Pub Date : 2018-10-01DOI: 10.1109/ISSREW.2018.000-1
Leonid Joffe
The proposed thesis introduces cutting edge Machine Learning (ML) tools into Search Based Software Engineering (SBST). The contribution is three-fold. The first is an ML driven property targeting search strategy. It uses a deep neural network to process execution trace information to yield a likelihood score of a presence of a crash, which is in turn used as a fitness function for search. This method clearly outperforms the baseline search technique. The second contribution is a method for defining a property agnostic search landscape. This is achieved by training an autoencoder on a corpus of execution traces to produce a "latent space" representation. The expectation is to observe a tendency for arbitrary properties of executions to group in distinct regions of the latent space. Location in this space would in turn be used to direct an SBST process. The third contribution is to augment an automated tool with a generative model. The intention is to produce approximately valid input seeds that would target desired locations of a fitness landscape. These contributions will provide novel ideas for future research in the intersection of SBST and ML.
{"title":"Machine Learning Augmented Fuzzing","authors":"Leonid Joffe","doi":"10.1109/ISSREW.2018.000-1","DOIUrl":"https://doi.org/10.1109/ISSREW.2018.000-1","url":null,"abstract":"The proposed thesis introduces cutting edge Machine Learning (ML) tools into Search Based Software Engineering (SBST). The contribution is three-fold. The first is an ML driven property targeting search strategy. It uses a deep neural network to process execution trace information to yield a likelihood score of a presence of a crash, which is in turn used as a fitness function for search. This method clearly outperforms the baseline search technique. The second contribution is a method for defining a property agnostic search landscape. This is achieved by training an autoencoder on a corpus of execution traces to produce a \"latent space\" representation. The expectation is to observe a tendency for arbitrary properties of executions to group in distinct regions of the latent space. Location in this space would in turn be used to direct an SBST process. The third contribution is to augment an automated tool with a generative model. The intention is to produce approximately valid input seeds that would target desired locations of a fitness landscape. These contributions will provide novel ideas for future research in the intersection of SBST and ML.","PeriodicalId":321448,"journal":{"name":"2018 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120954854","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 : 2018-10-01DOI: 10.1109/ISSREW.2018.00-16
Fuqun Huang, L. Strigini
As the primary cause of software defects, human error is the key to understanding and perhaps to predicting and preventing software defects. However, little research has been done to forecast software defects based on defects' cognitive nature. This paper proposes an idea for predicting software defects by applying the current scientific understanding of human error mechanisms. This new prediction method is based on the main causal mechanism underlying software defects: an error-prone scenario triggers a sequence of human error modes. Preliminary evidence for supporting this idea is presented.
{"title":"Predicting Software Defects Based on Cognitive Error Theories","authors":"Fuqun Huang, L. Strigini","doi":"10.1109/ISSREW.2018.00-16","DOIUrl":"https://doi.org/10.1109/ISSREW.2018.00-16","url":null,"abstract":"As the primary cause of software defects, human error is the key to understanding and perhaps to predicting and preventing software defects. However, little research has been done to forecast software defects based on defects' cognitive nature. This paper proposes an idea for predicting software defects by applying the current scientific understanding of human error mechanisms. This new prediction method is based on the main causal mechanism underlying software defects: an error-prone scenario triggers a sequence of human error modes. Preliminary evidence for supporting this idea is presented.","PeriodicalId":321448,"journal":{"name":"2018 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129282167","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 : 2018-10-01DOI: 10.1109/ISSREW.2018.00010
A. Andrzejak, Matthias Iacsa
Software applications routinely offer configuration settings to adapt them to specific deployment requirements. The number of available configuration options and their dependencies increase the likelihood of introducing configuration mistakes, with costly faults typically manifesting in a production environment. Automated diagnosis of configuration errors can help here, yet the practical value and acceptance of the proposed solutions depend - besides sufficient accuracy - on satisfying some non-functional requirements. These include: (i) low intrusiveness (i.e. little runtime data/instrumentation), (ii) full automation of the diagnosis process, and (iii) fast computation of a diagnosis. In this work we propose ConfGuru, an approach and a tool which attempts to fulfill all three of these requirements. ConfGuru complements and improves upon ConfDoctor, our previous (semi-automated) approach for diagnosis of configuration errors. ConfGuru adds a fast static analysis approach to identify all code locations where option values are read (so-called Option Read Points (ORPs)) in a targeted application. Previously, these locations needed to be found manually, severely limiting adoption of ConfDoctor for new targets. Furthermore, due to algorithmic optimizations we can reduce the total time for computing a diagnosis to below a minute, and streamline the analysis process. Our evaluation shows that ConfGuru can diagnose configuration errors and extract ORPs from a variety of applications with an accuracy matching previous semi-automated approaches. Simultaneously, it offers fast adaptation to new target applications and well as full process automation, and has low response time. This makes ConfGuru suitable as a practical configuration error diagnosis tool or a service for real-world scenarios.
