Pub Date : 2021-05-01DOI: 10.1109/SBST52555.2021.00019
D. Humeniuk, G. Antoniol, Foutse Khomh
SWAT is a test case generating tool for testing cyber-physical systems (CPS). In the context of SBST 2021 CPS testing competition, it has been adapted to generating virtual roads to test a lane keeping assist system. It has achieved the best ratio between valid and generated test cases, producing over 95% valid test cases in both testing configurations.
{"title":"SWAT tool at the SBST 2021 Tool Competition","authors":"D. Humeniuk, G. Antoniol, Foutse Khomh","doi":"10.1109/SBST52555.2021.00019","DOIUrl":"https://doi.org/10.1109/SBST52555.2021.00019","url":null,"abstract":"SWAT is a test case generating tool for testing cyber-physical systems (CPS). In the context of SBST 2021 CPS testing competition, it has been adapted to generating virtual roads to test a lane keeping assist system. It has achieved the best ratio between valid and generated test cases, producing over 95% valid test cases in both testing configurations.","PeriodicalId":199085,"journal":{"name":"2021 IEEE/ACM 14th International Workshop on Search-Based Software Testing (SBST)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134628855","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}
Pub Date : 2021-05-01DOI: 10.1109/SBST52555.2021.00014
A. Abdullin, M. Akhin, Mikhail Beliaev
Kex is an automatic white-box test generation tool for Java programs, which is able to generate executable test suites (as JUnit test suites) aiming to satisfy the branch coverage criterion. It uses symbolic execution to analyze control flow graphs of the program under test (PUT) and produces interesting symbolic inputs for each basic block of PUT. Kex then feeds these symbolic inputs to an original backward-search based algorithm called Reanimator, which generates executable JUnit test cases satisfying the symbolic inputs. This paper summarizes the results and experiences of Kex participation in the ninth edition of the Java unit testing tool competition at the International Workshop on Search-Based Software Testing (SBST) 2021.
{"title":"Kex at the 2021 SBST Tool Competition","authors":"A. Abdullin, M. Akhin, Mikhail Beliaev","doi":"10.1109/SBST52555.2021.00014","DOIUrl":"https://doi.org/10.1109/SBST52555.2021.00014","url":null,"abstract":"Kex is an automatic white-box test generation tool for Java programs, which is able to generate executable test suites (as JUnit test suites) aiming to satisfy the branch coverage criterion. It uses symbolic execution to analyze control flow graphs of the program under test (PUT) and produces interesting symbolic inputs for each basic block of PUT. Kex then feeds these symbolic inputs to an original backward-search based algorithm called Reanimator, which generates executable JUnit test cases satisfying the symbolic inputs. This paper summarizes the results and experiences of Kex participation in the ninth edition of the Java unit testing tool competition at the International Workshop on Search-Based Software Testing (SBST) 2021.","PeriodicalId":199085,"journal":{"name":"2021 IEEE/ACM 14th International Workshop on Search-Based Software Testing (SBST)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127690442","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}
Pub Date : 2021-05-01DOI: 10.1109/SBST52555.2021.00020
Seunghee Han, Jaeuk Kim, Geon Kim, Jaemin Cho, Jiin Kim, Shin Yoo
As autonomous driving gains attraction, testing of autonomous vehicles has become an important issue. However, testing in the real world is not only dangerous but also expensive. Consequently, a virtual test method has emerged as an alternative. Recently, a novel testing technique based on Procedural Content Generation (PCG) and Genetic Algorithm (GA), As-Fault, has been proposed to test the lane-keeping functionality of autonomous vehicles. This paper proposes new crossover operators for AsFault that can better preserve the coupling between genotype (representations of road segments) and phenotype (occurrences of interesting self-driving behaviour). We explain our design intentions and present a preliminary evaluation of the proposed operators using the Simulink autonomous driving simulator. We report promising early results: the proposed operators can lead not only to Out of Bound Episodes (OBEs) but also causes more vision errors in the simulation when compared to the original.
