Pub Date : 2022-12-01DOI: 10.1109/QRS57517.2022.00112
R. Pietrantuono, Domenico Cotroneo, E. Andrade, F. Machida
Efficient and effective object detection is a key problem in Computer Vision. Numerous object detection algorithms have been developed, whose aim is to achieve two conflicting goals, namely accuracy and efficiency, while being executed in real-time with high robustness. Many of these algorithms must run for an extended period of time, i.e., in video surveillance or in self-driving cars – a working condition that make them subject to the risk of software aging.In this work, we focus on evaluating several object detection algorithms to understand if and to what extent they are affected by software aging. A measurement-based aging approach was adopted, with a series of long-running tests and subsequent data analysis. The results report significant trends of performance degradation, sometimes leading to aging-related failures, as well as memory consumption trends, which turned out to be the main issue across all the experiments.
{"title":"An Empirical Study on Software Aging of Long-Running Object Detection Algorithms","authors":"R. Pietrantuono, Domenico Cotroneo, E. Andrade, F. Machida","doi":"10.1109/QRS57517.2022.00112","DOIUrl":"https://doi.org/10.1109/QRS57517.2022.00112","url":null,"abstract":"Efficient and effective object detection is a key problem in Computer Vision. Numerous object detection algorithms have been developed, whose aim is to achieve two conflicting goals, namely accuracy and efficiency, while being executed in real-time with high robustness. Many of these algorithms must run for an extended period of time, i.e., in video surveillance or in self-driving cars – a working condition that make them subject to the risk of software aging.In this work, we focus on evaluating several object detection algorithms to understand if and to what extent they are affected by software aging. A measurement-based aging approach was adopted, with a series of long-running tests and subsequent data analysis. The results report significant trends of performance degradation, sometimes leading to aging-related failures, as well as memory consumption trends, which turned out to be the main issue across all the experiments.","PeriodicalId":143812,"journal":{"name":"2022 IEEE 22nd International Conference on Software Quality, Reliability and Security (QRS)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123059619","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 : 2022-12-01DOI: 10.1109/QRS57517.2022.00016
Chen Qian, Tianyuan Hu, Bixin Li
Smart contracts are applications running on the blockchain which control many virtual currencies. Since smart contracts are composed of code, they inevitably have defects. In recent years, many smart contract defects have caused lots of economic losses and harmful impacts. A contract that has defects may have some errors that cause unwanted results. As smart contracts cannot be modified once deployed, it is necessary to ensure that they are free from defects. In this paper, we focus on eleven defects of smart contracts and construct a deep learning-based model to detect these contract defects more accurately. Our model regards the smart contract’s operation codes as a sequential sentence and uses an Attention-based bidirectional long short term memory (BiLSTM-Attention) model to find smart contract defects. We evaluate our model’s and other models’ performance on 45622 real-world smart contracts. The experimental results show that our model can achieve higher accuracy (95.40%) and F1-score (95.38%). In addition, our model is highly efficient and can quickly detect large numbers of contracts.
{"title":"A BiLSTM-Attention Model for Detecting Smart Contract Defects More Accurately","authors":"Chen Qian, Tianyuan Hu, Bixin Li","doi":"10.1109/QRS57517.2022.00016","DOIUrl":"https://doi.org/10.1109/QRS57517.2022.00016","url":null,"abstract":"Smart contracts are applications running on the blockchain which control many virtual currencies. Since smart contracts are composed of code, they inevitably have defects. In recent years, many smart contract defects have caused lots of economic losses and harmful impacts. A contract that has defects may have some errors that cause unwanted results. As smart contracts cannot be modified once deployed, it is necessary to ensure that they are free from defects. In this paper, we focus on eleven defects of smart contracts and construct a deep learning-based model to detect these contract defects more accurately. Our model regards the smart contract’s operation codes as a sequential sentence and uses an Attention-based bidirectional long short term memory (BiLSTM-Attention) model to find smart contract defects. We evaluate our model’s and other models’ performance on 45622 real-world smart contracts. The experimental results show that our model can achieve higher accuracy (95.40%) and F1-score (95.38%). In addition, our model is highly efficient and can quickly detect large numbers of contracts.","PeriodicalId":143812,"journal":{"name":"2022 IEEE 22nd International Conference on Software Quality, Reliability and Security (QRS)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125930606","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 : 2022-12-01DOI: 10.1109/QRS57517.2022.00058
Xueyong Sun, Qihao Bao, Bixin Li
Proof of Work (PoW) is the most widely used consensus protocol. However, due to the hash rate competition mechanism, longest chain principle, and transaction fee mechanism of the PoW consensus protocol, malicious nodes can launch attacks to obtain more relative revenue than honest mining, which will discourage honest miners from packing transactions into blocks and verifying blocks. As a result, the speed of the nodes reaching consensus in the network is slowed down, or even consensus cannot be reached, which ultimately affects the security of the PoW consensus protocol.In this paper, the Markov Decision Process (MDP) is used to simulate the whale attack launched by malicious nodes, and evaluate the capability of PoW consensus protocol against the whale attack. The experimental results show that the PoW consensus protocol is secure in the Bitcoin network when the transaction fee is set in the range of 0.002-0.3 block rewards and the transaction volume should not exceed 21.09 block rewards. In addition, the PoW consensus protocol will be more secure with the adjustment of parameters such as the number of block confirmations, block generation interval and block size.
