Arnab Sharma, Vitali M. Melnikov, E. Hüllermeier, H. Wehrheim
Testing is one of the most frequent means of quality assurance for software. Property-based testing aims at generating test suites for checking code against user-defined properties. Test input generation is, however, most often independent of the property to be checked, and is instead based on random or user-defined data generation.In this paper, we present property-driven unit testing of functions with numerical inputs and outputs. Alike property-based testing, it allows users to define the properties to be tested for. Contrary to property-based testing, it also uses the property for a targeted generation of test inputs. Our approach is a form of learning-based testing where we first of all learn a model of a given black-box function using standard machine learning algorithms, and in a second step use model and property for test input generation. This allows us to test both predefined functions as well as machine learned regression models. Our experimental evaluation shows that our property-driven approach is more effective than standard property-based testing techniques.
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Security of cryptographic protocols can be analysed by creating a model in a formal language and verifying the model in a tool. All such tools focus on the last part of the analysis, verification, and the interpretation of the specification is only explained in papers. Rather, we focus on the interpretation and modelling part by presenting a tool to aid the cryptographer throughout the process and automatically generating code in a target language. We adopt a data-centric approach where the protocol design is stored in a structured way rather than as textual specifications. Previous work shows how this approach facilitates the interpretation to a single language (for Tamarin) which required aftermath modifications. By improving the expressiveness of the specification data structure we extend the tool to export to an additional formal language, ProVerif, as well as a C++ fully running implementation. Furthermore, we extend the plugins to verify correctness in ProVerif and executability lemmas in Tamarin. In this paper we model the Diffie-Hellman key exchange, which is traditionally used as a case study; a demo is also provided for other commonly studied protocols, Needham-Schroeder and Needham-Schroeder-Lowe.
{"title":"Automating Cryptographic Protocol Language Generation from Structured Specifications","authors":"Roberto Metere, Luca Arnaboldi","doi":"10.1145/3524482.3527654","DOIUrl":"https://doi.org/10.1145/3524482.3527654","url":null,"abstract":"Security of cryptographic protocols can be analysed by creating a model in a formal language and verifying the model in a tool. All such tools focus on the last part of the analysis, verification, and the interpretation of the specification is only explained in papers. Rather, we focus on the interpretation and modelling part by presenting a tool to aid the cryptographer throughout the process and automatically generating code in a target language. We adopt a data-centric approach where the protocol design is stored in a structured way rather than as textual specifications. Previous work shows how this approach facilitates the interpretation to a single language (for Tamarin) which required aftermath modifications. By improving the expressiveness of the specification data structure we extend the tool to export to an additional formal language, ProVerif, as well as a C++ fully running implementation. Furthermore, we extend the plugins to verify correctness in ProVerif and executability lemmas in Tamarin. In this paper we model the Diffie-Hellman key exchange, which is traditionally used as a case study; a demo is also provided for other commonly studied protocols, Needham-Schroeder and Needham-Schroeder-Lowe.","PeriodicalId":119264,"journal":{"name":"2022 IEEE/ACM 10th International Conference on Formal Methods in Software Engineering (FormaliSE)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129423540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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