{"title":"Preliminary Evaluation of Path-aware Crossover Operators for Search-Based Test Data Generation for Autonomous Driving","authors":"Seunghee Han, Jaeuk Kim, Geon Kim, Jaemin Cho, Jiin Kim, Shin Yoo","doi":"10.1109/SBST52555.2021.00020","DOIUrl":"https://doi.org/10.1109/SBST52555.2021.00020","url":null,"abstract":"As autonomous driving gains attraction, testing of autonomous vehicles has become an important issue. However, testing in the real world is not only dangerous but also expensive. Consequently, a virtual test method has emerged as an alternative. Recently, a novel testing technique based on Procedural Content Generation (PCG) and Genetic Algorithm (GA), As-Fault, has been proposed to test the lane-keeping functionality of autonomous vehicles. This paper proposes new crossover operators for AsFault that can better preserve the coupling between genotype (representations of road segments) and phenotype (occurrences of interesting self-driving behaviour). We explain our design intentions and present a preliminary evaluation of the proposed operators using the Simulink autonomous driving simulator. We report promising early results: the proposed operators can lead not only to Out of Bound Episodes (OBEs) but also causes more vision errors in the simulation when compared to the original.","PeriodicalId":199085,"journal":{"name":"2021 IEEE/ACM 14th International Workshop on Search-Based Software Testing (SBST)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133228611","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}
Pub Date : 2021-02-23DOI: 10.1109/SBST52555.2021.00010
D. Humeniuk, G. Antoniol, Foutse Khomh
Consumer grade cyber-physical systems (CPS) are becoming an integral part of our life, automatizing and simplifying everyday tasks. Indeed, due to complex interactions between hardware, networking and software, developing and testing such systems is known to be a challenging task. Various quality assurance and testing strategies have been proposed. The most common approach for pre-deployment testing is to model the system and run simulations with models or software in the loop. In practice, most often, tests are run for a small number of simulations, which are selected based on the engineers' domain knowledge and experience. In this paper we propose an approach to automatically generate fault-revealing test cases for CPS. We have implemented our approach in Python, using standard frameworks and used it to generate scenarios violating temperature constraints for a smart thermostat implemented as a part of our IoT testbed. Data collected from an application managing a smart building have been used to learn models of the environment under ever changing conditions. The suggested approach allowed us to identify several pit-fails, scenarios (i.e., environment conditions and inputs), where the system behaves not as expected.
{"title":"Data Driven Testing of Cyber Physical Systems","authors":"D. Humeniuk, G. Antoniol, Foutse Khomh","doi":"10.1109/SBST52555.2021.00010","DOIUrl":"https://doi.org/10.1109/SBST52555.2021.00010","url":null,"abstract":"Consumer grade cyber-physical systems (CPS) are becoming an integral part of our life, automatizing and simplifying everyday tasks. Indeed, due to complex interactions between hardware, networking and software, developing and testing such systems is known to be a challenging task. Various quality assurance and testing strategies have been proposed. The most common approach for pre-deployment testing is to model the system and run simulations with models or software in the loop. In practice, most often, tests are run for a small number of simulations, which are selected based on the engineers' domain knowledge and experience. In this paper we propose an approach to automatically generate fault-revealing test cases for CPS. We have implemented our approach in Python, using standard frameworks and used it to generate scenarios violating temperature constraints for a smart thermostat implemented as a part of our IoT testbed. Data collected from an application managing a smart building have been used to learn models of the environment under ever changing conditions. The suggested approach allowed us to identify several pit-fails, scenarios (i.e., environment conditions and inputs), where the system behaves not as expected.","PeriodicalId":199085,"journal":{"name":"2021 IEEE/ACM 14th International Workshop on Search-Based Software Testing (SBST)","volume":"165 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126224999","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}
Pub Date : 1900-01-01DOI: 10.1109/sbst52555.2021.00005
{"title":"Message from the SBST 2021 Chairs","authors":"","doi":"10.1109/sbst52555.2021.00005","DOIUrl":"https://doi.org/10.1109/sbst52555.2021.00005","url":null,"abstract":"","PeriodicalId":199085,"journal":{"name":"2021 IEEE/ACM 14th International Workshop on Search-Based Software Testing (SBST)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128492240","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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