{"title":"Can PoW Consensus Protocol Resist the Whale Attack?","authors":"Xueyong Sun, Qihao Bao, Bixin Li","doi":"10.1109/QRS57517.2022.00058","DOIUrl":"https://doi.org/10.1109/QRS57517.2022.00058","url":null,"abstract":"Proof of Work (PoW) is the most widely used consensus protocol. However, due to the hash rate competition mechanism, longest chain principle, and transaction fee mechanism of the PoW consensus protocol, malicious nodes can launch attacks to obtain more relative revenue than honest mining, which will discourage honest miners from packing transactions into blocks and verifying blocks. As a result, the speed of the nodes reaching consensus in the network is slowed down, or even consensus cannot be reached, which ultimately affects the security of the PoW consensus protocol.In this paper, the Markov Decision Process (MDP) is used to simulate the whale attack launched by malicious nodes, and evaluate the capability of PoW consensus protocol against the whale attack. The experimental results show that the PoW consensus protocol is secure in the Bitcoin network when the transaction fee is set in the range of 0.002-0.3 block rewards and the transaction volume should not exceed 21.09 block rewards. In addition, the PoW consensus protocol will be more secure with the adjustment of parameters such as the number of block confirmations, block generation interval and block size.","PeriodicalId":143812,"journal":{"name":"2022 IEEE 22nd International Conference on Software Quality, Reliability and Security (QRS)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126550042","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 : 2022-12-01DOI: 10.1109/QRS57517.2022.00096
D. Reichelt, Stefan Kühne, W. Hasselbring
To develop software with optimal performance, even small performance changes need to be identified. Identifying performance changes is challenging since the performance of software is influenced by non-deterministic factors. Therefore, not every performance change is measurable with reasonable effort. In this work, we discuss which performance changes are measurable at code level with reasonable measurement effort and how to identify them. We present (1) an analysis of the boundaries of measuring performance changes, (2) an approach for determining a configuration for reproducible performance change identification, and (3) an evaluation comparing of how well our approach is able to identify performance changes in the application server Jetty compared with the usage of Jetty’s own performance regression benchmarks.Thereby, we find (1) that small performance differences are only measurable by fine-grained measurement workloads, (2) that performance changes caused by the change of one operation can be identified using a unit-test-sized workload definition and a suitable configuration, and (3) that using our approach identifies small performance regressions more efficiently than using Jetty’s performance regression benchmarks.
{"title":"Automated Identification of Performance Changes at Code Level","authors":"D. Reichelt, Stefan Kühne, W. Hasselbring","doi":"10.1109/QRS57517.2022.00096","DOIUrl":"https://doi.org/10.1109/QRS57517.2022.00096","url":null,"abstract":"To develop software with optimal performance, even small performance changes need to be identified. Identifying performance changes is challenging since the performance of software is influenced by non-deterministic factors. Therefore, not every performance change is measurable with reasonable effort. In this work, we discuss which performance changes are measurable at code level with reasonable measurement effort and how to identify them. We present (1) an analysis of the boundaries of measuring performance changes, (2) an approach for determining a configuration for reproducible performance change identification, and (3) an evaluation comparing of how well our approach is able to identify performance changes in the application server Jetty compared with the usage of Jetty’s own performance regression benchmarks.Thereby, we find (1) that small performance differences are only measurable by fine-grained measurement workloads, (2) that performance changes caused by the change of one operation can be identified using a unit-test-sized workload definition and a suitable configuration, and (3) that using our approach identifies small performance regressions more efficiently than using Jetty’s performance regression benchmarks.","PeriodicalId":143812,"journal":{"name":"2022 IEEE 22nd International Conference on Software Quality, Reliability and Security (QRS)","volume":"199 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122530498","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 : 2022-12-01DOI: 10.1109/QRS57517.2022.00070
Luca Giamattei, Antonio Guerriero, R. Pietrantuono, S. Russo
Microservices Architectures (MSA) have found large adoption in companies delivering online services, often in conjunction with agile development practices. Microservices are distributed, independent and polyglot entities – all features favouring black-box testing. However, for real-scale MSA, a pure black-box strategy may not be able to exercise the system to properly cover the interactions involving internal microservices.We propose a grey-box strategy (MACROHIVE) for automated testing and monitoring of (internal) microservices interactions. It uses combinatorial testing to generate valid and invalid tests from microservices specification. Tests execution and monitoring are automated by a service mesh infrastructure. MACROHIVE runs the tests and traces the interactions among microservices, to report about internal coverage and failing behaviour.MACROHIVE is experimented on TrainTicket, an open-source MSA benchmark. It performs comparably to state-of-the-art techniques in terms of edge-level coverage, but exposes internal failures undetected by black-box testing, gives detailed internal coverage information, and requires fewer tests.
