Allison Sullivan, Kaiyuan Wang, Razieh Nokhbeh Zaeem, S. Khurshid
We present two novel approaches for automated testing of models written in Alloy – a well-known declarative, first-order language that is supported by a fully automatic SAT-based analysis engine. The first approach introduces automated test generation for Alloy and is embodied by three techniques that create test suites in the traditional spirit of black-box, white-box, and mutation-based testing. The second approach introduces mutation testing for Alloy and defines how to create mutants of Alloy models, compute mutation testing results, and check for equivalent mutants using SAT. The two approaches build on the theoretical foundation defined previously by our AUnit framework, which introduced the idea of unit testing for Alloy in the spirit of unit testing for imperative languages. While test generation and mutation testing are heavily studied problems with many solutions in the context of imperative languages, the key novelty of our work is to introduce and address these problems for the declarative programming paradigm, specifically for the Alloy language. Experimental results using several Alloy subjects, including those with real faults, demonstrate the efficacy of our framework.
{"title":"Automated Test Generation and Mutation Testing for Alloy","authors":"Allison Sullivan, Kaiyuan Wang, Razieh Nokhbeh Zaeem, S. Khurshid","doi":"10.1109/ICST.2017.31","DOIUrl":"https://doi.org/10.1109/ICST.2017.31","url":null,"abstract":"We present two novel approaches for automated testing of models written in Alloy – a well-known declarative, first-order language that is supported by a fully automatic SAT-based analysis engine. The first approach introduces automated test generation for Alloy and is embodied by three techniques that create test suites in the traditional spirit of black-box, white-box, and mutation-based testing. The second approach introduces mutation testing for Alloy and defines how to create mutants of Alloy models, compute mutation testing results, and check for equivalent mutants using SAT. The two approaches build on the theoretical foundation defined previously by our AUnit framework, which introduced the idea of unit testing for Alloy in the spirit of unit testing for imperative languages. While test generation and mutation testing are heavily studied problems with many solutions in the context of imperative languages, the key novelty of our work is to introduce and address these problems for the declarative programming paradigm, specifically for the Alloy language. Experimental results using several Alloy subjects, including those with real faults, demonstrate the efficacy of our framework.","PeriodicalId":112258,"journal":{"name":"2017 IEEE International Conference on Software Testing, Verification and Validation (ICST)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121109424","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}
This paper presents a learning-based approach to detecting failures in reactive systems. The technique is based on inferring models of multiple implementations of a common specification which are pair-wise cross-checked for equivalence. Any counterexample to equivalence is flagged as suspicious and has to be analysed manually. Hence, it is possible to find possible failures in a semi-automatic way without prior modelling. We show that the approach is effective by means of a case study. For this case study, we carried out experiments in which we learned models of five implementations of MQTT brokers/servers, a protocol used in the Internet of Things. Examining these models, we found several violations of the MQTT specification. All but one of the considered implementations showed faulty behaviour. In the analysis, we discuss effectiveness and also issues we faced.
{"title":"Model-Based Testing IoT Communication via Active Automata Learning","authors":"Martin Tappler, B. Aichernig, R. Bloem","doi":"10.1109/ICST.2017.32","DOIUrl":"https://doi.org/10.1109/ICST.2017.32","url":null,"abstract":"This paper presents a learning-based approach to detecting failures in reactive systems. The technique is based on inferring models of multiple implementations of a common specification which are pair-wise cross-checked for equivalence. Any counterexample to equivalence is flagged as suspicious and has to be analysed manually. Hence, it is possible to find possible failures in a semi-automatic way without prior modelling. We show that the approach is effective by means of a case study. For this case study, we carried out experiments in which we learned models of five implementations of MQTT brokers/servers, a protocol used in the Internet of Things. Examining these models, we found several violations of the MQTT specification. All but one of the considered implementations showed faulty behaviour. In the analysis, we discuss effectiveness and also issues we faced.","PeriodicalId":112258,"journal":{"name":"2017 IEEE International Conference on Software Testing, Verification and Validation (ICST)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128738823","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}
Junjie Chen, Y. Bai, Dan Hao, Lingming Zhang, Lu Zhang, Bing Xie
Code coverage is the dominant criterion in test-suite reduction. Typically, most test-suite reduction techniques repeatedly remove tests covering code that has been covered by other tests from the test suite. However, test-suite reduction based on code coverage alone may incur fault-detection capability loss, because a test detects faults if and only if its execution covers buggy code and its test oracle catches the buggy state. In other words, test oracles may also affect test-suite reduction, However, to our knowledge, their impacts have never been studied before. In this paper, we conduct the first empirical study on such impacts by using 10 real-world GitHub Java projects, and find that assertions (i.e., a typical type of test oracles) are significantly correlated with coverage-based test-suite reduction. Based on our preliminary study results, we also proposed an assertion-aware test-suite reduction technique which outperforms traditional test-suite reduction in terms of cost-effectiveness.