{"title":"ConfGuru - A System for Fully Automated Debugging of Configuration Errors","authors":"A. Andrzejak, Matthias Iacsa","doi":"10.1109/ISSREW.2018.00010","DOIUrl":"https://doi.org/10.1109/ISSREW.2018.00010","url":null,"abstract":"Software applications routinely offer configuration settings to adapt them to specific deployment requirements. The number of available configuration options and their dependencies increase the likelihood of introducing configuration mistakes, with costly faults typically manifesting in a production environment. Automated diagnosis of configuration errors can help here, yet the practical value and acceptance of the proposed solutions depend - besides sufficient accuracy - on satisfying some non-functional requirements. These include: (i) low intrusiveness (i.e. little runtime data/instrumentation), (ii) full automation of the diagnosis process, and (iii) fast computation of a diagnosis. In this work we propose ConfGuru, an approach and a tool which attempts to fulfill all three of these requirements. ConfGuru complements and improves upon ConfDoctor, our previous (semi-automated) approach for diagnosis of configuration errors. ConfGuru adds a fast static analysis approach to identify all code locations where option values are read (so-called Option Read Points (ORPs)) in a targeted application. Previously, these locations needed to be found manually, severely limiting adoption of ConfDoctor for new targets. Furthermore, due to algorithmic optimizations we can reduce the total time for computing a diagnosis to below a minute, and streamline the analysis process. Our evaluation shows that ConfGuru can diagnose configuration errors and extract ORPs from a variety of applications with an accuracy matching previous semi-automated approaches. Simultaneously, it offers fast adaptation to new target applications and well as full process automation, and has low response time. This makes ConfGuru suitable as a practical configuration error diagnosis tool or a service for real-world scenarios.","PeriodicalId":321448,"journal":{"name":"2018 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","volume":"4 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126148838","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 : 2018-10-01DOI: 10.1109/issrew.2018.00-43
H. Alemzadeh, B. Gallina, R. Natella, Kateryna Netkachova, R. Pietrantuono, Nuno Silva
{"title":"Message from the WoSoCer 2018 Workshop Chairs","authors":"H. Alemzadeh, B. Gallina, R. Natella, Kateryna Netkachova, R. Pietrantuono, Nuno Silva","doi":"10.1109/issrew.2018.00-43","DOIUrl":"https://doi.org/10.1109/issrew.2018.00-43","url":null,"abstract":"","PeriodicalId":321448,"journal":{"name":"2018 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114090461","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 : 2018-10-01DOI: 10.1109/ISSREW.2018.00-38
Maninder Singh, G. Walia, Anurag Goswami
Software development is a multi-phase process that starts with requirement engineering. Requirements elicited from different stakeholders are documented in natural language (NL) software requirement specification (SRS) document. Due to the inherent ambiguity of NL, SRS is prone to faults (e.g., ambiguity, incorrectness, inconsistency). To find and fix faults early (where they are cheapest to find), companies routinely employ inspections, where skilled inspectors are selected to review the SRS and log faults. While other researchers have attempted to understand the factors (experience and learning styles) that can guide the selection of effective inspectors but could not report improved results. This study analyzes the reading patterns (RPs) of inspectors recorded by eye-tracking equipment and evaluates their abilities to find various fault-types. The inspectors' characteristics are selected by employing ML algorithms to find the most common RPs w.r.t each fault-types. Our results show that our approach could guide the inspector selection with an accuracy ranging between 79.3% and 94% for various fault-types.
