2020 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)最新文献_第6页

Ensuring Dataset Quality for Machine Learning Certification 确保机器学习认证的数据集质量

2020 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW) Pub Date : 2020-10-01 DOI: 10.1109/ISSREW51248.2020.00085

Sylvaine Picard, Camille Chapdelaine, Cyril Cappi, L. Gardes, E. Jenn, Baptiste Lefèvre, Thomas Soumarmon

引用次数: 17

Using Metamorphic Testing to Evaluate DNN Coverage Criteria 使用变质测试评估DNN覆盖标准

2020 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW) Pub Date : 2020-10-01 DOI: 10.1109/ISSREW51248.2020.00055

Jinyi Zhou, Kun Qiu, Zheng Zheng, T. Chen, P. Poon

引用次数: 1

WoSoCer 2020 Workshop Committees wsocer2020车间委员会

2020 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW) Pub Date : 2020-10-01 DOI: 10.1109/issrew51248.2020.00023

引用次数: 0

A Reconfiguration Approach for Open Adaptive Systems-of-Systems 开放自适应系统的重构方法

2020 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW) Pub Date : 2020-10-01 DOI: 10.1109/ISSREW51248.2020.00076

B. Wudka, Carsten Thomas, Lennart Siefke, V. Sommer

{"title":"A Reconfiguration Approach for Open Adaptive Systems-of-Systems","authors":"B. Wudka, Carsten Thomas, Lennart Siefke, V. Sommer","doi":"10.1109/ISSREW51248.2020.00076","DOIUrl":"https://doi.org/10.1109/ISSREW51248.2020.00076","url":null,"abstract":"The drive for digitalization in industry and transport results in an increasing application of cooperative systems that form adaptive system-of-systems. With reconfiguration these systems are able to change their behavior to react on internal changes and changes in their environment. In this paper, we present a novel service-oriented approach for decentralized reconfiguration within such systems-of-systems that specifically supports open adaptive systems-of-systems. We introduce the concept of strategy blueprints that define possible combinations of services provided by individual members of the system-of-systems. During reconfiguration, individual member systems evaluate all strategies that are instantiable under the given conditions, and select the one optimally fulfilling a set of predefined goals as the most appropriate target configuration. With this novel approach, we support flexible reconfiguration across the borders of individual member systems, and the inclusion of new member system types that provide service variants not yet known at design time of the reconfiguration algorithm.","PeriodicalId":202247,"journal":{"name":"2020 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126846821","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

Evaluating Deep Learning Classification Reliability in Android Malware Family Detection 深度学习分类在Android恶意软件家族检测中的可靠性评估

2020 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW) Pub Date : 2020-10-01 DOI: 10.1109/ISSREW51248.2020.00082

Giacomo Iadarola, F. Martinelli, F. Mercaldo, A. Santone

引用次数: 10

How Robust is the Optimal Software Rejuvenation Timing? 软件复兴的最佳时机有多稳健?

2020 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW) Pub Date : 2020-10-01 DOI: 10.1109/ISSREW51248.2020.00098

Junjun Zheng, H. Okamura, T. Dohi

{"title":"How Robust is the Optimal Software Rejuvenation Timing?","authors":"Junjun Zheng, H. Okamura, T. Dohi","doi":"10.1109/ISSREW51248.2020.00098","DOIUrl":"https://doi.org/10.1109/ISSREW51248.2020.00098","url":null,"abstract":"Robustness is usually relevant for characterizing the dependence between the values of model parameters and system behavior, and can be understood as stability of system behavior under changes in model parameters. In this paper, we consider a simple software rejuvenation model and its optimal rejuvenation timing, which maximizes the steady-state availability of the system. The main contribution of this work is to provide a new perspective on the optimal software rejuvenation timing, that is, the robustness of the optimal rejuvenation timing against input factors. In particular, the degree of robustness is quantified by the first derivatives of the optimal rejuvenation timing with respect to the model parameters. The robustnesses of both optimal rejuvenation timing and system availability with the optimal rejuvenation timing are considered. A numerical example with Weibull distributed failure time is devoted to clarifying how robust the optimal rejuvenation timing is, and determine the most sensitive model parameter. As a result, the optimal rejuvenation timing seems to be more robust to the parameters regarding failure time distribution, compared with the other parameters.","PeriodicalId":202247,"journal":{"name":"2020 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125465908","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

Vulnerability Analysis as Trustworthiness Evidence in Security Benchmarking: A Case Study on Xen. 漏洞分析作为安全基准测试中的可信证据——以Xen为例

