Pub Date : 2022-10-01DOI: 10.1109/ISSREW55968.2022.00059
Sebastian Frank, M. A. Hakamian, Lion Wagner, J. V. Kistowski, A. Hoorn
Microservice-based software systems aim to be re-silient to changes, which lead to transient behavior. Precisely specifying resilience requirements is challenging, as transient behavior is complex and subject to uncertainty. We envision a process of continuous resilience requirement specification and verification at runtime, which assists software architects in understanding their system and continuously improves the quality and quantity of the specified resilience requirements. The envisioned approach uses specifications in easy-to-use formats like scenarios and property specification patterns, which can be automatically verified based on various data sources, i.e., monitoring, simulation, and chaos experiments. Furthermore, it provides suggestions for improving requirements through visualization and interaction. Our preliminary results consist of several tools, e.g., the resilience simulator MiSim and Resirio for elicitation and specification of initial resilience scenarios.
{"title":"Towards Continuous and Data-driven Specification and Verification of Resilience Scenarios","authors":"Sebastian Frank, M. A. Hakamian, Lion Wagner, J. V. Kistowski, A. Hoorn","doi":"10.1109/ISSREW55968.2022.00059","DOIUrl":"https://doi.org/10.1109/ISSREW55968.2022.00059","url":null,"abstract":"Microservice-based software systems aim to be re-silient to changes, which lead to transient behavior. Precisely specifying resilience requirements is challenging, as transient behavior is complex and subject to uncertainty. We envision a process of continuous resilience requirement specification and verification at runtime, which assists software architects in understanding their system and continuously improves the quality and quantity of the specified resilience requirements. The envisioned approach uses specifications in easy-to-use formats like scenarios and property specification patterns, which can be automatically verified based on various data sources, i.e., monitoring, simulation, and chaos experiments. Furthermore, it provides suggestions for improving requirements through visualization and interaction. Our preliminary results consist of several tools, e.g., the resilience simulator MiSim and Resirio for elicitation and specification of initial resilience scenarios.","PeriodicalId":178302,"journal":{"name":"2022 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","volume":"46 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":"123135440","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/ISSREW55968.2022.00080
Wanjin Zhou, Feifei Hu, Junyan Ma
A dynamic reconfigurable embedded system runtime verification framework Hat-RV based on hardware-assisted tracing is proposed for resource-constrained embedded systems. Hardware-assisted tracing reduces the overhead of obtaining detailed program execution information. Hat-RV reconstructs the program trajectory through real-time online analysis of trace data to further support runtime verification. At the same time, taking advantage of the PYNQ architecture, the overall framework of Hat-RV is abstracted into an Overlay, where the monitor modules can be dynamically loaded to change the properties of the verification at runtime. The user can achieve the monitoring loading and Overlay mapping simply through the Python interface, thereby increasing the flexibility of runtime verification of embedded systems.
{"title":"Improving Flexibility in Embedded System Runtime Verification with Python","authors":"Wanjin Zhou, Feifei Hu, Junyan Ma","doi":"10.1109/ISSREW55968.2022.00080","DOIUrl":"https://doi.org/10.1109/ISSREW55968.2022.00080","url":null,"abstract":"A dynamic reconfigurable embedded system runtime verification framework Hat-RV based on hardware-assisted tracing is proposed for resource-constrained embedded systems. Hardware-assisted tracing reduces the overhead of obtaining detailed program execution information. Hat-RV reconstructs the program trajectory through real-time online analysis of trace data to further support runtime verification. At the same time, taking advantage of the PYNQ architecture, the overall framework of Hat-RV is abstracted into an Overlay, where the monitor modules can be dynamically loaded to change the properties of the verification at runtime. The user can achieve the monitoring loading and Overlay mapping simply through the Python interface, thereby increasing the flexibility of runtime verification of embedded systems.","PeriodicalId":178302,"journal":{"name":"2022 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","volume":"27 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":"131998642","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/ISSREW55968.2022.00053
Yintong Huo, Yuxin Su, Michael R. Lyu
Modern automated log analytics rely on log events without paying attention to variables. However, variables, such as the return code (e.g., “404”) in logs, are noteworthy for their specific semantics of system running status. To unlock the critical bottleneck of mining such semantics from log messages, this study proposes LogVM with three components: (1) an encoder to capture the context information; (2) a pair matcher to resolve variable semantics; and (3) a word scorer to disambiguate different semantic roles. The experiments over seven widely-used software systems demonstrate that Log Vm can derive rich semantics from log messages. We believe such uncovered variable semantics can facilitate downstream applications for system maintainers.