{"title":"Using Supervised Learning to Guide the Selection of Software Inspectors in Industry","authors":"Maninder Singh, G. Walia, Anurag Goswami","doi":"10.1109/ISSREW.2018.00-38","DOIUrl":"https://doi.org/10.1109/ISSREW.2018.00-38","url":null,"abstract":"Software development is a multi-phase process that starts with requirement engineering. Requirements elicited from different stakeholders are documented in natural language (NL) software requirement specification (SRS) document. Due to the inherent ambiguity of NL, SRS is prone to faults (e.g., ambiguity, incorrectness, inconsistency). To find and fix faults early (where they are cheapest to find), companies routinely employ inspections, where skilled inspectors are selected to review the SRS and log faults. While other researchers have attempted to understand the factors (experience and learning styles) that can guide the selection of effective inspectors but could not report improved results. This study analyzes the reading patterns (RPs) of inspectors recorded by eye-tracking equipment and evaluates their abilities to find various fault-types. The inspectors' characteristics are selected by employing ML algorithms to find the most common RPs w.r.t each fault-types. Our results show that our approach could guide the inspector selection with an accuracy ranging between 79.3% and 94% for various fault-types.","PeriodicalId":321448,"journal":{"name":"2018 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133364332","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 : 2018-10-01DOI: 10.1109/ISSREW.2018.00023
R. Pietrantuono, S. Russo
As robots become increasingly intelligent and autonomous, spread well beyond the traditional area of industrial automation, and find many new critical applications - from robotics medicine to anthropic domains - we advocate the need for certification for robotics software. We discuss some relevant issues in robotics software engineering and certification, and outline some important challenges for the dependable software engineering community.
{"title":"Robotics Software Engineering and Certification: Issues and Challenges","authors":"R. Pietrantuono, S. Russo","doi":"10.1109/ISSREW.2018.00023","DOIUrl":"https://doi.org/10.1109/ISSREW.2018.00023","url":null,"abstract":"As robots become increasingly intelligent and autonomous, spread well beyond the traditional area of industrial automation, and find many new critical applications - from robotics medicine to anthropic domains - we advocate the need for certification for robotics software. We discuss some relevant issues in robotics software engineering and certification, and outline some important challenges for the dependable software engineering community.","PeriodicalId":321448,"journal":{"name":"2018 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130607249","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 : 2018-10-01DOI: 10.1109/ISSREW.2018.00-37
Chin-Wei Tien, Tsung-Ta Tsai, Ing-Yi Chen, S. Kuo
Recently, the use of embedded devices such as WiFi APs, IP CAM, and drones in Internet of Things (IoT) applications has become more widespread. These embedded devices are connected to networks and are often used for critical services. Thus, they receive significant attention from hackers who attempt to find a major intrusion vector in IoT applications. Hackers focus on identifying hidden backdoors in embedded devices to gain full remote access; if they gain access, they can cause significant damage to critical infrastructures. Therefore, to improve embedded device security, this study introduces Universal Firmware vulnerability Observer (UFO); UFO is a firmware vulnerability discovery system, which can automatically perform tasks such as reversing firmware embedded filesystem, identifying vulnerability, and exploring password leaks to meet the IoT firmware security verification standards, including OWASP, UL-2900, and ICSA Labs. In addition, we design a Shell Script Dependency algorithm to help identify hidden backdoor problems by discovering suspicious shell script execution paths in the extracted firmware filesystem. We use 237 real-world embedded device firmware files to evaluate UFO. The results indicate that the effectiveness of reversing firmware binary is 96%, which is significantly higher than that of open source tools. Besides, we also conclude that 73% of firmware files contain Common Vulnerabilities and Exposures in their embedded Linux kernel, 22% of firmware files can leak login passwords, and 6% of firmware files contain hidden backdoors. Moreover, we reported hidden backdoor problems to two IoT device vendors in Taiwan and received their confirmation. UFO can be successfully used for verifying firmware security and discovering hidden backdoor threats in commercial IoT devices.
