2021 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)最新文献_第8页

An efficient dual ensemble software defect prediction method with neural network 基于神经网络的双集成软件缺陷预测方法

2021 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW) Pub Date : 2021-10-01 DOI: 10.1109/ISSREW53611.2021.00049

Jinfu Chen, Jiaping Xu, Saihua Cai, Xiaoli Wang, Yuechao Gu, Shuhui Wang

{"title":"An efficient dual ensemble software defect prediction method with neural network","authors":"Jinfu Chen, Jiaping Xu, Saihua Cai, Xiaoli Wang, Yuechao Gu, Shuhui Wang","doi":"10.1109/ISSREW53611.2021.00049","DOIUrl":"https://doi.org/10.1109/ISSREW53611.2021.00049","url":null,"abstract":"With the rapid development of technology, software projects are becoming increasingly complex, but the problem of defects is still not well solved, and the application of defective software will bring some security problems, therefore, it is necessary to identify the defective modules to ensure the quality of software. Software defect prediction (SDP) can achieve this goal and it is now an essential part of software testing. However, there is a problem of class imbalance in the defective datasets, which can easily cause the prediction models inaccuracy. Ensemble learning has been proven to be one of the best ways to address the problem of class imbalance. In this paper, we propose an efficient dual ensemble software defect prediction method with neural network (DE-SDP) to solve the class imbalance problem, thereby improving the performance of prediction model. Firstly, we combine cross-validation and seven different classifiers to build base ensemble classifiers. Then, we use stacking method and neural network model to re-ensemble the base ensemble classifiers. Finally, we evaluate the performance of proposed DE-SDP on eight public datasets, and the results demonstrate the effectiveness of the DE-SDP method.","PeriodicalId":385392,"journal":{"name":"2021 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","volume":"67 51","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113937613","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

$^{prime}mathbf{R}$: Towards Detecting and Understanding Code-Document Violations in Rust $^{prime}mathbf{R}$:在Rust中检测和理解代码文档冲突

2021 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW) Pub Date : 2021-10-01 DOI: 10.1109/ISSREW53611.2021.00063

Wanrong Ouyang, Baojian Hua

{"title":"$^{prime}mathbf{R}$: Towards Detecting and Understanding Code-Document Violations in Rust","authors":"Wanrong Ouyang, Baojian Hua","doi":"10.1109/ISSREW53611.2021.00063","DOIUrl":"https://doi.org/10.1109/ISSREW53611.2021.00063","url":null,"abstract":"Documentation and comments are important for any software project. Although documentation is not executed, it is useful for many purposes, such as code comprehension, reuse, and maintenance. As a project evolves, the code and documentation can easily grow out-of-sync, and inconsistencies are introduced, which can mislead developers and introduce new bugs in subsequent developments. Recent studies have shown it is promising to use natural language processing and machine learning to detect inconsistencies between code and documentation. However, it's challenging to apply existing techniques to detect code-document inconsistency in Rust programs, as Rustdoc supports advanced document features like document testing, which makes existing solutions inapplicable. This paper presents the first software tool prototype, 'R, to detect and understand code-document inconsistencies in Rust. To perform such analysis, 'R leverages static program analysis, not only on Rust source code, but also on document testing code, to detect inconsistency indicating either bugs or bad documentation. To evaluate the effectiveness of 'R, we applied it to 37 open source Rust projects from 9 domains, with a total of 6,192,251 lines of Rust source code (with 322,330 lines of comments). The results of the analysis give interesting insights, for example: the cryptocurrency domain has the highest documentation ratio (58.23%), documentation testing is rarely used (ratio 2.30% on average) in real-world Rust projects in all domains, etc. Based on these findings, we propose recommendations to guide the construction of better Rust documentation, better Rust documentation quality detection tools, and boarder adoption of the language.","PeriodicalId":385392,"journal":{"name":"2021 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","volume":"466 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131886067","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