{"title":"LogVm: Variable Semantics Miner for Log Messages","authors":"Yintong Huo, Yuxin Su, Michael R. Lyu","doi":"10.1109/ISSREW55968.2022.00053","DOIUrl":"https://doi.org/10.1109/ISSREW55968.2022.00053","url":null,"abstract":"Modern automated log analytics rely on log events without paying attention to variables. However, variables, such as the return code (e.g., “404”) in logs, are noteworthy for their specific semantics of system running status. To unlock the critical bottleneck of mining such semantics from log messages, this study proposes LogVM with three components: (1) an encoder to capture the context information; (2) a pair matcher to resolve variable semantics; and (3) a word scorer to disambiguate different semantic roles. The experiments over seven widely-used software systems demonstrate that Log Vm can derive rich semantics from log messages. We believe such uncovered variable semantics can facilitate downstream applications for system maintainers.","PeriodicalId":178302,"journal":{"name":"2022 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","volume":"343 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":"132305670","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/ISSREW55968.2022.00036
J. Willenbring, G. Walia
Software sustainability is critical for Computational Science and Engineering (CSE) software. Measuring sustainability is challenging because sustainability consists of many attributes. One factor that impacts software sustainability is the complexity of the source code. This paper introduces an approach for utilizing complexity data, with a focus on hotspots of and changes in complexity, to assist developers in performing code reviews and inform project teams about longer-term changes in sustainability and maintainability from the perspective of cyclomatic complexity. We present an analysis of data associated with four real-world pull requests to demonstrate how the metrics may help guide and inform the code review process and how the data can be used to measure changes in complexity over time.
{"title":"Using Complexity Metrics with Hotspot Analysis to Support Software Sustainability","authors":"J. Willenbring, G. Walia","doi":"10.1109/ISSREW55968.2022.00036","DOIUrl":"https://doi.org/10.1109/ISSREW55968.2022.00036","url":null,"abstract":"Software sustainability is critical for Computational Science and Engineering (CSE) software. Measuring sustainability is challenging because sustainability consists of many attributes. One factor that impacts software sustainability is the complexity of the source code. This paper introduces an approach for utilizing complexity data, with a focus on hotspots of and changes in complexity, to assist developers in performing code reviews and inform project teams about longer-term changes in sustainability and maintainability from the perspective of cyclomatic complexity. We present an analysis of data associated with four real-world pull requests to demonstrate how the metrics may help guide and inform the code review process and how the data can be used to measure changes in complexity over time.","PeriodicalId":178302,"journal":{"name":"2022 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","volume":"1 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":"114139031","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/ISSREW55968.2022.00037
K. Okumoto
Predicting the number of defects in software at release is a critical need for quality managers to evaluate the readiness to deliver high-quality software. Even though this is a well-studied subject, it continues to be challenging in large-scale projects. This is particularly so during early stages of the development process when no defect data is available. This paper proposes a novel approach for defect prediction in early stages of development. It utilises a software development and testing plan, and also learns from previous releases of the same project to predict defects. By producing key quality metrics such as percentage residual defects and percentage open defects at delivery, we enable decisions regarding the readiness of a software product for delivery. Over several years, the approach has been successfully applied to large-scale software products, which has helped to evaluate the stability and accuracy of defects predicted at delivery over time.
{"title":"Early Software Defect Prediction: Right-Shifting Software Effort Data into a Defect Curve","authors":"K. Okumoto","doi":"10.1109/ISSREW55968.2022.00037","DOIUrl":"https://doi.org/10.1109/ISSREW55968.2022.00037","url":null,"abstract":"Predicting the number of defects in software at release is a critical need for quality managers to evaluate the readiness to deliver high-quality software. Even though this is a well-studied subject, it continues to be challenging in large-scale projects. This is particularly so during early stages of the development process when no defect data is available. This paper proposes a novel approach for defect prediction in early stages of development. It utilises a software development and testing plan, and also learns from previous releases of the same project to predict defects. By producing key quality metrics such as percentage residual defects and percentage open defects at delivery, we enable decisions regarding the readiness of a software product for delivery. Over several years, the approach has been successfully applied to large-scale software products, which has helped to evaluate the stability and accuracy of defects predicted at delivery over time.","PeriodicalId":178302,"journal":{"name":"2022 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","volume":"7 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":"115156417","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/ISSREW55968.2022.00088
Alwyn E. Goodloe
Machine learning is increasingly being used in safety-critical systems, where the public safety requires a rigorous assurance process. We shall outline how assurance processes work for conventional systems and identify the primary difficulty in applying them to machine learning enabled systems. We will then outline a path forward including identifying where considerable basic research remains.