{"title":"Automated Grey-Box Testing of Microservice Architectures","authors":"Luca Giamattei, Antonio Guerriero, R. Pietrantuono, S. Russo","doi":"10.1109/QRS57517.2022.00070","DOIUrl":"https://doi.org/10.1109/QRS57517.2022.00070","url":null,"abstract":"Microservices Architectures (MSA) have found large adoption in companies delivering online services, often in conjunction with agile development practices. Microservices are distributed, independent and polyglot entities – all features favouring black-box testing. However, for real-scale MSA, a pure black-box strategy may not be able to exercise the system to properly cover the interactions involving internal microservices.We propose a grey-box strategy (MACROHIVE) for automated testing and monitoring of (internal) microservices interactions. It uses combinatorial testing to generate valid and invalid tests from microservices specification. Tests execution and monitoring are automated by a service mesh infrastructure. MACROHIVE runs the tests and traces the interactions among microservices, to report about internal coverage and failing behaviour.MACROHIVE is experimented on TrainTicket, an open-source MSA benchmark. It performs comparably to state-of-the-art techniques in terms of edge-level coverage, but exposes internal failures undetected by black-box testing, gives detailed internal coverage information, and requires fewer tests.","PeriodicalId":143812,"journal":{"name":"2022 IEEE 22nd International Conference on Software Quality, Reliability and Security (QRS)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125362759","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 : 2022-12-01DOI: 10.1109/QRS57517.2022.00090
Runbing Qin, Ningjiang Chen, Yihui Huang
Defect detection aims to locate the accurate position of defects in images, which is of great significance to quality inspection in the industrial product manufacturing. Currently, many defect detection methods rely on deep neural networks to extract features. Although the accuracy of these methods is relatively high, it is computationally intensive, making the methods difficult to deploy in resource-limited edge devices. In order to solve these problems, a lightweight defect detection model for the industrial edge environment is proposed, termed the efficient defect detection network (EDDNet). EfficientNet-B0 is used as the feature extraction backbone, extracting feature maps from feature layers of different depths of the network and fusing multilevel features by multilevel feature fusion (MFF). To obtain more information, we redesign the attention mechanism in MBConv blocks, taking the encoding space (ES) attention mechanism as a new module, which solves the problem that the defective image spatial information is ignored. The experimental results on the NEU-DET and DAGM2007 datasets and PCB defect datasets demonstrate the effectiveness of the proposed EDDNet and its possibility for application in industrial edge device.
{"title":"EDDNet: An Efficient and Accurate Defect Detection Network for the Industrial Edge Environment","authors":"Runbing Qin, Ningjiang Chen, Yihui Huang","doi":"10.1109/QRS57517.2022.00090","DOIUrl":"https://doi.org/10.1109/QRS57517.2022.00090","url":null,"abstract":"Defect detection aims to locate the accurate position of defects in images, which is of great significance to quality inspection in the industrial product manufacturing. Currently, many defect detection methods rely on deep neural networks to extract features. Although the accuracy of these methods is relatively high, it is computationally intensive, making the methods difficult to deploy in resource-limited edge devices. In order to solve these problems, a lightweight defect detection model for the industrial edge environment is proposed, termed the efficient defect detection network (EDDNet). EfficientNet-B0 is used as the feature extraction backbone, extracting feature maps from feature layers of different depths of the network and fusing multilevel features by multilevel feature fusion (MFF). To obtain more information, we redesign the attention mechanism in MBConv blocks, taking the encoding space (ES) attention mechanism as a new module, which solves the problem that the defective image spatial information is ignored. The experimental results on the NEU-DET and DAGM2007 datasets and PCB defect datasets demonstrate the effectiveness of the proposed EDDNet and its possibility for application in industrial edge device.","PeriodicalId":143812,"journal":{"name":"2022 IEEE 22nd International Conference on Software Quality, Reliability and Security (QRS)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125967042","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 : 2022-12-01DOI: 10.1109/QRS57517.2022.00026
Tao Wang, Chuanqi Tao, Hongjing Guo, Lijin Tang
Software defect prediction(SDP), which predicts defective code areas, including files, code blocks, code lines, etc. It can help developers or testers in allocating test resources before the testing phase. Software defect number prediction(SDNP) is an important research direction of SDP. Previous studies mostly used regression-based methods or different neural networks to mine the semantic features contained in AST, but the way to represent code was relatively simple. In this article, we propose a framework for representing the semantic features in terms of sequences of nodes with a double sequence structure, by analyzing the ASTs and the changes in the code blocks between adjacent version. In addition, to combine statistical metric information, we also propose a model that dynamically determines the ratio of semantic features to traditional metric features during model training by using the gated fusion mechanism to perform SDNP. In the experimental part, we select 10 open source Java projects as training and test sets, and conduct a lot of comparative experiments. The experimental results demonstrate the superiority of our proposed method compared to the baseline approach.
