IEEE International Conference on Software Quality, Reliability and Security : proceedings. IEEE International Conference on Software Quality, Reliability and Security最新文献
{"title":"High-Reliability Compilation Optimization Sequence Generation Framework Based ANN","authors":"Jiang Wu, Jianjun Xu, Xiankai Meng, Zhuo Zhang, Nan Zhang, Haoyu Zhang","doi":"10.1109/QRS51102.2020.00053","DOIUrl":"https://doi.org/10.1109/QRS51102.2020.00053","url":null,"abstract":"","PeriodicalId":92210,"journal":{"name":"IEEE International Conference on Software Quality, Reliability and Security : proceedings. IEEE International Conference on Software Quality, Reliability and Security","volume":"200 1","pages":"347-355"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75744140","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}
Daniel Flemström, Eduard Paul Enoiu, W. Afzal, Daniel Sundmark, T. Gustafsson, A. Kobetski
{"title":"From Natural Language Requirements to Passive Test Cases Using Guarded Assertions","authors":"Daniel Flemström, Eduard Paul Enoiu, W. Afzal, Daniel Sundmark, T. Gustafsson, A. Kobetski","doi":"10.1109/QRS.2018.00060","DOIUrl":"https://doi.org/10.1109/QRS.2018.00060","url":null,"abstract":"","PeriodicalId":92210,"journal":{"name":"IEEE International Conference on Software Quality, Reliability and Security : proceedings. IEEE International Conference on Software Quality, Reliability and Security","volume":"127 1","pages":"470-481"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76051757","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}
Motivated by frequent failures in cloud computing systems, we analyze failure frequency and failure continuity of tasks from the Google cloud cluster, and find what we call killer tasks that suffer from frequent failures and repeated rescheduling. Killer tasks cause unnecessary resource wasting and significant increase of scheduling workloads, which can be a big concern in cloud systems. We aim to recognize killer tasks at the very early stage of their occurrence so that they can be addressed proactively instead of being rescheduled repeatedly, so as to promote reliability and save resources. To recognize killer tasks from a large amount of tasks in real time is really challenging. In this paper, we first investigate characteristics and behavior patterns of killer tasks and then develop two machine learning based methods, K-HUNTER and C-HUNTER, for online recognition of killer tasks. The empirical results show that our approach performs at 97% of precision in recognizing killer tasks with an 89% timing advance and 88% of resource saving for the cloud system on average.
{"title":"Hunting Killer Tasks for Cloud System through Machine Learning: A Google Cluster Case Study","authors":"Hongyan Tang, Ying Li, Tong Jia, Zhonghai Wu","doi":"10.1109/QRS.2016.11","DOIUrl":"https://doi.org/10.1109/QRS.2016.11","url":null,"abstract":"Motivated by frequent failures in cloud computing systems, we analyze failure frequency and failure continuity of tasks from the Google cloud cluster, and find what we call killer tasks that suffer from frequent failures and repeated rescheduling. Killer tasks cause unnecessary resource wasting and significant increase of scheduling workloads, which can be a big concern in cloud systems. We aim to recognize killer tasks at the very early stage of their occurrence so that they can be addressed proactively instead of being rescheduled repeatedly, so as to promote reliability and save resources. To recognize killer tasks from a large amount of tasks in real time is really challenging. In this paper, we first investigate characteristics and behavior patterns of killer tasks and then develop two machine learning based methods, K-HUNTER and C-HUNTER, for online recognition of killer tasks. The empirical results show that our approach performs at 97% of precision in recognizing killer tasks with an 89% timing advance and 88% of resource saving for the cloud system on average.","PeriodicalId":92210,"journal":{"name":"IEEE International Conference on Software Quality, Reliability and Security : proceedings. IEEE International Conference on Software Quality, Reliability and Security","volume":"20 1","pages":"1-12"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81922765","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 : 2016-01-01Epub Date: 2016-10-13DOI: 10.1109/QRS.2016.50
Harish Sukhwani, Javier Alonso, Kishor S Trivedi, Issac Mcginnis
In this paper, we present the software reliability analysis of the flight software of a recently launched space mission. For our analysis, we use the defect reports collected during the flight software development. We find that this software was developed in multiple releases, each release spanning across all software life-cycle phases. We also find that the software releases were developed and tested for four different hardware platforms, spanning from off-the-shelf or emulation hardware to actual flight hardware. For releases that exhibit reliability growth or decay, we fit Software Reliability Growth Models (SRGM); otherwise we fit a distribution function. We find that most releases exhibit reliability growth, with Log-Logistic (NHPP) and S-Shaped (NHPP) as the best-fit SRGMs. For the releases that experience reliability decay, we investigate the causes for the same. We find that such releases were the first software releases to be tested on a new hardware platform, and hence they encountered major hardware integration issues. Also such releases seem to have been developed under time pressure in order to start testing on the new hardware platform sooner. Such releases exhibit poor reliability growth, and hence exhibit high predicted failure rate. Other problems include hardware specification changes and delivery delays from vendors. Thus, our analysis provides critical insights and inputs to the management to improve the software development process. As NASA has moved towards a product line engineering for its flight software development, software for future space missions will be developed in a similar manner and hence the analysis results for this mission can be considered as a baseline for future flight software missions.
