Pub Date : 2022-10-01DOI: 10.1109/ISSREW55968.2022.00051
L. Cerný
Systems implementing safety functions are becoming more complex, which is also related to their communication and perception capabilities in an environment. Such systems, primarily seen in mobility, become more susceptible to failures in complex decision-making situations that are difficult to uncover. This paper presents an idea formed in a PhD topic on validating and verifying the system specified by formal logic models. We aim to do so by using automatically generated test scenarios including edge situations (as generalizations of edge cases) invoked by an environment in a simulation tool.
{"title":"Towards automatic validation of composite heterogeneous systems in edge situations","authors":"L. Cerný","doi":"10.1109/ISSREW55968.2022.00051","DOIUrl":"https://doi.org/10.1109/ISSREW55968.2022.00051","url":null,"abstract":"Systems implementing safety functions are becoming more complex, which is also related to their communication and perception capabilities in an environment. Such systems, primarily seen in mobility, become more susceptible to failures in complex decision-making situations that are difficult to uncover. This paper presents an idea formed in a PhD topic on validating and verifying the system specified by formal logic models. We aim to do so by using automatically generated test scenarios including edge situations (as generalizations of edge cases) invoked by an environment in a simulation tool.","PeriodicalId":178302,"journal":{"name":"2022 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127923625","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 : 2022-10-01DOI: 10.1109/ISSREW55968.2022.00077
Bohan Zhang, Yafan Huang, Rachael Chen, Guanpeng Li
This paper proposes D2MON, a data-driven real-time safety monitor, to detect and mitigate safety violations of an autonomous vehicle (AV). The key insight is that traffic situations that lead to AV safety violations fall into patterns and can be identified by learning from existing safety violations. Our approach is to use machine learning techniques to model the traffic behaviors that result in safety violations and detect their symptoms in advance before the actual crashes happen. If D2MoN detects surroundings as dangerous, it will take safety actions to mitigate the safety violations so that the AV remains safe in the evolving traffic environment. Our steps are twofold: (1) We use software fuzzing and data augmentation techniques to generate efficient safety violation data for training our ML model. (2) We deploy the model as a plug-and-play module to the AV software, detecting and mitigating safety violations of the AV in runtime. Our evaluation demonstrates our proposed technique is effective in reducing over 99% of safety violations in an industry-level autonomous driving system, Baidu Apollo.
{"title":"D2MoN: Detecting and Mitigating Real-Time Safety Violations in Autonomous Driving Systems","authors":"Bohan Zhang, Yafan Huang, Rachael Chen, Guanpeng Li","doi":"10.1109/ISSREW55968.2022.00077","DOIUrl":"https://doi.org/10.1109/ISSREW55968.2022.00077","url":null,"abstract":"This paper proposes D2MON, a data-driven real-time safety monitor, to detect and mitigate safety violations of an autonomous vehicle (AV). The key insight is that traffic situations that lead to AV safety violations fall into patterns and can be identified by learning from existing safety violations. Our approach is to use machine learning techniques to model the traffic behaviors that result in safety violations and detect their symptoms in advance before the actual crashes happen. If D2MoN detects surroundings as dangerous, it will take safety actions to mitigate the safety violations so that the AV remains safe in the evolving traffic environment. Our steps are twofold: (1) We use software fuzzing and data augmentation techniques to generate efficient safety violation data for training our ML model. (2) We deploy the model as a plug-and-play module to the AV software, detecting and mitigating safety violations of the AV in runtime. Our evaluation demonstrates our proposed technique is effective in reducing over 99% of safety violations in an industry-level autonomous driving system, Baidu Apollo.","PeriodicalId":178302,"journal":{"name":"2022 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125746889","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 : 2022-10-01DOI: 10.1109/ISSREW55968.2022.00032
Jung-Hoon Kim, Young-Sik Lee
A soft error in flash-based storage might impair a host system. For instance, if the soft error infiltrates the storage mapping function, the host system could experience severe operation failures, such as data corruption or a drive freeze. To harden the storage against soft errors, we propose a novel page-mapping consistency checker (PCK) method implemented with a lightweight redundancy. Our PCK exploits a small page tracing table written previously and only performs mapping-related functions again with the time redundant. Then, with that redundancy result, the storage detects page mapping corruption and finally recovers it. Consequently, the flash-based storage keeps the page-mapping consistency and improves the host system's reliability.