{"title":"Semantic Feature Learning based on Double Sequences Structure for Software Defect Number Prediction","authors":"Tao Wang, Chuanqi Tao, Hongjing Guo, Lijin Tang","doi":"10.1109/QRS57517.2022.00026","DOIUrl":"https://doi.org/10.1109/QRS57517.2022.00026","url":null,"abstract":"Software defect prediction(SDP), which predicts defective code areas, including files, code blocks, code lines, etc. It can help developers or testers in allocating test resources before the testing phase. Software defect number prediction(SDNP) is an important research direction of SDP. Previous studies mostly used regression-based methods or different neural networks to mine the semantic features contained in AST, but the way to represent code was relatively simple. In this article, we propose a framework for representing the semantic features in terms of sequences of nodes with a double sequence structure, by analyzing the ASTs and the changes in the code blocks between adjacent version. In addition, to combine statistical metric information, we also propose a model that dynamically determines the ratio of semantic features to traditional metric features during model training by using the gated fusion mechanism to perform SDNP. In the experimental part, we select 10 open source Java projects as training and test sets, and conduct a lot of comparative experiments. The experimental results demonstrate the superiority of our proposed method compared to the baseline approach.","PeriodicalId":143812,"journal":{"name":"2022 IEEE 22nd International Conference on Software Quality, Reliability and Security (QRS)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128660103","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 : 2022-12-01DOI: 10.1109/QRS57517.2022.00014
Kedrian James, Yufei Du, Sanjeev Das, F. Monrose
Bug triaging entails a laborious process wherein triagers spend time examining new bug reports, localizing the bugs, and assigning them to the appropriate developer(s) to fix the bugs. In recent years, the adoption of automated software testing techniques (e.g., fuzzing) further complicates the process because bug hunters can submit an overwhelming number of reports in a short period. To lessen these pain points, we present an approach that extracts a fingerprint from crash information within a bug report, and returns a group of bugs with similar behaviors. Our approach uses symptoms of the crash to create a robust fingerprint, and leverages MinHashing and Locality Sensitive Hashing to match crashes, as well as a sequential pattern mining algorithm to find frequent closed sequences among bugs. Our evaluation shows that our approach outperforms contemporary approaches (e.g., finding previously unknown duplicates among 81 CVEs), and saves triagers time and effort.
