{"project":[{"_id":"1","name":"SFB 901"},{"_id":"11","name":"SFB 901 - Subprojekt B3"},{"_id":"12","name":"SFB 901 - Subprojekt B4"},{"name":"SFB 901 - Project Area B","_id":"3"}],"date_updated":"2022-01-06T07:03:29Z","type":"report","file":[{"content_type":"application/pdf","relation":"main_file","file_name":"Predicting Rankings of Soware Verification Competitions.pdf","date_updated":"2018-11-21T10:50:11Z","access_level":"closed","file_id":"5782","file_size":869984,"creator":"florida","success":1,"date_created":"2018-11-21T10:50:11Z"}],"year":"2017","has_accepted_license":"1","_id":"72","user_id":"15504","language":[{"iso":"eng"}],"ddc":["000"],"date_created":"2017-10-17T12:41:05Z","citation":{"apa":"Czech, M., Hüllermeier, E., Jakobs, M.-C., &#38; Wehrheim, H. (2017). <i>Predicting Rankings of Software Verification Competitions</i>.","short":"M. Czech, E. Hüllermeier, M.-C. Jakobs, H. Wehrheim, Predicting Rankings of Software Verification Competitions, 2017.","mla":"Czech, Mike, et al. <i>Predicting Rankings of Software Verification Competitions</i>. 2017.","chicago":"Czech, Mike, Eyke Hüllermeier, Marie-Christine Jakobs, and Heike Wehrheim. <i>Predicting Rankings of Software Verification Competitions</i>, 2017.","ieee":"M. Czech, E. Hüllermeier, M.-C. Jakobs, and H. Wehrheim, <i>Predicting Rankings of Software Verification Competitions</i>. 2017.","ama":"Czech M, Hüllermeier E, Jakobs M-C, Wehrheim H. <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} }"},"status":"public","title":"Predicting Rankings of Software Verification Competitions","author":[{"first_name":"Mike","last_name":"Czech","full_name":"Czech, Mike"},{"first_name":"Eyke","full_name":"Hüllermeier, Eyke","id":"48129","last_name":"Hüllermeier"},{"first_name":"Marie-Christine","full_name":"Jakobs, Marie-Christine","last_name":"Jakobs"},{"last_name":"Wehrheim","id":"573","full_name":"Wehrheim, Heike","first_name":"Heike"}],"department":[{"_id":"77"},{"_id":"355"}],"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). "}],"file_date_updated":"2018-11-21T10:50:11Z"}