{"title":"How Do Assertions Impact Coverage-Based Test-Suite Reduction?","authors":"Junjie Chen, Y. Bai, Dan Hao, Lingming Zhang, Lu Zhang, Bing Xie","doi":"10.1109/ICST.2017.45","DOIUrl":"https://doi.org/10.1109/ICST.2017.45","url":null,"abstract":"Code coverage is the dominant criterion in test-suite reduction. Typically, most test-suite reduction techniques repeatedly remove tests covering code that has been covered by other tests from the test suite. However, test-suite reduction based on code coverage alone may incur fault-detection capability loss, because a test detects faults if and only if its execution covers buggy code and its test oracle catches the buggy state. In other words, test oracles may also affect test-suite reduction, However, to our knowledge, their impacts have never been studied before. In this paper, we conduct the first empirical study on such impacts by using 10 real-world GitHub Java projects, and find that assertions (i.e., a typical type of test oracles) are significantly correlated with coverage-based test-suite reduction. Based on our preliminary study results, we also proposed an assertion-aware test-suite reduction technique which outperforms traditional test-suite reduction in terms of cost-effectiveness.","PeriodicalId":112258,"journal":{"name":"2017 IEEE International Conference on Software Testing, Verification and Validation (ICST)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128398391","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}
In the automotive industry, as the complexity of electronic control units (ECUs) increase, there is a need for the creation of models that facilitate early tests to ensure functionality, but there is little guidance on how to write these tests in order to achieve maximum coverage. Our prototype CANoe+, which builds on the CANoe and GraphWalker tools, was evaluated against CANoe with regard to coverage maximization of generated test cases from the viewpoint of both software developers and software testers.
{"title":"A Controlled Experiment on Coverage Maximization of Automated Model-Based Software Test Cases in the Automotive Industry","authors":"R. Darwish, Lynnie Nakyanzi Gwosuta, R. Torkar","doi":"10.1109/ICST.2017.65","DOIUrl":"https://doi.org/10.1109/ICST.2017.65","url":null,"abstract":"In the automotive industry, as the complexity of electronic control units (ECUs) increase, there is a need for the creation of models that facilitate early tests to ensure functionality, but there is little guidance on how to write these tests in order to achieve maximum coverage. Our prototype CANoe+, which builds on the CANoe and GraphWalker tools, was evaluated against CANoe with regard to coverage maximization of generated test cases from the viewpoint of both software developers and software testers.","PeriodicalId":112258,"journal":{"name":"2017 IEEE International Conference on Software Testing, Verification and Validation (ICST)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125905979","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}
Benjamin Holland, Ganesh Ram Santhanam, S. Kothari
Immutability analysis is important to software testing, verification and validation (V&V) because it can be used to identify independently testable functions without side-effects. Existing tools for immutability analysis are largely academic prototypes that have not been rigorously tested for accuracy or have not been maintained and are unable to analyze programs written in later versions of Java. In this paper, we re-implement two prominent approaches to inferring the immutability of an object: one that leverages a points-to analysis and another that uses a type-system. In addition to supporting Java 8 source programs, our re-implementations support the analysis of compiled Java bytecode. In order to evaluate the relative accuracy, we create a benchmark that rigorously tests the accuracy boundaries of the respective approaches. We report results of experiments on analyzing the benchmark with the two approaches and compare their scalability to real world applications. Our results from the benchmark reveal that points-to based approach is more accurate than the type inference based approach in certain cases. However, experiments with real world applications show that the points-to based approach does not scale well to very large applications and a type inference based approach may offer a scalable alternative.