{"title":"Separating the Wheat from the Chaff: Using Indexing and Sub-Sequence Mining Techniques to Identify Related Crashes During Bug Triage","authors":"Kedrian James, Yufei Du, Sanjeev Das, F. Monrose","doi":"10.1109/QRS57517.2022.00014","DOIUrl":"https://doi.org/10.1109/QRS57517.2022.00014","url":null,"abstract":"Bug triaging entails a laborious process wherein triagers spend time examining new bug reports, localizing the bugs, and assigning them to the appropriate developer(s) to fix the bugs. In recent years, the adoption of automated software testing techniques (e.g., fuzzing) further complicates the process because bug hunters can submit an overwhelming number of reports in a short period. To lessen these pain points, we present an approach that extracts a fingerprint from crash information within a bug report, and returns a group of bugs with similar behaviors. Our approach uses symptoms of the crash to create a robust fingerprint, and leverages MinHashing and Locality Sensitive Hashing to match crashes, as well as a sequential pattern mining algorithm to find frequent closed sequences among bugs. Our evaluation shows that our approach outperforms contemporary approaches (e.g., finding previously unknown duplicates among 81 CVEs), and saves triagers time and effort.","PeriodicalId":143812,"journal":{"name":"2022 IEEE 22nd International Conference on Software Quality, Reliability and Security (QRS)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133617526","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 : 2022-12-01DOI: 10.1109/QRS57517.2022.00073
G. Carvalho, N. Medeiros, H. Madeira, Bruno Cabral
The damage or destruction of Critical Infrastructures (CIs) affect societies’ sustainable functioning. Therefore, it is crucial to have effective methods to assess the risk and resilience of CIs. Failure Mode and Effects Analysis (FMEA) and Failure Mode Effects and Criticality Analysis (FMECA) are two approaches to risk assessment and criticality analysis. However, these approaches are complex to apply to intricate CIs and associated Cyber-Physical Systems (CPS). We provide a top-down strategy, starting from a high abstraction level of the system and progressing to cover the functional elements of the infrastructures. This approach develops from FMECA but estimates risks and focuses on assessing resilience. We applied the proposed technique to a real-world CI, predicting how possible improvement scenarios may influence the overall system resilience. The results show the effectiveness of our approach in benchmarking the CI resilience, providing a cost-effective way to evaluate plausible alternatives concerning the improvement of preventive measures.
{"title":"A Functional FMECA Approach for the Assessment of Critical Infrastructure Resilience","authors":"G. Carvalho, N. Medeiros, H. Madeira, Bruno Cabral","doi":"10.1109/QRS57517.2022.00073","DOIUrl":"https://doi.org/10.1109/QRS57517.2022.00073","url":null,"abstract":"The damage or destruction of Critical Infrastructures (CIs) affect societies’ sustainable functioning. Therefore, it is crucial to have effective methods to assess the risk and resilience of CIs. Failure Mode and Effects Analysis (FMEA) and Failure Mode Effects and Criticality Analysis (FMECA) are two approaches to risk assessment and criticality analysis. However, these approaches are complex to apply to intricate CIs and associated Cyber-Physical Systems (CPS). We provide a top-down strategy, starting from a high abstraction level of the system and progressing to cover the functional elements of the infrastructures. This approach develops from FMECA but estimates risks and focuses on assessing resilience. We applied the proposed technique to a real-world CI, predicting how possible improvement scenarios may influence the overall system resilience. The results show the effectiveness of our approach in benchmarking the CI resilience, providing a cost-effective way to evaluate plausible alternatives concerning the improvement of preventive measures.","PeriodicalId":143812,"journal":{"name":"2022 IEEE 22nd International Conference on Software Quality, Reliability and Security (QRS)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132318858","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 : 2022-12-01DOI: 10.1109/QRS57517.2022.00067
Yezhou Liu, Jing Sun, R. Nicolescu, Hai H. Wang
As a newly proposed computational paradigm of membrane computing, cP systems are used to solve several NP-complete and PSPACE-complete problems in linear or sub-linear time theoretically. Most cP systems proposed in previous studies lack of automated verification support. In this paper, we present cPV, the first software implementation for cP system simulation and verification. cPV offers multiple features, which include modelling, simulation, automated verification of properties such as absence of deadlock, confluence, termination, determinism, and goal reachability. As an extensible framework, modules in cPV are loosely coupled, where new verification algorithms, reduction techniques, and property specifications can be easily extended. To evaluate cPV, we constructed two benchmark datasets that cover several important aspects of cP systems. The experimental results demonstrated effective automatic verification support to the membrane computing problem domain.
{"title":"cPV – Simulation and Verification for Membrane Computing","authors":"Yezhou Liu, Jing Sun, R. Nicolescu, Hai H. Wang","doi":"10.1109/QRS57517.2022.00067","DOIUrl":"https://doi.org/10.1109/QRS57517.2022.00067","url":null,"abstract":"As a newly proposed computational paradigm of membrane computing, cP systems are used to solve several NP-complete and PSPACE-complete problems in linear or sub-linear time theoretically. Most cP systems proposed in previous studies lack of automated verification support. In this paper, we present cPV, the first software implementation for cP system simulation and verification. cPV offers multiple features, which include modelling, simulation, automated verification of properties such as absence of deadlock, confluence, termination, determinism, and goal reachability. As an extensible framework, modules in cPV are loosely coupled, where new verification algorithms, reduction techniques, and property specifications can be easily extended. To evaluate cPV, we constructed two benchmark datasets that cover several important aspects of cP systems. The experimental results demonstrated effective automatic verification support to the membrane computing problem domain.","PeriodicalId":143812,"journal":{"name":"2022 IEEE 22nd International Conference on Software Quality, Reliability and Security (QRS)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130985327","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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