Proceedings of the 2nd ACM International Workshop on AI and Software Testing/Analysis最新文献

Bugsby: a tool support for bug triage automation Bugsby:一个支持错误自动分类的工具

Proceedings of the 2nd ACM International Workshop on AI and Software Testing/Analysis Pub Date : 2022-07-18 DOI: 10.1145/3536168.3543301

Oskar Picus, C. Serban

引用次数: 0

TEESlice: slicing DNN models for secure and efficient deployment teesslice:对DNN模型进行切片，实现安全高效的部署

Proceedings of the 2nd ACM International Workshop on AI and Software Testing/Analysis Pub Date : 2022-07-18 DOI: 10.1145/3536168.3543299

Ziqi Zhang, Lucien K. L. Ng, Bingyan Liu, Yifeng Cai, Ding Li, Yao Guo, Xiangqun Chen

{"title":"TEESlice: slicing DNN models for secure and efficient deployment","authors":"Ziqi Zhang, Lucien K. L. Ng, Bingyan Liu, Yifeng Cai, Ding Li, Yao Guo, Xiangqun Chen","doi":"10.1145/3536168.3543299","DOIUrl":"https://doi.org/10.1145/3536168.3543299","url":null,"abstract":"Providing machine learning services is becoming profit business for IT companies. It is estimated that the AI-related business will bring trillions of dollars to the global economy. When selling machine learning services, companies should consider two important aspects: the security of the DNN model and the inference latency. The DNN models are expensive to train and represent precious intellectual property. The inference latency is important because modern DNN models are usually deployed to time-sensitive tasks and the inference latency affects the user's experience. Existing solutions cannot achieve a good balance between these two factors. To solve this problem, we propose TEESlice that provides a strong security guarantee and low service latency at the same time. TEESlice utilizes two kinds of specialized hardware: Trusted Execution Environments (TEE) and existing AI accelerators. When the company wants to deploy a private DNN model on the user's device, TEESlice can be used to extract the private information into model slices. The slices are attached to a public privacy-excluded backbone to form a hybrid model that has similar performance to the original model. When deploying the hybrid model, the lightweight privacy-related slice is secured by the TEE and the public backbone is put on the AI accelerators. The TEE provides a strong security guarantee on the model privacy and the accelerators reduce the computation latency of the heavy model backbone. Experimental results show that TEESlice can achieve more than 10x throughput promotion with the same level of strong security guarantee as putting the whole model inside the TEE. If the model provider wants to further verify the correctness of the accelerator's computation, TEESlice can still achieve 3-4x performance improvement.","PeriodicalId":287847,"journal":{"name":"Proceedings of the 2nd ACM International Workshop on AI and Software Testing/Analysis","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122881389","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: 8

Test case prioritization based on neural networks classification 基于神经网络分类的测试用例优先级

Proceedings of the 2nd ACM International Workshop on AI and Software Testing/Analysis Pub Date : 2022-07-18 DOI: 10.1145/3536168.3543300

C.M. Tiutin, A. Vescan

{"title":"Test case prioritization based on neural networks classification","authors":"C.M. Tiutin, A. Vescan","doi":"10.1145/3536168.3543300","DOIUrl":"https://doi.org/10.1145/3536168.3543300","url":null,"abstract":"Regression testing focuses on validating modified software, in order to detect if new errors were added into previously tested code and to provide confidence that modifications are correct. An approach that involves running all test cases would be time-consuming, however, test case prioritization plans an execution order of the test cases as an attempt to achieve the regression testing goals early in the testing phase. In this paper, we propose a Test Case Prioritization based on Neural Networks Classification (TCP-NNC) approach to be further used in the test case prioritization strategy. The proposed approach incorporates among other factors, the associations between requirements, tests and discovered faults, based on which an artificial neural network is trained, in order to be able to predict priorities for new test cases. The proposal is evaluated through experiments designed on both a real and a synthetic dataset, considering two different sets of features with different neural network architectures. The metrics observed include accuracy, precision and recall, while their results imply that the proposed method is feasible and effective. Among the proposed models, the one with Adam optimizer and three-layered architecture is the best obtained. Statistical tests are also used to compare various proposed models from various perspectives: NN architecture, optimizer, number of used features, used dataset and validation method.","PeriodicalId":287847,"journal":{"name":"Proceedings of the 2nd ACM International Workshop on AI and Software Testing/Analysis","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114765338","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

Proceedings of the 2nd ACM International Workshop on AI and Software Testing/Analysis 第二届ACM人工智能与软件测试/分析国际研讨会论文集

Proceedings of the 2nd ACM International Workshop on AI and Software Testing/Analysis Pub Date : 1900-01-01 DOI: 10.1145/3536168

引用次数: 0