{"title":"Transferring State-of-the-Art Immutability Analyses: Experimentation Toolbox and Accuracy Benchmark","authors":"Benjamin Holland, Ganesh Ram Santhanam, S. Kothari","doi":"10.1109/ICST.2017.55","DOIUrl":"https://doi.org/10.1109/ICST.2017.55","url":null,"abstract":"Immutability analysis is important to software testing, verification and validation (V&V) because it can be used to identify independently testable functions without side-effects. Existing tools for immutability analysis are largely academic prototypes that have not been rigorously tested for accuracy or have not been maintained and are unable to analyze programs written in later versions of Java. In this paper, we re-implement two prominent approaches to inferring the immutability of an object: one that leverages a points-to analysis and another that uses a type-system. In addition to supporting Java 8 source programs, our re-implementations support the analysis of compiled Java bytecode. In order to evaluate the relative accuracy, we create a benchmark that rigorously tests the accuracy boundaries of the respective approaches. We report results of experiments on analyzing the benchmark with the two approaches and compare their scalability to real world applications. Our results from the benchmark reveal that points-to based approach is more accurate than the type inference based approach in certain cases. However, experiments with real world applications show that the points-to based approach does not scale well to very large applications and a type inference based approach may offer a scalable alternative.","PeriodicalId":112258,"journal":{"name":"2017 IEEE International Conference on Software Testing, Verification and Validation (ICST)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131075624","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}
Stochastic simulations are developed and employed across many fields, to advise governmental policy decisions and direct future research. Faulty simulation software can have serious consequences, but its correctness is difficult to determine due to complexity and random behaviour. Stochastic simulations may output a different result each time they are run, whereas most testing techniques are designed for programs which (for a given set of inputs) always produce the same behaviour. In this paper, we introduce a new approach towards testing stochastic simulations using statistical oracles and transition probabilities. Our approach was implemented as a toolkit, which allows the frequency of state transitions to be tested, along with their final output distribution. We evaluated our toolkit on eight simulation programs from a variety fields and found it can detect errors at least three times smaller (and in one case, over 1000 times smaller) than a conventional (tolerance threshold) approach.
{"title":"A Toolkit for Testing Stochastic Simulations against Statistical Oracles","authors":"Matthew Patrick, R. Donnelly, C. Gilligan","doi":"10.1109/ICST.2017.50","DOIUrl":"https://doi.org/10.1109/ICST.2017.50","url":null,"abstract":"Stochastic simulations are developed and employed across many fields, to advise governmental policy decisions and direct future research. Faulty simulation software can have serious consequences, but its correctness is difficult to determine due to complexity and random behaviour. Stochastic simulations may output a different result each time they are run, whereas most testing techniques are designed for programs which (for a given set of inputs) always produce the same behaviour. In this paper, we introduce a new approach towards testing stochastic simulations using statistical oracles and transition probabilities. Our approach was implemented as a toolkit, which allows the frequency of state transitions to be tested, along with their final output distribution. We evaluated our toolkit on eight simulation programs from a variety fields and found it can detect errors at least three times smaller (and in one case, over 1000 times smaller) than a conventional (tolerance threshold) approach.","PeriodicalId":112258,"journal":{"name":"2017 IEEE International Conference on Software Testing, Verification and Validation (ICST)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126379502","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}
In mobile application development, the frequentsoftware release limits the testing time resource. In order todetect bugs in early phases, researchers proposed various testcase prioritization (TCP) techniques in past decades. In practice, considering that some test case is described or contains text, theresearchers also employed Natural Language Processing (NLP)to assist the TCP techniques. This paper conducted an extensiveempirical study to analyze the performance of three NLP basedTCP technologies, which is based on 15059 test cases from 30industrial projects. The result shows that all of these threestrategies can help to improve the efficiency of software testing, and the Risk strategy achieved the best performance across thesubject programs.