{"title":"A Page-mapping Consistency Protecting Method for Soft Error Damage in Flash-based Storage","authors":"Jung-Hoon Kim, Young-Sik Lee","doi":"10.1109/ISSREW55968.2022.00032","DOIUrl":"https://doi.org/10.1109/ISSREW55968.2022.00032","url":null,"abstract":"A soft error in flash-based storage might impair a host system. For instance, if the soft error infiltrates the storage mapping function, the host system could experience severe operation failures, such as data corruption or a drive freeze. To harden the storage against soft errors, we propose a novel page-mapping consistency checker (PCK) method implemented with a lightweight redundancy. Our PCK exploits a small page tracing table written previously and only performs mapping-related functions again with the time redundant. Then, with that redundancy result, the storage detects page mapping corruption and finally recovers it. Consequently, the flash-based storage keeps the page-mapping consistency and improves the host system's reliability.","PeriodicalId":178302,"journal":{"name":"2022 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133549009","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 : 2022-10-01DOI: 10.1109/ISSREW55968.2022.00076
Tianyu Li, Xiuwen Lu, Hui Xu
This paper studies the problem of automated test case generation for online coding test, i.e., given an input specification in natural language, how can we generate test cases automatically to examine the correctness of the code implemented by the testee? To tackle the problem, this paper proposes an approach that first extracts noun phrases from an input specification; then it removes irrelevant noun phrases and only retains the key phrases related to input construction; by reorganizing these key phrases, it can form an information tree and generate test cases accordingly. We have evaluated our approach with two datasets from LeetCode and ACM and achieved promising results.
{"title":"Automated Test Case Generation from Input Specification in Natural Language","authors":"Tianyu Li, Xiuwen Lu, Hui Xu","doi":"10.1109/ISSREW55968.2022.00076","DOIUrl":"https://doi.org/10.1109/ISSREW55968.2022.00076","url":null,"abstract":"This paper studies the problem of automated test case generation for online coding test, i.e., given an input specification in natural language, how can we generate test cases automatically to examine the correctness of the code implemented by the testee? To tackle the problem, this paper proposes an approach that first extracts noun phrases from an input specification; then it removes irrelevant noun phrases and only retains the key phrases related to input construction; by reorganizing these key phrases, it can form an information tree and generate test cases accordingly. We have evaluated our approach with two datasets from LeetCode and ACM and achieved promising results.","PeriodicalId":178302,"journal":{"name":"2022 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134038850","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 : 2022-10-01DOI: 10.1109/ISSREW55968.2022.00057
Wenxian Zhang, Kazunori Sakamoto, H. Washizaki, Y. Fukazawa
Coverage-guided fuzzing is one of the most effective types of fuzz testing. Code coverage is an important parameter of performance evaluation of the coverage-guided fuzzing tools since normally higher coverage result means a higher chance of fault detection. To expand the overall code covered, based on previous basic block analysis, we propose a method for selecting the mutants of inputs that are able to execute some specific length of the execution path.
{"title":"Improving Fuzzing Coverage with Execution Path Length Selection","authors":"Wenxian Zhang, Kazunori Sakamoto, H. Washizaki, Y. Fukazawa","doi":"10.1109/ISSREW55968.2022.00057","DOIUrl":"https://doi.org/10.1109/ISSREW55968.2022.00057","url":null,"abstract":"Coverage-guided fuzzing is one of the most effective types of fuzz testing. Code coverage is an important parameter of performance evaluation of the coverage-guided fuzzing tools since normally higher coverage result means a higher chance of fault detection. To expand the overall code covered, based on previous basic block analysis, we propose a method for selecting the mutants of inputs that are able to execute some specific length of the execution path.","PeriodicalId":178302,"journal":{"name":"2022 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131067574","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 : 2022-10-01DOI: 10.1109/ISSREW55968.2022.00065
Soichiro Sakamoto, Keita Suzuki, K. Kono
Persistent Memory(PM) has non-volatilability and byte-addressability, and it can be used in many situations due to its high reliability and high performance. However, the persis-tent nature of PM has great impact on “rejuvenation”. Crash consistency bugs, which result in inconsistent data structures inside PM after system crashes, cannot be recovered by restarting the crashed program because the data structures in PM are not initialized with the restarts. Most of existing tools for detecting crash consistency bugs adopt static analysis that can explore a wider range of PM code regions and can detect bugs effectively, but it is hard for these tools to consider all the possible states because of the combinatorial explosion. In addition, PM programs usually have recovery code, which recovers PM data from inconsistent states, hence a crash consistency bug can be recovered to a correct state and it should not be reported as a bug. To simulate the execution of PM programs and detect crash consistency bugs dynamically, we propose PM Crash Injector, the first crash injection tool for PM programs to check the correctness of the recovery code. Like fault injection tools, PM Crash Injector injects system crashes into PM programs to cause crash consistency bugs intentionally. If the recovery code works correctly, inconsistent states in PM will be recovered, but if not, they will be left in PM regions and detected as unexpected behavior the program. PM Crash Injector has found 3 bugs in real-world PM systems and 6 manually inserted bugs in the sample programs of PMDK.
