":"15504","_id":"72","file_date_updated":"2018-11-21T10:50:11Z","status":"public","date_created":"2017-10-17T12:41:05Z","department":[{"_id":"77"},{"_id":"355"}],"project":[{"_id":"1","name":"SFB 901"},{"name":"SFB 901 - Subprojekt B3","_id":"11"},{"name":"SFB 901 - Subprojekt B4","_id":"12"},{"_id":"3","name":"SFB 901 - Project Area B"}],"citation":{"ieee":"M. Czech, E. Hüllermeier, M.-C. Jakobs, and H. Wehrheim, <i>Predicting Rankings of Software Verification Competitions</i>. 2017.","bibtex":"@book{Czech_Hüllermeier_Jakobs_Wehrheim_2017, title={Predicting Rankings of Software Verification Competitions}, author={Czech, Mike and Hüllermeier, Eyke and Jakobs, Marie-Christine and Wehrheim, Heike}, year={2017} }","ama":"Czech M, Hüllermeier E, Jakobs M-C, Wehrheim H. <i>Predicting Rankings of Software Verification Competitions</i>.; 2017.","short":"M. Czech, E. Hüllermeier, M.-C. Jakobs, H. Wehrheim, Predicting Rankings of Software Verification Competitions, 2017.","apa":"Czech, M., Hüllermeier, E., Jakobs, M.-C., &#38; Wehrheim, H. (2017). <i>Predicting Rankings of Software Verification Competitions</i>.","chicago":"Czech, Mike, Eyke Hüllermeier, Marie-Christine Jakobs, and Heike Wehrheim. <i>Predicting Rankings of Software Verification Competitions</i>, 2017.","mla":"Czech, Mike, et al. <i>Predicting Rankings of Software Verification Competitions</i>. 2017."},"ddc":["000"],"has_accepted_license":"1","type":"report","date_updated":"2022-01-06T07:03:29Z","file":[{"date_created":"2018-11-21T10:50:11Z","success":1,"file_id":"5782","access_level":"closed","content_type":"application/pdf","file_name":"Predicting Rankings of Soware Verification Competitions.pdf","date_updated":"2018-11-21T10:50:11Z","relation":"main_file","file_size":869984,"creator":"florida"}],"language":[{"iso":"eng"}],"abstract":[{"lang":"eng","text":"Software verification competitions, such as the annual SV-COMP, evaluate software verification tools with respect to their effectivity and efficiency. Typically, the outcome of a competition is a (possibly category-specific) ranking of the tools. For many applications, such as building portfolio solvers, it would be desirable to have an idea of the (relative) performance of verification tools on a given verification task beforehand, i.e., prior to actually running all tools on the task.In this paper, we present a machine learning approach to predicting rankings of tools on verification tasks. The method builds upon so-called label ranking algorithms, which we complement with appropriate kernels providing a similarity measure for verification tasks. Our kernels employ a graph representation for software source code that mixes elements of control flow and program dependence graphs with abstract syntax trees. Using data sets from SV-COMP, we demonstrate our rank prediction technique to generalize well and achieve a rather high predictive accuracy. In particular, our method outperforms a recently proposed feature-based approach of Demyanova et al. (when applied to rank predictions). "}],"title":"Predicting Rankings of Software Verification Competitions"}