{"title":"An Industrial Study of Natural Language Processing Based Test Case Prioritization","authors":"Yilin Yang, Xinhai Huang, Xuefei Hao, Zicong Liu, Zhenyu Chen","doi":"10.1109/ICST.2017.66","DOIUrl":"https://doi.org/10.1109/ICST.2017.66","url":null,"abstract":"In mobile application development, the frequentsoftware release limits the testing time resource. In order todetect bugs in early phases, researchers proposed various testcase prioritization (TCP) techniques in past decades. In practice, considering that some test case is described or contains text, theresearchers also employed Natural Language Processing (NLP)to assist the TCP techniques. This paper conducted an extensiveempirical study to analyze the performance of three NLP basedTCP technologies, which is based on 15059 test cases from 30industrial projects. The result shows that all of these threestrategies can help to improve the efficiency of software testing, and the Risk strategy achieved the best performance across thesubject programs.","PeriodicalId":112258,"journal":{"name":"2017 IEEE International Conference on Software Testing, Verification and Validation (ICST)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132345554","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}
Accurate and up-to-date models describing the behavior of software systems are seldom available in practice. To address this issue, software engineers may use specification mining techniques, which can automatically derive models that capture the behavior of the system under analysis. So far, most specification mining techniques focused on the functional behavior of the systems, with specific emphasis on models that represent the ordering of operations, such as temporal rules and finite state models. Although useful, these models are inherently partial. For instance, they miss the timing behavior, which is extremely relevant for many classes of systems and components, such as shared libraries and user-driven applications. Mining specifications that include both the functional and the timing aspects can improve the applicability of many testing and analysis solutions. This paper addresses this challenge by presenting the Timed k-Tail (TkT) specification mining technique that can mine timed automata from program traces. Since timed automata can effectively represent the interplay between the functional and the timing behavior of a system, TkT could be exploited in those contexts where time-related information is relevant. Our empirical evaluation shows that TkT can efficiently and effectively mine accurate models. The mined models have been used to identify executions with anomalous timing. The evaluation shows that most of the anomalous executions have been correctly identified while producing few false positives.
{"title":"Timed k-Tail: Automatic Inference of Timed Automata","authors":"F. Pastore, D. Micucci, L. Mariani","doi":"10.1109/ICST.2017.43","DOIUrl":"https://doi.org/10.1109/ICST.2017.43","url":null,"abstract":"Accurate and up-to-date models describing the behavior of software systems are seldom available in practice. To address this issue, software engineers may use specification mining techniques, which can automatically derive models that capture the behavior of the system under analysis. So far, most specification mining techniques focused on the functional behavior of the systems, with specific emphasis on models that represent the ordering of operations, such as temporal rules and finite state models. Although useful, these models are inherently partial. For instance, they miss the timing behavior, which is extremely relevant for many classes of systems and components, such as shared libraries and user-driven applications. Mining specifications that include both the functional and the timing aspects can improve the applicability of many testing and analysis solutions. This paper addresses this challenge by presenting the Timed k-Tail (TkT) specification mining technique that can mine timed automata from program traces. Since timed automata can effectively represent the interplay between the functional and the timing behavior of a system, TkT could be exploited in those contexts where time-related information is relevant. Our empirical evaluation shows that TkT can efficiently and effectively mine accurate models. The mined models have been used to identify executions with anomalous timing. The evaluation shows that most of the anomalous executions have been correctly identified while producing few false positives.","PeriodicalId":112258,"journal":{"name":"2017 IEEE International Conference on Software Testing, Verification and Validation (ICST)","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116208972","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}
D. Pradhan, Shuai Wang, Shaukat Ali, T. Yue, Marius Liaaen
Multi-objective search algorithms (e.g., non-dominated sorting genetic algorithm II (NSGA-II)) have been frequently applied to address various testing problems requiring multi-objective optimization such as test case selection. However, existing multi-objective search algorithms have certain randomness when selecting parent solutions for producing offspring solutions. In the worse case, suboptimal parent solutions may result in offspring solutions with bad quality, and thus affect the overall quality of the next generation. To address such a challenge, we propose a cluster-based genetic algorithm with elitist selection (CBGA-ES) with the aim to reduce such randomness for supporting multi-objective test optimization. We empirically compared CBGA-ES with random search, greedy (as baselines) and four commonly used multi-objective search algorithms (e.g., NSGA-II) using two industrial and one real world test optimization problem, i.e., test suite minimization, test case prioritization, and test case selection. The results showed that CBGA-ES significantly outperformed the baseline algorithms (e.g., greedy), and the four selected search algorithms for all the three test optimization problems. CBGA-ES managed to outperform more than 75% of the objectives for all the four algorithms in each test optimization problem. Moreover, CBGA-ES was able to improve the quality of the solutions for an average of 32.5% for each objective as compared to the four algorithms for the three test optimization problems.