{"title":"Software Reliability Analysis of NASA Space Flight Software: A Practical Experience.","authors":"Harish Sukhwani, Javier Alonso, Kishor S Trivedi, Issac Mcginnis","doi":"10.1109/QRS.2016.50","DOIUrl":"https://doi.org/10.1109/QRS.2016.50","url":null,"abstract":"<p><p>In this paper, we present the software reliability analysis of the flight software of a recently launched space mission. For our analysis, we use the defect reports collected during the flight software development. We find that this software was developed in multiple releases, each release spanning across all software life-cycle phases. We also find that the software releases were developed and tested for four different hardware platforms, spanning from off-the-shelf or emulation hardware to actual flight hardware. For releases that exhibit reliability growth or decay, we fit Software Reliability Growth Models (SRGM); otherwise we fit a distribution function. We find that most releases exhibit reliability growth, with Log-Logistic (NHPP) and S-Shaped (NHPP) as the best-fit SRGMs. For the releases that experience reliability decay, we investigate the causes for the same. We find that such releases were the first software releases to be tested on a new hardware platform, and hence they encountered major hardware integration issues. Also such releases seem to have been developed under time pressure in order to start testing on the new hardware platform sooner. Such releases exhibit poor reliability growth, and hence exhibit high predicted failure rate. Other problems include hardware specification changes and delivery delays from vendors. Thus, our analysis provides critical insights and inputs to the management to improve the software development process. As NASA has moved towards a product line engineering for its flight software development, software for future space missions will be developed in a similar manner and hence the analysis results for this mission can be considered as a baseline for future flight software missions.</p>","PeriodicalId":92210,"journal":{"name":"IEEE International Conference on Software Quality, Reliability and Security : proceedings. IEEE International Conference on Software Quality, Reliability and Security","volume":"3 ","pages":"386-397"},"PeriodicalIF":0.0,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/QRS.2016.50","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35688247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Modeling Embedded Software Test Requirement Based on MARTE","authors":"Yichen Wang, Xinsheng Lan, Yikun Wang","doi":"10.1109/SERE-C.2013.43","DOIUrl":"https://doi.org/10.1109/SERE-C.2013.43","url":null,"abstract":"","PeriodicalId":92210,"journal":{"name":"IEEE International Conference on Software Quality, Reliability and Security : proceedings. IEEE International Conference on Software Quality, Reliability and Security","volume":"166 1","pages":"109-115"},"PeriodicalIF":0.0,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77293394","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}
{"title":"A Study on Airborne Software Safety Requirements Patterns","authors":"Wei Chang, Xiaohong Bao, Xuefei Li","doi":"10.1109/SERE-C.2013.44","DOIUrl":"https://doi.org/10.1109/SERE-C.2013.44","url":null,"abstract":"","PeriodicalId":92210,"journal":{"name":"IEEE International Conference on Software Quality, Reliability and Security : proceedings. IEEE International Conference on Software Quality, Reliability and Security","volume":"26 1","pages":"131-136"},"PeriodicalIF":0.0,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79242509","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}
{"title":"Application of Software Safety Analysis Using Event-B","authors":"Hong Zhang, Lili Xu","doi":"10.1109/SERE-C.2013.45","DOIUrl":"https://doi.org/10.1109/SERE-C.2013.45","url":null,"abstract":"","PeriodicalId":92210,"journal":{"name":"IEEE International Conference on Software Quality, Reliability and Security : proceedings. IEEE International Conference on Software Quality, Reliability and Security","volume":"58 1","pages":"137-144"},"PeriodicalIF":0.0,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78368462","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}
{"title":"A Novel Method for Modeling Complex Network of Software System Security","authors":"Hailin Li, Yadi Wang, Jihong Han","doi":"10.1109/SERE-C.2012.33","DOIUrl":"https://doi.org/10.1109/SERE-C.2012.33","url":null,"abstract":"","PeriodicalId":92210,"journal":{"name":"IEEE International Conference on Software Quality, Reliability and Security : proceedings. IEEE International Conference on Software Quality, Reliability and Security","volume":"17 1","pages":"24-26"},"PeriodicalIF":0.0,"publicationDate":"2012-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82659925","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}
IEEE International Conference on Software Quality, Reliability and Security : proceedings. IEEE International Conference on Software Quality, Reliability and Security
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