最近，在物联网(IoT)应用中使用WiFi ap, IP CAM和无人机等嵌入式设备变得越来越普遍。这些嵌入式设备连接到网络，通常用于关键服务。因此，它们受到试图在物联网应用中找到主要入侵向量的黑客的极大关注。黑客专注于识别嵌入式设备中隐藏的后门，以获得完全的远程访问权限;如果他们进入，他们可能会对关键基础设施造成重大破坏。因此，为了提高嵌入式设备的安全性，本研究引入了通用固件漏洞观察者(Universal Firmware vulnerability Observer, UFO);UFO是一个固件漏洞发现系统，可以自动执行固件嵌入式文件系统反转、漏洞识别、密码泄露探索等任务，满足物联网固件安全验证标准，包括OWASP、UL-2900、ICSA Labs等。此外，我们设计了一个Shell脚本依赖算法，通过在提取的固件文件系统中发现可疑的Shell脚本执行路径来帮助识别隐藏的后门问题。我们使用237个真实的嵌入式设备固件文件来评估UFO。结果表明，反转固件二进制文件的有效性为96%，显著高于开源工具。此外，我们还得出结论，73%的固件文件在其嵌入式Linux内核中包含常见漏洞和暴露，22%的固件文件可以泄露登录密码，6%的固件文件包含隐藏后门。此外，我们向台湾的两家物联网设备供应商报告了隐藏的后门问题，并得到了他们的确认。UFO可以成功用于验证固件安全性和发现商业物联网设备中隐藏的后门威胁。
{"title":"UFO - Hidden Backdoor Discovery and Security Verification in IoT Device Firmware","authors":"Chin-Wei Tien, Tsung-Ta Tsai, Ing-Yi Chen, S. Kuo","doi":"10.1109/ISSREW.2018.00-37","DOIUrl":"https://doi.org/10.1109/ISSREW.2018.00-37","url":null,"abstract":"Recently, the use of embedded devices such as WiFi APs, IP CAM, and drones in Internet of Things (IoT) applications has become more widespread. These embedded devices are connected to networks and are often used for critical services. Thus, they receive significant attention from hackers who attempt to find a major intrusion vector in IoT applications. Hackers focus on identifying hidden backdoors in embedded devices to gain full remote access; if they gain access, they can cause significant damage to critical infrastructures. Therefore, to improve embedded device security, this study introduces Universal Firmware vulnerability Observer (UFO); UFO is a firmware vulnerability discovery system, which can automatically perform tasks such as reversing firmware embedded filesystem, identifying vulnerability, and exploring password leaks to meet the IoT firmware security verification standards, including OWASP, UL-2900, and ICSA Labs. In addition, we design a Shell Script Dependency algorithm to help identify hidden backdoor problems by discovering suspicious shell script execution paths in the extracted firmware filesystem. We use 237 real-world embedded device firmware files to evaluate UFO. The results indicate that the effectiveness of reversing firmware binary is 96%, which is significantly higher than that of open source tools. Besides, we also conclude that 73% of firmware files contain Common Vulnerabilities and Exposures in their embedded Linux kernel, 22% of firmware files can leak login passwords, and 6% of firmware files contain hidden backdoors. Moreover, we reported hidden backdoor problems to two IoT device vendors in Taiwan and received their confirmation. UFO can be successfully used for verifying firmware security and discovering hidden backdoor threats in commercial IoT devices.","PeriodicalId":321448,"journal":{"name":"2018 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133652894","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 : 2018-10-01DOI: 10.1109/ISSREW.2018.00025
Domenico Cotroneo, L. Simone, R. Natella
Network Function Virtualization (NFV) is an emerging networking paradigm that offers new ways of creating, deploying, and managing networking services, by turning physical network functions into virtualized one. The NFV paradigm heavily relies on cloud computing and virtualization technologies to provide carrier-grade services. The certification process of NFV systems is an open and critical question to ensure that the delivered network service provides specific guarantees about performance and dependability. In this paper, we propose potential guidelines for evaluating the reliability of NFV Infrastructures (NFVIs), with the aim of verifying whether NFVIs satisfy its reliability and performance requirements even in presence of faults. The guidelines are described as a set of key practices to be followed, in terms of inputs, activities, and outputs. These practices are intended to be conducted by companies that want to evaluate the reliability of their NFVI against quantitative performance, availability, and fault tolerance objectives, and to get precise feedback on how to improve its fault tolerance.