{"title":"Assuring Safety-Critical Machine Learning Enabled Systems: Challenges and Promise","authors":"Alwyn E. Goodloe","doi":"10.1109/ISSREW55968.2022.00088","DOIUrl":"https://doi.org/10.1109/ISSREW55968.2022.00088","url":null,"abstract":"Machine learning is increasingly being used in safety-critical systems, where the public safety requires a rigorous assurance process. We shall outline how assurance processes work for conventional systems and identify the primary difficulty in applying them to machine learning enabled systems. We will then outline a path forward including identifying where considerable basic research remains.","PeriodicalId":178302,"journal":{"name":"2022 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","volume":"34 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":"131888575","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/issrew55968.2022.00096
Boyi Hu, Yue Luo, Yuhao Chen
The overarching goal of this work is to understand how human locomotion adapts to mobile collaborative robots (cobots) that are designed to complement human well-being. This understanding will provide relevant inherent safe and human-centered design guidance for future mobile cobot systems. In this study, we will focus on the warehousing, wholesale, and retail trade (WRT) industry, where in general human workers are exposed to extensive experience working with mobile cobots, investigating the human locomotion safety in this environment. Eight participants were recruited to simulate a grocery shopping task with and without the mobile robot nearby. The walking trajectory of all participants revealed that the mobile robot complicated participants walking path selection, compared to the baseline “No Robot” condition. Meanwhile, participants lowered their walking speed and showed a proactive reaction to the approaching robot by initiating and ceasing the walking actions more smoothly. In conclusion, findings confirmed the values of mobile cobots in complex occupational settings and suggested more a systematic approach to ensure these intelligent systems' inherent safety.
{"title":"Evaluating Human Locomotion Safety in Mobile Robots Populated Environments","authors":"Boyi Hu, Yue Luo, Yuhao Chen","doi":"10.1109/issrew55968.2022.00096","DOIUrl":"https://doi.org/10.1109/issrew55968.2022.00096","url":null,"abstract":"The overarching goal of this work is to understand how human locomotion adapts to mobile collaborative robots (cobots) that are designed to complement human well-being. This understanding will provide relevant inherent safe and human-centered design guidance for future mobile cobot systems. In this study, we will focus on the warehousing, wholesale, and retail trade (WRT) industry, where in general human workers are exposed to extensive experience working with mobile cobots, investigating the human locomotion safety in this environment. Eight participants were recruited to simulate a grocery shopping task with and without the mobile robot nearby. The walking trajectory of all participants revealed that the mobile robot complicated participants walking path selection, compared to the baseline “No Robot” condition. Meanwhile, participants lowered their walking speed and showed a proactive reaction to the approaching robot by initiating and ceasing the walking actions more smoothly. In conclusion, findings confirmed the values of mobile cobots in complex occupational settings and suggested more a systematic approach to ensure these intelligent systems' inherent safety.","PeriodicalId":178302,"journal":{"name":"2022 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","volume":"28 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":"114283317","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/ISSREW55968.2022.00052
Mengyuan Hou, Hui Xu
Deep learning is a powerful technique for many real- world problems. However, due to its unexplainable characteristic and over-fitting issue, there remains a great challenge for building reliable system with deep learning modules. In this paper, we present the idea of LegoAI that aims to build reliable AI software with pluggable modules of different functionalities, such as ensemble for fault tolerance and anomaly detection for result validation. In particular, we have applied the idea to develop a real-world AI software for handwritten digit recognition and achieved promising results.
{"title":"LegoAI: Towards Building Reliable AI Software for Real-world Applications","authors":"Mengyuan Hou, Hui Xu","doi":"10.1109/ISSREW55968.2022.00052","DOIUrl":"https://doi.org/10.1109/ISSREW55968.2022.00052","url":null,"abstract":"Deep learning is a powerful technique for many real- world problems. However, due to its unexplainable characteristic and over-fitting issue, there remains a great challenge for building reliable system with deep learning modules. In this paper, we present the idea of LegoAI that aims to build reliable AI software with pluggable modules of different functionalities, such as ensemble for fault tolerance and anomaly detection for result validation. In particular, we have applied the idea to develop a real-world AI software for handwritten digit recognition and achieved promising results.","PeriodicalId":178302,"journal":{"name":"2022 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","volume":"30 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":"123085545","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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