{"series_title":"SWAN'17","type":"conference","ddc":["000"],"_id":"71","author":[{"first_name":"Mike","last_name":"Czech","full_name":"Czech, Mike"},{"first_name":"Eyke","full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier","id":"48129"},{"last_name":"Jakobs","full_name":"Jakobs, Marie-Christine","first_name":"Marie-Christine"},{"first_name":"Heike","id":"573","last_name":"Wehrheim","full_name":"Wehrheim, Heike"}],"user_id":"15504","publication":"Proceedings of the 3rd International Workshop on Software Analytics","page":"23-26","has_accepted_license":"1","date_created":"2017-10-17T12:41:05Z","title":"Predicting Rankings of Software Verification Tools","citation":{"ieee":"M. Czech, E. Hüllermeier, M.-C. Jakobs, and H. Wehrheim, “Predicting Rankings of Software Verification Tools,” in <i>Proceedings of the 3rd International Workshop on Software Analytics</i>, 2017, pp. 23–26.","ama":"Czech M, Hüllermeier E, Jakobs M-C, Wehrheim H. Predicting Rankings of Software Verification Tools. In: <i>Proceedings of the 3rd International Workshop on Software Analytics</i>. SWAN’17. ; 2017:23-26. doi:<a href=\"https://doi.org/10.1145/3121257.3121262\">10.1145/3121257.3121262</a>","bibtex":"@inproceedings{Czech_Hüllermeier_Jakobs_Wehrheim_2017, series={SWAN’17}, title={Predicting Rankings of Software Verification Tools}, DOI={<a href=\"https://doi.org/10.1145/3121257.3121262\">10.1145/3121257.3121262</a>}, booktitle={Proceedings of the 3rd International Workshop on Software Analytics}, author={Czech, Mike and Hüllermeier, Eyke and Jakobs, Marie-Christine and Wehrheim, Heike}, year={2017}, pages={23–26}, collection={SWAN’17} }","mla":"Czech, Mike, et al. “Predicting Rankings of Software Verification Tools.” <i>Proceedings of the 3rd International Workshop on Software Analytics</i>, 2017, pp. 23–26, doi:<a href=\"https://doi.org/10.1145/3121257.3121262\">10.1145/3121257.3121262</a>.","chicago":"Czech, Mike, Eyke Hüllermeier, Marie-Christine Jakobs, and Heike Wehrheim. “Predicting Rankings of Software Verification Tools.” In <i>Proceedings of the 3rd International Workshop on Software Analytics</i>, 23–26. SWAN’17, 2017. <a href=\"https://doi.org/10.1145/3121257.3121262\">https://doi.org/10.1145/3121257.3121262</a>.","short":"M. Czech, E. Hüllermeier, M.-C. Jakobs, H. Wehrheim, in: Proceedings of the 3rd International Workshop on Software Analytics, 2017, pp. 23–26.","apa":"Czech, M., Hüllermeier, E., Jakobs, M.-C., &#38; Wehrheim, H. (2017). Predicting Rankings of Software Verification Tools. In <i>Proceedings of the 3rd International Workshop on Software Analytics</i> (pp. 23–26). <a href=\"https://doi.org/10.1145/3121257.3121262\">https://doi.org/10.1145/3121257.3121262</a>"},"year":"2017","date_updated":"2022-01-06T07:03:28Z","file_date_updated":"2018-11-02T14:24:29Z","status":"public","department":[{"_id":"355"},{"_id":"77"}],"file":[{"access_level":"closed","content_type":"application/pdf","relation":"main_file","creator":"ups","file_size":822383,"file_id":"5271","date_updated":"2018-11-02T14:24:29Z","success":1,"file_name":"fsews17swan-swanmain1.pdf","date_created":"2018-11-02T14:24:29Z"}],"abstract":[{"text":"Today, software verification tools have reached the maturity to be used for large scale programs. Different tools perform differently well on varying code. A software developer is hence faced with the problem of choosing a tool appropriate for her program at hand. A ranking of tools on programs could facilitate the choice. Such rankings can, however, so far only be obtained by running all considered tools on the program.In this paper, we present a machine learning approach to predicting rankings of tools on programs. The method builds upon so-called label ranking algorithms, which we complement with appropriate kernels providing a similarity measure for programs. 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 the software verification competition SV-COMP, we demonstrate our rank prediction technique to generalize well and achieve a rather high predictive accuracy (rank correlation > 0.6).","lang":"eng"}],"project":[{"_id":"1","name":"SFB 901"},{"_id":"12","name":"SFB 901 - Subprojekt B4"},{"name":"SFB 901 - Subproject B2","_id":"10"},{"name":"SFB 901 - Project Area B","_id":"3"},{"_id":"11","name":"SFB 901 - Subproject B3"}],"language":[{"iso":"eng"}],"doi":"10.1145/3121257.3121262"}