{"title":"Message from the SHIFT 2019 Workshop Chairs","authors":"","doi":"10.1109/issrew.2019.00030","DOIUrl":"https://doi.org/10.1109/issrew.2019.00030","url":null,"abstract":"","PeriodicalId":166239,"journal":{"name":"2019 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121350368","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":"Research Proposal: Reliability Evaluation of the Apache Kafka Streaming System","authors":"Han Wu","doi":"10.1109/ISSREW.2019.00055","DOIUrl":"https://doi.org/10.1109/ISSREW.2019.00055","url":null,"abstract":"Apache Kafka is a distributed messaging system with high throughput, high scalability and low latency. It has been widely adopted in enterprise and due to its widespread integration into enterprise-level infrastructures, the research on the reliability of Kafka consumers has become an increasingly important issue. The application scenarios vary from tracking user profiles on a website, server log monitoring, to online bank transfer and online reservation. The main purpose of this research is to evaluate the reliability of Kafka in different application scenarios. Kafka is highly configurable and provides many options to manage reliability strategies. In this research we test the impacts of all kinds of configuration parameters on the reliability of Kafka, including retry strategies and replications of partitions for fault tolerance. The tradeoffs between performance and reliability is another portion of our research, which help users of Kafka using it in an appropriate way.","PeriodicalId":166239,"journal":{"name":"2019 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115661978","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}
K. Hogan, N. Warford, Robert Morrison, David Miller, Sean Malone, James M. Purtilo
{"title":"The Challenges of Labeling Vulnerability-Contributing Commits","authors":"K. Hogan, N. Warford, Robert Morrison, David Miller, Sean Malone, James M. Purtilo","doi":"10.1109/ISSREW.2019.00083","DOIUrl":"https://doi.org/10.1109/ISSREW.2019.00083","url":null,"abstract":"Software projects developed using version control are enhanced incrementally through commits, some of which inevitably introduce security vulnerabilities. The features of these vulnerability-contributing commits (VCCs) could be used to train a VCC detector or to inform software development best-practices. Previous work has attempted to label VCCs in open-source software projects for this purpose. We present a manual approach to VCC labeling using the fix commits listed in Common Vulnerabilities and Exposures (CVEs). We show that a published automated method of VCC labeling disagrees with our manual method on 42% of VCCs. We argue that the automated method, while effective in scaling VCC labeling, is therefore not sufficiently accurate. Finally, we discuss the benefits and drawbacks of trying to predict vulnerable software components rather than VCCs.","PeriodicalId":166239,"journal":{"name":"2019 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130949082","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 Framework for Resilient Data Management for Smart Grids","authors":"Theresa Bettmann","doi":"10.1109/issrew.2019.00048","DOIUrl":"https://doi.org/10.1109/issrew.2019.00048","url":null,"abstract":"Smart grids add information and communication technologies to traditional energy grids. This extension leads to an increased number of new devices in the energy system, like smart meters, sensors, and other electrical infrastructure devices. Collectively these devices produce a vast amount of heterogeneous data that needs to be captured, processed, and analyzed. Besides, the energy system transforms from a one-way into a bidirectional distribution of energy and information. System users not only consume the energy they also produce, store, and share it. These innovations result in potential more and new vulnerabilities. Therefore, a reliable, failure resistant, and secure data management for smart grids is required. This paper describes the problem definition and the research goals of a dissertation research project with a focus on resilient data management for smart grids. The dissertation aims to contribute to the design and development of resilient data management systems for smart grids through a systematic and structured framework that provides suitable technology suggestions and methods to address the described vulnerabilities.","PeriodicalId":166239,"journal":{"name":"2019 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114975121","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}
V. Mendiratta, Zhuoran Liu, Mrinmoy Bhattacharjee, Yu Zhou