{"title":"CBGA-ES: A Cluster-Based Genetic Algorithm with Elitist Selection for Supporting Multi-Objective Test Optimization","authors":"D. Pradhan, Shuai Wang, Shaukat Ali, T. Yue, Marius Liaaen","doi":"10.1109/ICST.2017.40","DOIUrl":"https://doi.org/10.1109/ICST.2017.40","url":null,"abstract":"Multi-objective search algorithms (e.g., non-dominated sorting genetic algorithm II (NSGA-II)) have been frequently applied to address various testing problems requiring multi-objective optimization such as test case selection. However, existing multi-objective search algorithms have certain randomness when selecting parent solutions for producing offspring solutions. In the worse case, suboptimal parent solutions may result in offspring solutions with bad quality, and thus affect the overall quality of the next generation. To address such a challenge, we propose a cluster-based genetic algorithm with elitist selection (CBGA-ES) with the aim to reduce such randomness for supporting multi-objective test optimization. We empirically compared CBGA-ES with random search, greedy (as baselines) and four commonly used multi-objective search algorithms (e.g., NSGA-II) using two industrial and one real world test optimization problem, i.e., test suite minimization, test case prioritization, and test case selection. The results showed that CBGA-ES significantly outperformed the baseline algorithms (e.g., greedy), and the four selected search algorithms for all the three test optimization problems. CBGA-ES managed to outperform more than 75% of the objectives for all the four algorithms in each test optimization problem. Moreover, CBGA-ES was able to improve the quality of the solutions for an average of 32.5% for each objective as compared to the four algorithms for the three test optimization problems.","PeriodicalId":112258,"journal":{"name":"2017 IEEE International Conference on Software Testing, Verification and Validation (ICST)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124588481","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}
Linear hybrid automaton is a specification language for hybrid systems. For verification of hybrid systems, it is important to check fairness assumptions. For example, an embedded system keeps running forever when it starts to move by turning on the switch. Such a system has to be checked not only system safety but also fairness and non-Zenoness. The state space explosion is a fundamental problem in model checking, since it is a method that performs an exhaustive search of states. To avoid state space explosion problem in model checking, CEGAR (Counter Example Guided Abstraction Re?nement) is an effective technique. In this paper, we propose transition predicate abstraction and CEGAR verification algorithm for linear hybrid automata.
{"title":"Abstraction Re?nement for Non-Zeno Fairness Veri?cation of Linear Hybrid Automata","authors":"Ryo Yanase","doi":"10.1109/ICST.2017.78","DOIUrl":"https://doi.org/10.1109/ICST.2017.78","url":null,"abstract":"Linear hybrid automaton is a specification language for hybrid systems. For verification of hybrid systems, it is important to check fairness assumptions. For example, an embedded system keeps running forever when it starts to move by turning on the switch. Such a system has to be checked not only system safety but also fairness and non-Zenoness. The state space explosion is a fundamental problem in model checking, since it is a method that performs an exhaustive search of states. To avoid state space explosion problem in model checking, CEGAR (Counter Example Guided Abstraction Re?nement) is an effective technique. In this paper, we propose transition predicate abstraction and CEGAR verification algorithm for linear hybrid automata.","PeriodicalId":112258,"journal":{"name":"2017 IEEE International Conference on Software Testing, Verification and Validation (ICST)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132246448","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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