2022 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)最新文献

Performance Bottleneck Analysis of Drone Computation Offloading to a Shared Fog Node 无人机计算向共享雾节点卸载的性能瓶颈分析

2022 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW) Pub Date : 2022-10-01 DOI: 10.1109/ISSREW55968.2022.00070

Qingyang Zhang, F. Machida, E. Andrade

{"title":"Performance Bottleneck Analysis of Drone Computation Offloading to a Shared Fog Node","authors":"Qingyang Zhang, F. Machida, E. Andrade","doi":"10.1109/ISSREW55968.2022.00070","DOIUrl":"https://doi.org/10.1109/ISSREW55968.2022.00070","url":null,"abstract":"Computing in drones has recently become popular for various real-world applications. To assure the performance and reliability of drone computing, systems can also adopt computation offloading to a nearby fog or edge server through a wireless network. As the offloading performance is significantly affected by the amount of workload, the network stability, and the competing use of a shared resource, performance estimation is essential for such systems. In this paper, we analyze the performance bottleneck of a drone system consisting of multiple drones that offload the tasks to a shared fog node. We investigate how resource conflict due to computation offloading causes the performance bottleneck of the drone computation system. To model the behavior of the system and analyze the performance and availability, we use Stochastic Reward Nets (SRN s). Through the numerical experiments, we confirm that the benefit of computation offloading deteriorates as the number of competing drones increases. To overcome the performance bottleneck, we also discuss potential solutions to mitigate the issue of a shared fog node.","PeriodicalId":178302,"journal":{"name":"2022 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","volume":"78 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":"114377706","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}

引用次数: 2

Machine-Learned Specifications for the Verification and Validation of Autonomous Cyberphysical Systems 自主网络物理系统验证与验证的机器学习规范

2022 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW) Pub Date : 2022-10-01 DOI: 10.1109/ISSREW55968.2022.00089

D. Drusinsky, J. Michael, Matthew Litton

引用次数: 0

Automated Dependability Assessment in DevOps Environments DevOps环境中的自动化可靠性评估

2022 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW) Pub Date : 2022-10-01 DOI: 10.1109/ISSREW55968.2022.00046

James J. Cusick, Alberto Avritzer, Allen Tse, Andrea Janes

引用次数: 0

XAI for Communication Networks XAI代表通信网络

2022 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW) Pub Date : 2022-10-01 DOI: 10.1109/ISSREW55968.2022.00093

Sayan Mukherjee, J. Rupe, J. Zhu

引用次数: 0

Towards Effective Performance Fuzzing 迈向有效的性能模糊测试

2022 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW) Pub Date : 2022-10-01 DOI: 10.1109/ISSREW55968.2022.00055

Yiqun Chen, M. Bradbury, N. Suri

引用次数: 1

WoSAR 2022 Workshop Keynotes WoSAR 2022研讨会主题演讲

2022 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW) Pub Date : 2022-10-01 DOI: 10.1109/issrew55968.2022.00014

引用次数: 0

Towards the Quantitative Verification of Deep Learning for Safe Perception 面向安全感知的深度学习定量验证

2022 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW) Pub Date : 2022-10-01 DOI: 10.1109/ISSREW55968.2022.00069

Philipp Schleiss, Yuki Hagiwara, Iwo Kurzidem, Francesco Carella

{"title":"Towards the Quantitative Verification of Deep Learning for Safe Perception","authors":"Philipp Schleiss, Yuki Hagiwara, Iwo Kurzidem, Francesco Carella","doi":"10.1109/ISSREW55968.2022.00069","DOIUrl":"https://doi.org/10.1109/ISSREW55968.2022.00069","url":null,"abstract":"Deep learning (DL) is seen as an inevitable building block for perceiving the environment with sufficient detail and accuracy as required by automated driving functions. Despite this, its black-box nature and the therewith intertwined unpredictability still hinders its use in safety-critical systems. As such, this work addresses the problem of making this seemingly unpredictable nature measurable by providing a risk-based verification strategy, such as required by ISO 21448. In detail, a method is developed to break down acceptable risk into quantitative performance targets of individual DL-based components along the perception architecture. To verify these targets, the DL input space is split into areas according to the dimensions of a fine-grained operational design domain $(mu mathbf{ODD})$. As it is not feasible to reach full test coverage, the strategy suggests to distribute test efforts across these areas according to the associated risk. Moreover, the testing approach provides answers with respect to how much test coverage and confidence in the test result is required and how these figures relate to safety integrity levels (SILs).","PeriodicalId":178302,"journal":{"name":"2022 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","volume":"9 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":"125968972","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}

