AmbieGen is a tool for generating test cases for cyber-physical systems (CPS). In the context of SBST 2022 CPS tool competition, it has been adapted to generating virtual roads to test a car lane keeping assist system. AmbieGen leverages a two objective NSGA-II algorithm to produce the test cases. It has achieved the highest final score, accounting for the test case efficiency, effectiveness and diversity in both testing configurations.
{"title":"AmbieGen tool at the SBST 2022 Tool Competition","authors":"D. Humeniuk, G. Antoniol, Foutse Khomh","doi":"10.1145/3526072.3527531","DOIUrl":"https://doi.org/10.1145/3526072.3527531","url":null,"abstract":"AmbieGen is a tool for generating test cases for cyber-physical systems (CPS). In the context of SBST 2022 CPS tool competition, it has been adapted to generating virtual roads to test a car lane keeping assist system. AmbieGen leverages a two objective NSGA-II algorithm to produce the test cases. It has achieved the highest final score, accounting for the test case efficiency, effectiveness and diversity in both testing configurations.","PeriodicalId":206275,"journal":{"name":"2022 IEEE/ACM 15th International Workshop on Search-Based Software Testing (SBST)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123034426","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}
UTBotCpp and UTBot Java [3] are automatic white-box test generators for C/C++ and Java programs correspondingly. The tools were developed by Huawei and are based on symbolic and concrete execution. They try to cover as many branches as possible using program bytecode. For this purpose, UTBot tools analyze paths in the control flow graph of a given method, construct constraints for them, and try to find satisfying input values using SMT-solver to cover corresponding branches. In this paper, we report the results of UTBot Java at the tenth edition of the SBST 2022 tool competition.
{"title":"UTBot Java at the SBST2022 Tool Competition","authors":"Dmitry Ivanov, Alexey Menshutin, Denis Fokin, Yury Kamenev, Sergey Pospelov, Egor Kulikov, Nikita Stroganov","doi":"10.1145/3526072.3527529","DOIUrl":"https://doi.org/10.1145/3526072.3527529","url":null,"abstract":"UTBotCpp and UTBot Java [3] are automatic white-box test generators for C/C++ and Java programs correspondingly. The tools were developed by Huawei and are based on symbolic and concrete execution. They try to cover as many branches as possible using program bytecode. For this purpose, UTBot tools analyze paths in the control flow graph of a given method, construct constraints for them, and try to find satisfying input values using SMT-solver to cover corresponding branches. In this paper, we report the results of UTBot Java at the tenth edition of the SBST 2022 tool competition.","PeriodicalId":206275,"journal":{"name":"2022 IEEE/ACM 15th International Workshop on Search-Based Software Testing (SBST)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116527617","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}
Ezequiel Castellano, Stefan Klikovits, A. Cetinkaya, Paolo Arcaini
FreneticV is a search-based testing tool based on an evolutionary approach that generates roads where an automated driving agent possibly fails the lane-keeping task. It uses a curvature-based road representation and, compared to its predecessor Frenetic, considers the validity of the generated roads. In particular, it tries to avoid generating roads with overly sharp turns, detects self-intersecting roads, and can rotate and relocate roads to fit them in a given map.
{"title":"FreneticV at the SBST 2022 Tool Competition","authors":"Ezequiel Castellano, Stefan Klikovits, A. Cetinkaya, Paolo Arcaini","doi":"10.1145/3526072.3527532","DOIUrl":"https://doi.org/10.1145/3526072.3527532","url":null,"abstract":"FreneticV is a search-based testing tool based on an evolutionary approach that generates roads where an automated driving agent possibly fails the lane-keeping task. It uses a curvature-based road representation and, compared to its predecessor Frenetic, considers the validity of the generated roads. In particular, it tries to avoid generating roads with overly sharp turns, detects self-intersecting roads, and can rotate and relocate roads to fit them in a given map.","PeriodicalId":206275,"journal":{"name":"2022 IEEE/ACM 15th International Workshop on Search-Based Software Testing (SBST)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123929778","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}
Raihana Ferdous, Chia-kang Hung, Fitsum Meshesha Kifetew, D. Prandi, A. Susi
EvoMBT is a model-based test generator that uses search algorithms to generate tests from a given extended finite state machine (EFSM). In the context of Cyber-physical systems (CPS) testing, and in particular self-driving cars, we model a set of road configurations as an EFSM and use EvoMBT to generate different roads for testing the car. This report briefly introduces EvoMBT and summarizes its results in the Cyber-physical systems testing competition at SBST 2022. Overall the results achieved by EvoMBT are promising where effectiveness and efficiency scores are quite good while the scores related to diversity need improvement.
{"title":"EvoMBT at the SBST 2022 Tool Competition","authors":"Raihana Ferdous, Chia-kang Hung, Fitsum Meshesha Kifetew, D. Prandi, A. Susi","doi":"10.1145/3526072.3527534","DOIUrl":"https://doi.org/10.1145/3526072.3527534","url":null,"abstract":"EvoMBT is a model-based test generator that uses search algorithms to generate tests from a given extended finite state machine (EFSM). In the context of Cyber-physical systems (CPS) testing, and in particular self-driving cars, we model a set of road configurations as an EFSM and use EvoMBT to generate different roads for testing the car. This report briefly introduces EvoMBT and summarizes its results in the Cyber-physical systems testing competition at SBST 2022. Overall the results achieved by EvoMBT are promising where effectiveness and efficiency scores are quite good while the scores related to diversity need improvement.","PeriodicalId":206275,"journal":{"name":"2022 IEEE/ACM 15th International Workshop on Search-Based Software Testing (SBST)","volume":"329 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115875743","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}
We propose a novel online test generation algorithm WOGAN based on Wasserstein Generative Adversarial Networks. WOGAN is a general-purpose black-box test generator applicable to any system under test having a fitness function for determining failing tests. As a proof of concept, we evaluate WOGAN by generating roads such that a lane assistance system of a car fails to stay on the designated lane. We find that our algorithm has a competitive performance respect to previously published algorithms.
{"title":"Wasserstein Generative Adversarial Networks for Online Test Generation for Cyber Physical Systems","authors":"J. Peltomäki, Frankie Spencer, Ivan Porres","doi":"10.1145/3526072.3527522","DOIUrl":"https://doi.org/10.1145/3526072.3527522","url":null,"abstract":"We propose a novel online test generation algorithm WOGAN based on Wasserstein Generative Adversarial Networks. WOGAN is a general-purpose black-box test generator applicable to any system under test having a fitness function for determining failing tests. As a proof of concept, we evaluate WOGAN by generating roads such that a lane assistance system of a car fails to stay on the designated lane. We find that our algorithm has a competitive performance respect to previously published algorithms.","PeriodicalId":206275,"journal":{"name":"2022 IEEE/ACM 15th International Workshop on Search-Based Software Testing (SBST)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126687250","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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