{"title":"Dependability Certification Guidelines for NFVIs through Fault Injection","authors":"Domenico Cotroneo, L. Simone, R. Natella","doi":"10.1109/ISSREW.2018.00025","DOIUrl":"https://doi.org/10.1109/ISSREW.2018.00025","url":null,"abstract":"Network Function Virtualization (NFV) is an emerging networking paradigm that offers new ways of creating, deploying, and managing networking services, by turning physical network functions into virtualized one. The NFV paradigm heavily relies on cloud computing and virtualization technologies to provide carrier-grade services. The certification process of NFV systems is an open and critical question to ensure that the delivered network service provides specific guarantees about performance and dependability. In this paper, we propose potential guidelines for evaluating the reliability of NFV Infrastructures (NFVIs), with the aim of verifying whether NFVIs satisfy its reliability and performance requirements even in presence of faults. The guidelines are described as a set of key practices to be followed, in terms of inputs, activities, and outputs. These practices are intended to be conducted by companies that want to evaluate the reliability of their NFVI against quantitative performance, availability, and fault tolerance objectives, and to get precise feedback on how to improve its fault tolerance.","PeriodicalId":321448,"journal":{"name":"2018 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131017802","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 : 2018-10-01DOI: 10.1109/ISSREW.2018.00-31
K. Manjunath, Vaibhav Anu, G. Walia, Gary L. Bradshaw
This paper reports an industrial study that was conducted to evaluate whether human error training procedures and instrumentation created by authors can be used to train industry software practitioners on human errors that occur during requirements engineering process. Industry practitioners were trained (using an on-line audio-visual package) to analyze requirements faults and map them to underlying human errors (i.e., the root causes of faults). Results of the study show that even though our training helped practitioners in gaining knowledge about requirements phase human errors, parts of the training procedures need to be improved. Additionally, practitioners also reported mechanisms to prevent human errors from happening during the requirements engineering process. These mechanisms can help organizations create interventions (like checklists) that can help software developers avoid committing human errors, thereby preventing faults that are caused due to these errors.
{"title":"Training Industry Practitioners to Investigate the Human Error Causes of Requirements Faults","authors":"K. Manjunath, Vaibhav Anu, G. Walia, Gary L. Bradshaw","doi":"10.1109/ISSREW.2018.00-31","DOIUrl":"https://doi.org/10.1109/ISSREW.2018.00-31","url":null,"abstract":"This paper reports an industrial study that was conducted to evaluate whether human error training procedures and instrumentation created by authors can be used to train industry software practitioners on human errors that occur during requirements engineering process. Industry practitioners were trained (using an on-line audio-visual package) to analyze requirements faults and map them to underlying human errors (i.e., the root causes of faults). Results of the study show that even though our training helped practitioners in gaining knowledge about requirements phase human errors, parts of the training procedures need to be improved. Additionally, practitioners also reported mechanisms to prevent human errors from happening during the requirements engineering process. These mechanisms can help organizations create interventions (like checklists) that can help software developers avoid committing human errors, thereby preventing faults that are caused due to these errors.","PeriodicalId":321448,"journal":{"name":"2018 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116493712","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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