{"title":"Crash Injection to Persistent Memory for Recovery Code Validation","authors":"Soichiro Sakamoto, Keita Suzuki, K. Kono","doi":"10.1109/ISSREW55968.2022.00065","DOIUrl":"https://doi.org/10.1109/ISSREW55968.2022.00065","url":null,"abstract":"Persistent Memory(PM) has non-volatilability and byte-addressability, and it can be used in many situations due to its high reliability and high performance. However, the persis-tent nature of PM has great impact on “rejuvenation”. Crash consistency bugs, which result in inconsistent data structures inside PM after system crashes, cannot be recovered by restarting the crashed program because the data structures in PM are not initialized with the restarts. Most of existing tools for detecting crash consistency bugs adopt static analysis that can explore a wider range of PM code regions and can detect bugs effectively, but it is hard for these tools to consider all the possible states because of the combinatorial explosion. In addition, PM programs usually have recovery code, which recovers PM data from inconsistent states, hence a crash consistency bug can be recovered to a correct state and it should not be reported as a bug. To simulate the execution of PM programs and detect crash consistency bugs dynamically, we propose PM Crash Injector, the first crash injection tool for PM programs to check the correctness of the recovery code. Like fault injection tools, PM Crash Injector injects system crashes into PM programs to cause crash consistency bugs intentionally. If the recovery code works correctly, inconsistent states in PM will be recovered, but if not, they will be left in PM regions and detected as unexpected behavior the program. PM Crash Injector has found 3 bugs in real-world PM systems and 6 manually inserted bugs in the sample programs of PMDK.","PeriodicalId":178302,"journal":{"name":"2022 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120980124","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 : 2022-10-01DOI: 10.1109/ISSREW55968.2022.00048
Diaeddin Rimawi
Cyber-Physical System (CPS) represents systems that join both hardware and software components to perform real-time services. Maintaining the system's reliability is critical to the continuous delivery of these services. However, the CPS running environment is full of uncertainties and can easily lead to performance degradation. As a result, the need for a recovery technique is highly needed to achieve resilience in the system, with keeping in mind that this technique should be as green as possible. This early doctorate proposal, suggests a game theory solution to achieve resilience and green in CPS. Game theory has been known for its fast performance in decision-making, helping the system to choose what maximizes its payoffs. The proposed game model is described over a real-life collaborative artificial intelligence system (CAIS), that involves robots with humans to achieve a common goal. It shows how the expected results of the system will achieve the resilience of CAIS with minimized CO2 footprint.
{"title":"Green Resilience of Cyber-Physical Systems","authors":"Diaeddin Rimawi","doi":"10.1109/ISSREW55968.2022.00048","DOIUrl":"https://doi.org/10.1109/ISSREW55968.2022.00048","url":null,"abstract":"Cyber-Physical System (CPS) represents systems that join both hardware and software components to perform real-time services. Maintaining the system's reliability is critical to the continuous delivery of these services. However, the CPS running environment is full of uncertainties and can easily lead to performance degradation. As a result, the need for a recovery technique is highly needed to achieve resilience in the system, with keeping in mind that this technique should be as green as possible. This early doctorate proposal, suggests a game theory solution to achieve resilience and green in CPS. Game theory has been known for its fast performance in decision-making, helping the system to choose what maximizes its payoffs. The proposed game model is described over a real-life collaborative artificial intelligence system (CAIS), that involves robots with humans to achieve a common goal. It shows how the expected results of the system will achieve the resilience of CAIS with minimized CO2 footprint.","PeriodicalId":178302,"journal":{"name":"2022 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126051573","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 : 2022-10-01DOI: 10.1109/ISSREW55968.2022.00083
Iwo Kurzidem, Adam Misik, Philipp Schleiss, S. Burton
Safety assurance for Machine-Learning (ML) based applications such as object detection is a challenging task due to the black-box nature of many ML methods and the associated uncertainties of its output. To increase evidence in the safe behavior of such ML algorithms an explainable and/or interpretable introspective model can help to investigate the black-box prediction quality. For safety assessment this explainable model should be of reduced complexity and humanly comprehensible, so that any decision regarding safety can be traced back to known and comprehensible factors. We present an approach to create an explainable, introspective model (i.e., white-box) for a deep neural network (i.e., black-box) to determine how safety-relevant input features influence the prediction performance, in particular, for confidence and Bounding Box (BBox) regression. For this, Random Forest (RF) models are trained to predict a YOLOv5 object detector output, for specifically selected safety-relevant input features from the open context environment. The RF predicts the YOLOv5 output reliability for three safety related target variables, namely: softmax score, BBox center shift and BBox size shift. The results indicate that the RF prediction for softmax score are only reliable within certain constrains, while the RF prediction for BBox center/size shift are only reliable for small offsets.