A next-generation platform for Cyber Range-as-a-Service 下一代网络靶场即服务平台

2021 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW) Pub Date : 2021-10-01 DOI: 10.1109/ISSREW53611.2021.00094

Vittorio Orbinato

引用次数: 1

RADAS 2021 Workshop Keynote RADAS 2021研讨会主题演讲

2021 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW) Pub Date : 2021-10-01 DOI: 10.1109/issrew53611.2021.00028

引用次数: 0

Changes in Intent: Behavioral Predictions of Distributed SDN Controller Reconfiguration 意图的改变:分布式SDN控制器重构的行为预测

2021 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW) Pub Date : 2021-10-01 DOI: 10.1109/ISSREW53611.2021.00115

Yuming Wu, N. Mohanasamy, L. Jagadeesan, M. Rahman

{"title":"Changes in Intent: Behavioral Predictions of Distributed SDN Controller Reconfiguration","authors":"Yuming Wu, N. Mohanasamy, L. Jagadeesan, M. Rahman","doi":"10.1109/ISSREW53611.2021.00115","DOIUrl":"https://doi.org/10.1109/ISSREW53611.2021.00115","url":null,"abstract":"Intent-based programming enables software-defined networks (SDN) to be able to dynamically reconfigure themselves through automatic intent recomputation in response to network events, such as host mobility. This allows SDN to be used as a platform for new technologies such as swarms of drones in data-driven agriculture. At the same time, this dynamicity results in SDN networks having a very large state space - whose size is further exacerbated when SDN controllers are distributed for reliability and scalability. This renders infeasible comprehensive testing or verification of network performance prior to deployment, necessitating the use of monitoring at run-time, together with associated abortive or healing actions to ensure reliability. However, as intent recomputation time can vary significantly based on the underlying network topologies, it is very difficult to experimentally determine the boundary between normal expected performance and anomalous performance at scale, and hence to specify when these actions should take place. In this paper, we demonstrate the use of machine learning to automatically learn intent recomputation performance; the resulting predictions can be used as input into the specification of run-time monitors and the determination of associated reliability mitigations. More specifically, we describe our proof-of-concept case study on using linear regression to predict the expected time for intent recomputation due to host mobility on the distributed ONOS open-source SDN controller.","PeriodicalId":385392,"journal":{"name":"2021 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130848477","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

Network Intrusion Detection by an Approximate Logic Neural Model 基于近似逻辑神经模型的网络入侵检测

2021 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW) Pub Date : 2021-10-01 DOI: 10.1109/ISSREW53611.2021.00072

Jiajun Zhao, Qiuzhen Lin, Junkai Ji

{"title":"Network Intrusion Detection by an Approximate Logic Neural Model","authors":"Jiajun Zhao, Qiuzhen Lin, Junkai Ji","doi":"10.1109/ISSREW53611.2021.00072","DOIUrl":"https://doi.org/10.1109/ISSREW53611.2021.00072","url":null,"abstract":"With a growing threat of cyber-attacks, network intrusion detection remains challenging in the domain of cyberspace security. To defend against cyber-attacks on computer systems, various machine learning approaches have been applied for intrusion detection over the past few decades, such as random forest, support vector machine and long short-term memory. Although most of these approaches can provide satisfactory detection performances in terms of accuracy, recall and area under the receiver operating characteristic curve (AUC), their performances rely heavily on the training sample amount of attacks. When the type of attacks is unknown and the training sample amount is insufficient, the performances of these approaches may degenerate more or less. Therefore, based on a recently emerging approximate logic neural model (ALNM), a novel intrusion detection approach termed ALNM-IDA is proposed to overcome the issue in this paper. In the ALNM-IDA, the k-means clustering is first applied to discretize continuous features, and the maximum relevance minimum redundancy is adopted to select essential features. Then, the training dataset of normal and attack inputs is fed to the ALNM. In addition, adaptive moment estimation (Adam) is used as the training algorithm to improve the detection performance and accelerate the training phase. To validate the effectiveness of the ALNM-IDA, three benchmark intrusion detection datasets are employed in our experiments. Comparative results demonstrate that the ALNM-IDA can provide superior detection performance than other widely-used machine learning approaches in the case of insufficient training information.","PeriodicalId":385392,"journal":{"name":"2021 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132925857","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