引用次数: 3

SSSML 2022 Workshop Committee: ISSREW 2022 SSSML 2022研讨会委员会:ISSREW 2022

2022 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW) Pub Date : 2022-10-01 DOI: 10.1109/issrew55968.2022.00025

引用次数: 0

A Method for Component Evaluation for Live Testing of Cloud Systems 一种面向云系统实时测试的组件评估方法

2022 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW) Pub Date : 2022-10-01 DOI: 10.1109/ISSREW55968.2022.00045

Oussama Jebbar, F. Khendek, M. Toeroe

{"title":"A Method for Component Evaluation for Live Testing of Cloud Systems","authors":"Oussama Jebbar, F. Khendek, M. Toeroe","doi":"10.1109/ISSREW55968.2022.00045","DOIUrl":"https://doi.org/10.1109/ISSREW55968.2022.00045","url":null,"abstract":"Live testing is about testing a subsystem in production without causing any unacceptable disturbance to the production traffic. A subsystem is tested in production for multiple purposes such as deployment verification, fault prediction, fault localization, etc. The main challenge of live testing is alleviating the risk of test interferences as it may lead to a violation of a system's functional or non-functional requirements. To properly handle this risk, one needs to know which components present a risk of test interferences and what is the cost of the countermeasures to handle that risk. Existing literature relies heavily on human judgement, which can be time consuming, not always feasible, may provide misleading insight. In this paper we go through the challenges of automating this evaluation process and propose a solution to overcome them. Our solution consists of a method for components evaluation which goes through three steps, evaluation of test interferences that may manifest in external behaviour, evaluation of test interferences that may manifest in resource consumption, and finally the evaluation of the cost of implementing the countermeasures to overcome the risk of test interferences.","PeriodicalId":178302,"journal":{"name":"2022 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","volume":"19 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":"130949299","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}

引用次数: 0

Correlating Test Events With Monitoring Logs For Test Log Reduction And Anomaly Prediction 将测试事件与监控日志相关联，以减少测试日志并预测异常

2022 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW) Pub Date : 2022-10-01 DOI: 10.1109/ISSREW55968.2022.00079

Bahareh Afshinpour, Roland Groz, Massih-Reza Amini

{"title":"Correlating Test Events With Monitoring Logs For Test Log Reduction And Anomaly Prediction","authors":"Bahareh Afshinpour, Roland Groz, Massih-Reza Amini","doi":"10.1109/ISSREW55968.2022.00079","DOIUrl":"https://doi.org/10.1109/ISSREW55968.2022.00079","url":null,"abstract":"Automated fault identification in long test logs is a tough problem, mainly because of their sequential character and the impossibility of constructing training sets for zero-day faults. To reduce software testers' workload, rule-based approaches have been extensively investigated as solutions for efficiently finding and predicting the fault. Based on software system status monitoring log analysis, we propose a new learning-based technique to automate anomaly detection, correlate test events to anomalies and predict system failures. Since the meaning of fault is not established in system status monitoring-based fault detection, the suggested technique first detects periods of time when a software system status encounters aberrant situations (Bug-Zones). The suggested technique is then tested in a real-time system for anomaly prediction of new tests. The model may be used in two ways. It can assist testers to focus on faulty-like time intervals by reducing the number of test logs. It may also be used to forecast a Bug-Zone in an online system, allowing system administrators to anticipate or even prevent a system failure. An extensive study on a real-world database acquired by a telecommunication operator demonstrates that our approach achieves 71 % accuracy as a Bug-Zones predictor.","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":"128762729","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}

引用次数: 1