{"title":"Detecting and Diagnosing Anomalous Behavior in Large Systems with Change Detection Algorithms","authors":"V. Mendiratta, Zhuoran Liu, Mrinmoy Bhattacharjee, Yu Zhou","doi":"10.1109/ISSREW.2019.00041","DOIUrl":"https://doi.org/10.1109/ISSREW.2019.00041","url":null,"abstract":"Large telecommunications networks are designed to achieve high reliability with hardware and software redundancy that is managed through complex fault-tolerant mechanisms for error detection and recovery. Because of the fault tolerant mechanisms, when errors do occur they do not always cause failures and, hence, it can be difficult to detect anomalous behavior of the system and to determine its root cause. In this paper, using sequential system performance data, we present the application of multivariate change detection algorithms and visual analytics methods for detecting and diagnosing anomalous behavior with low latency in telecommunications systems. Such methods, coupled with domain knowledge, are efficient and effective for detecting and diagnosing anomalies as compared to log analysis. We demonstrate our methods with real data from a large system.","PeriodicalId":166239,"journal":{"name":"2019 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115674637","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":"From Monolithic Architecture to Microservices Architecture","authors":"Lorenzo De Lauretis","doi":"10.1109/ISSREW.2019.00050","DOIUrl":"https://doi.org/10.1109/ISSREW.2019.00050","url":null,"abstract":"The purpose of this work is the definition of a strategy, still in early stage, that will be able to support the migration from a Monolithic Architecture to a Microservices Architecture. This strategy aims to be applied to monolith systems, encouraging their evolution into microservices-based systems. Using this migration strategy, the newborn system will take advantages of a number of benefits offered by microservices architecture, such as scalability and maintainability. Companies will be able to migrate their old monolith systems into more flexible microservices-based systems, evolving their software in a more powerful one.","PeriodicalId":166239,"journal":{"name":"2019 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117055075","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}
Guolin Tan, Peng Zhang, Lei Zhang, Yu Zhang, Chuang Zhang, Qingyun Liu, Xinran Liu
{"title":"Learning from Time Series with Outlier Correction for Malicious Domain Identification","authors":"Guolin Tan, Peng Zhang, Lei Zhang, Yu Zhang, Chuang Zhang, Qingyun Liu, Xinran Liu","doi":"10.1109/ISSREW.2019.00040","DOIUrl":"https://doi.org/10.1109/ISSREW.2019.00040","url":null,"abstract":"Malicious domain identification is an important task in the field of cyberspace security. However, most of existing work for this task heavily relies on expert experience when constructing machine learning features. What makes matters worse is that these features can be deliberately changed by attackers. As a result, such malicious domain identification methods are easily bypassed by cyber criminals. To solve this problem, in this paper, we propose a novel method for malicious domain identification by effectively learning time series shapelets, the discriminative local patterns of time series. More specifically, our method consists of two main components: 1) modeling user's habits of accessing domains by learning shapelets from domain time series. As the domain time series is generated by the crowd visiting websites, the learned user's habits of accessing domains can potentially reflect what type of service a domain provides, such as pornography, gambling and so on. 2) an outlier correction algorithm designed for a single time series and independent of the model which can enhance the robustness of shapelet initialization. We integrate shapelet learning and outlier correction in our model. Extensive experiments on real-world dataset demonstrates that our proposed method has better performance compared with state-of-the-art methods.","PeriodicalId":166239,"journal":{"name":"2019 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126909178","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}
C. Donnarumma, Pietro Fara, Gabriele Serra, Sandro Di Leonardi, Mauro Marinoni
{"title":"EN-50128 Certification-Oriented Design of a Safety-Critical Hard Real-Time Kernel","authors":"C. Donnarumma, Pietro Fara, Gabriele Serra, Sandro Di Leonardi, Mauro Marinoni","doi":"10.1109/ISSREW.2019.00090","DOIUrl":"https://doi.org/10.1109/ISSREW.2019.00090","url":null,"abstract":"The growing complexity and the need for high safety standards in railways infrastructures are pushing the infrastructure operators toward the adoption of newer solutions able to exploit modern platforms and state-of-the-art software solutions while guaranteeing safety and timing constraints, and maintaining the compliance with the standards. This paper presents the design guidelines of a novel real-time kernel whose development is based on the Italian use case, highlighting its focus on adherence to the standards.","PeriodicalId":166239,"journal":{"name":"2019 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127301982","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}