RESS 2021 Workshop Keynote RESS 2021研讨会主题演讲

2021 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW) Pub Date : 2021-10-01 DOI: 10.1109/issrew53611.2021.00031

引用次数: 0

Meaningful color image encryption algorithm based on compressive sensing and chaotic map 基于压缩感知和混沌映射的有意义彩色图像加密算法

2021 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW) Pub Date : 2021-10-01 DOI: 10.1109/ISSREW53611.2021.00073

Min Liu, G. Ye, Qiuzhen Lin

{"title":"Meaningful color image encryption algorithm based on compressive sensing and chaotic map","authors":"Min Liu, G. Ye, Qiuzhen Lin","doi":"10.1109/ISSREW53611.2021.00073","DOIUrl":"https://doi.org/10.1109/ISSREW53611.2021.00073","url":null,"abstract":"At present, most image encryption algorithms protect the security of the plain images by encrypting them into visually meaningless cipher images similar to noises. However, noise-like images can easily attract the attention of an attacker, thus increasing the risk of being broken. Based on this, a visually meaningful image encryption algorithm based on compressive sensing and chaotic map is proposed in this paper. The proposed image encryption algorithm consists of three steps: compression, encryption and hiding. Here, the compression part uses the compressive sensing technology to compress the plain image. Then, the encryption part transforms the compressed image into a meaningless noise-like image by confusion operation. Finally, the hiding process is to embed the encrypted image into a carrier image to achieve a visual hiding effect and reduce the attention of the attacker to the carrier image. Experimental results and analysis show that the proposed algorithm has satisfactory hiding effect and high-quality information reconstruction and extraction. Especially, the values of PSNR can surpass 30 dB in the tests.","PeriodicalId":385392,"journal":{"name":"2021 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133760524","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

MC-FGSM: Black-box Adversarial Attack for Deep Learning System MC-FGSM:深度学习系统的黑盒对抗攻击

2021 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW) Pub Date : 2021-10-01 DOI: 10.1109/ISSREW53611.2021.00058

Wenqiang Zheng, Yanfang Li

{"title":"MC-FGSM: Black-box Adversarial Attack for Deep Learning System","authors":"Wenqiang Zheng, Yanfang Li","doi":"10.1109/ISSREW53611.2021.00058","DOIUrl":"https://doi.org/10.1109/ISSREW53611.2021.00058","url":null,"abstract":"Deep learning (DL) technology has been widely applied in the safety-critical area, for instance, autopilot system in which the misbehavior will have a huge influence. Hence the reliability of DL system should be tested thoroughly. DL reliability testing is mainly achieved via adversarial attack, however, the existing attack methods lack mathematical proof whether the convergence of the attack can be guaranteed. This paper proposes a novel adversarial attack method, i.e., Monte Carlo-Fast Gradient Sign Method (MC-FGSM) to test the DL robustness. This method does not require any knowledge of the victim DL system. Specifically, this method first approximates the gradient of the input variable via Monte Carlo sampling technique, and then the gradient-based method is applied to generate adversarial attacks. Moreover, a strict mathematical proof has shown the gradient estimation is unbiased and the time complexity is $boldsymbol{O}(1)$, while the existing method is $boldsymbol{O}(N)$. The effectiveness of the proposed method is demonstrated by numerical experiments. This method can work as the reliability evaluation tool of the autopilot system.","PeriodicalId":385392,"journal":{"name":"2021 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117249448","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

Innovation evaluation framework using state transition probability of the product 基于产品状态转移概率的创新评价框架

2021 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW) Pub Date : 2021-10-01 DOI: 10.1109/ISSREW53611.2021.00099

K. Jinzenji, Akio Jin, Tatsuya Muramoto

引用次数: 0