2020 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW) Pub Date : 2020-10-01 DOI: 10.1109/ISSREW51248.2020.00078

Charles F. Gonçalves, Nuno Antunes

引用次数: 2

Multi-label Classification of Commit Messages using Transfer Learning 基于迁移学习的提交消息多标签分类

2020 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW) Pub Date : 2020-10-01 DOI: 10.1109/ISSREW51248.2020.00034

Muhammad Usman Sarwar, Sarim Zafar, Mohamed Wiem Mkaouer, G. Walia, Muhammad Zubair Malik

{"title":"Multi-label Classification of Commit Messages using Transfer Learning","authors":"Muhammad Usman Sarwar, Sarim Zafar, Mohamed Wiem Mkaouer, G. Walia, Muhammad Zubair Malik","doi":"10.1109/ISSREW51248.2020.00034","DOIUrl":"https://doi.org/10.1109/ISSREW51248.2020.00034","url":null,"abstract":"Commit messages are used in the industry by developers to annotate changes made to the code. Accurate classification of these messages can help monitor the software evolution process and enable better tracking for various industrial stakeholders. In this paper, we present a state of the art method for commit message classification into categories as per Swanson’s maintenance activities i.e. “Corrective”, “Perfective”, and “Adaptive”. This is a challenging task because not all commit messages are well written and informative. Existing approaches rely on keyword-based techniques to solve this problem. However, these approaches are oblivious to the full language model and do not recognize the contextual relationship between words. State of the art methodology in Natural Language Processing (NLP), is to train a context-aware neural network (Transformer) on a very large data set that encompasses the entire language and then fine-tunes it for a specific task. In this way, the model can learn the language, pay attention to the context, and then transfer that knowledge for better performance at the specific task. We use an off-the-shelf neural network called DistilBERT and fine-tune it for commit message classification task. This step is non-trivial because programming languages and commit messages have unique keywords, jargon, and idioms. This paper presents our effort in training this model and constructing the data set for this task. We describe the rules used to construct the data set. We validate our approach on industrial projects from GitHub, such as Kubernetes, Linux, TensorFlow, Spark, TypeScript, and PyTorch. We were able to achieve 87% F1-score for the commit message classification task, which is an order of magnitude accurate than previous studies.","PeriodicalId":202247,"journal":{"name":"2020 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133961935","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: 7

Message from the GAUSS 2020 Workshop Chairs GAUSS 2020研讨会主席致辞

2020 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW) Pub Date : 2020-10-01 DOI: 10.1109/issrew51248.2020.00013

Pietro Braione, D. Briola, G. Angelis, F. Gallo, F. Poggi, G. Quattrocchi

引用次数: 0

Domain Metric Driven Decomposition of Data-Intensive Applications 数据密集型应用的域度量驱动分解

2020 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW) Pub Date : 2020-10-01 DOI: 10.1109/ISSREW51248.2020.00071

Matteo Camilli, Carmine Colarusso, B. Russo, E. Zimeo

{"title":"Domain Metric Driven Decomposition of Data-Intensive Applications","authors":"Matteo Camilli, Carmine Colarusso, B. Russo, E. Zimeo","doi":"10.1109/ISSREW51248.2020.00071","DOIUrl":"https://doi.org/10.1109/ISSREW51248.2020.00071","url":null,"abstract":"The microservices architectural style is picking up more and more momentum in IT industry for the development of systems as loosely coupled, collaborating services. Companies that undergo the migration of their own applications have aspirations such as increasing maintainability and the scale of operation. Such a process is worthwhile but not easy, since it should ensure atomic improvements to the overall architecture for each migration step. Furthermore, the systematic evaluation of migration steps becomes cumbersome without sensible optimization metrics that take into account performance and scalability under expected operational conditions. Recent lines of research recognize this task as challenging, especially in data-intensive applications where known approaches based, for instance, on Domain Driven Design may not be adequate. In this paper, we introduce an approach to evaluate a migration in an iterative way and recognize whether it represents an improvement in terms of performance and scalability. The approach leverages a Domain Metric-based analysis to quantitatively evaluate alternative architectures. We exemplified the envisioned approach on a data-intensive application case study in the domain of smart mobility. Preliminary results from our controlled experiments show the effectiveness of our approach to support systematic and automated evaluation of migration processes.","PeriodicalId":202247,"journal":{"name":"2020 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122218696","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