{"title":"Safety Assessment: From Black-Box to White-Box","authors":"Iwo Kurzidem, Adam Misik, Philipp Schleiss, S. Burton","doi":"10.1109/ISSREW55968.2022.00083","DOIUrl":"https://doi.org/10.1109/ISSREW55968.2022.00083","url":null,"abstract":"Safety assurance for Machine-Learning (ML) based applications such as object detection is a challenging task due to the black-box nature of many ML methods and the associated uncertainties of its output. To increase evidence in the safe behavior of such ML algorithms an explainable and/or interpretable introspective model can help to investigate the black-box prediction quality. For safety assessment this explainable model should be of reduced complexity and humanly comprehensible, so that any decision regarding safety can be traced back to known and comprehensible factors. We present an approach to create an explainable, introspective model (i.e., white-box) for a deep neural network (i.e., black-box) to determine how safety-relevant input features influence the prediction performance, in particular, for confidence and Bounding Box (BBox) regression. For this, Random Forest (RF) models are trained to predict a YOLOv5 object detector output, for specifically selected safety-relevant input features from the open context environment. The RF predicts the YOLOv5 output reliability for three safety related target variables, namely: softmax score, BBox center shift and BBox size shift. The results indicate that the RF prediction for softmax score are only reliable within certain constrains, while the RF prediction for BBox center/size shift are only reliable for small offsets.","PeriodicalId":178302,"journal":{"name":"2022 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","volume":"321 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122620015","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 : 2022-10-01DOI: 10.1109/ISSREW55968.2022.00064
Douglas Dias, F. Machida, E. Andrade
Blockchain platforms have gained popularity in recent years and integrated with other digital technologies like Internet of Things (IoT) and Artificial Intelligence (AI) for multiple-business purposes. Software aging is a common issue in many long-running software systems, but little has been experienced in the context of blockchain platforms. To narrow this gap, this work aims to characterize potential software aging issues in the Cardano blockchain platform that is considered the largest cryptocurrency adopting proof-of-stake. By performing statistical analysis on the measurement data of the Cardano blockchain deployed in two environments with different configurations, we found a symptom of software aging through memory degradation that was confirmed by the Mann-Kendall test. By analyzing the running processes, we identify the cardano-node (the main process of the platform) as the process possibly responsible for such degradation.
{"title":"Analysis of Software Aging in a Blockchain Platform","authors":"Douglas Dias, F. Machida, E. Andrade","doi":"10.1109/ISSREW55968.2022.00064","DOIUrl":"https://doi.org/10.1109/ISSREW55968.2022.00064","url":null,"abstract":"Blockchain platforms have gained popularity in recent years and integrated with other digital technologies like Internet of Things (IoT) and Artificial Intelligence (AI) for multiple-business purposes. Software aging is a common issue in many long-running software systems, but little has been experienced in the context of blockchain platforms. To narrow this gap, this work aims to characterize potential software aging issues in the Cardano blockchain platform that is considered the largest cryptocurrency adopting proof-of-stake. By performing statistical analysis on the measurement data of the Cardano blockchain deployed in two environments with different configurations, we found a symptom of software aging through memory degradation that was confirmed by the Mann-Kendall test. By analyzing the running processes, we identify the cardano-node (the main process of the platform) as the process possibly responsible for such degradation.","PeriodicalId":178302,"journal":{"name":"2022 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127017975","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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