Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering最新文献_第5页

A fault injection platform for learning AIOps models 一个学习AIOps模型的故障注入平台

Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering Pub Date : 2022-10-10 DOI: 10.1145/3551349.3559503

F. Bagehorn, Jesus Rios, Saurabh Jha, Robert Filepp, L. Shwartz, Naoki Abe, Xi Yang

{"title":"A fault injection platform for learning AIOps models","authors":"F. Bagehorn, Jesus Rios, Saurabh Jha, Robert Filepp, L. Shwartz, Naoki Abe, Xi Yang","doi":"10.1145/3551349.3559503","DOIUrl":"https://doi.org/10.1145/3551349.3559503","url":null,"abstract":"In today’s IT environment with a growing number of costly outages, increasing complexity of the systems, and availability of massive operational data, there is a strengthening demand to effectively leverage Artificial Intelligence and Machine Learning (AI/ML) towards enhanced resiliency. In this paper, we present an automatic fault injection platform to enable and optimize the generation of data needed for building AI/ML models to support modern IT operations. The merits of our platform include the ease of use, the possibility to orchestrate complex fault scenarios and to optimize the data generation for the modeling task at hand. Specifically, we designed a fault injection service that (i) combines fault injection with data collection in a unified framework, (ii) supports hybrid and multi-cloud environments, and (iii) does not require programming skills for its use. Our current implementation covers the most common fault types both at the application and infrastructure levels. The platform also includes some AI capabilities. In particular, we demonstrate the interventional causal learning capability currently available in our platform. We show how our system is able to learn a model of error propagation in a micro-service application in a cloud environment (when the communication graph among micro-services is unknown and only logs are available) for use in subsequent applications such as fault localization.","PeriodicalId":197939,"journal":{"name":"Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering","volume":"41 8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122037721","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

A Study of User Privacy in Android Mobile AR Apps Android手机AR应用中的用户隐私问题研究

Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering Pub Date : 2022-10-10 DOI: 10.1145/3551349.3560512

Xiaoyi Yang, Xueling Zhang

引用次数: 2

DeepPerform: An Efficient Approach for Performance Testing of Resource-Constrained Neural Networks DeepPerform:一种有效的资源约束神经网络性能测试方法

Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering Pub Date : 2022-10-10 DOI: 10.1145/3551349.3561158

Simin Chen, Mirazul Haque, Cong Liu, Wei Yang

{"title":"DeepPerform: An Efficient Approach for Performance Testing of Resource-Constrained Neural Networks","authors":"Simin Chen, Mirazul Haque, Cong Liu, Wei Yang","doi":"10.1145/3551349.3561158","DOIUrl":"https://doi.org/10.1145/3551349.3561158","url":null,"abstract":"Today, an increasing number of Adaptive Deep Neural Networks (AdNNs) are being used on resource-constrained embedded devices. We observe that, similar to traditional software, redundant computation exists in AdNNs, resulting in considerable performance degradation. The performance degradation is dependent on the input and is referred to as input-dependent performance bottlenecks (IDPBs). To ensure an AdNN satisfies the performance requirements of resource-constrained applications, it is essential to conduct performance testing to detect IDPBs in the AdNN. Existing neural network testing methods are primarily concerned with correctness testing, which does not involve performance testing. To fill this gap, we propose DeepPerform, a scalable approach to generate test samples to detect the IDPBs in AdNNs. We first demonstrate how the problem of generating performance test samples detecting IDPBs can be formulated as an optimization problem. Following that, we demonstrate how DeepPerform efficiently handles the optimization problem by learning and estimating the distribution of AdNNs’ computational consumption. We evaluate DeepPerform on three widely used datasets against five popular AdNN models. The results show that DeepPerform generates test samples that cause more severe performance degradation (FLOPs: increase up to 552%). Furthermore, DeepPerform is substantially more efficient than the baseline methods in generating test inputs (runtime overhead: only 6–10 milliseconds).","PeriodicalId":197939,"journal":{"name":"Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering","volume":"121 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122652387","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: 9

Coverage-based Greybox Fuzzing with Pointer Monitoring for C Programs 基于覆盖率的灰盒模糊与指针监控的C程序

Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering Pub Date : 2022-10-10 DOI: 10.1145/3551349.3559566

Haibo Chen, Jinfu Chen

引用次数: 1

ASTOR: An Approach to Identify Security Code Reviews ASTOR:一种识别安全代码审查的方法

Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering Pub Date : 2022-10-10 DOI: 10.1145/3551349.3559509

Rajshakhar Paul

引用次数: 0

RESTCluster: Automated Crash Clustering for RESTful API RESTCluster: RESTful API的自动崩溃集群

Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering Pub Date : 2022-10-10 DOI: 10.1145/3551349.3559511

Yi Liu

引用次数: 2

Global Decision Making Over Deep Variability in Feedback-Driven Software Development 反馈驱动软件开发中深度可变性的全局决策

Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering Pub Date : 2022-10-10 DOI: 10.1145/3551349.3559551

J. Kienzle, B. Combemale, G. Mussbacher, Omar Alam, F. Bordeleau, Lola Burgueño, G. Engels, Jessie Galasso, J. Jézéquel, Bettina Kemme, Sébastien Mosser, H. Sahraoui, Maximilian Schiedermeier, Eugene Syriani

{"title":"Global Decision Making Over Deep Variability in Feedback-Driven Software Development","authors":"J. Kienzle, B. Combemale, G. Mussbacher, Omar Alam, F. Bordeleau, Lola Burgueño, G. Engels, Jessie Galasso, J. Jézéquel, Bettina Kemme, Sébastien Mosser, H. Sahraoui, Maximilian Schiedermeier, Eugene Syriani","doi":"10.1145/3551349.3559551","DOIUrl":"https://doi.org/10.1145/3551349.3559551","url":null,"abstract":"To succeed with the development of modern software, organizations must have the agility to adapt faster to constantly evolving environments to deliver more reliable and optimized solutions that can be adapted to the needs and environments of their stakeholders including users, customers, business, development, and IT. However, stakeholders do not have sufficient automated support for global decision making, considering the increasing variability of the solution space, the frequent lack of explicit representation of its associated variability and decision points, and the uncertainty of the impact of decisions on stakeholders and the solution space. This leads to an ad-hoc decision making process that is slow, error-prone, and often favors local knowledge over global, organization-wide objectives. The Multi-Plane Models and Data (MP-MODA) framework explicitly represents and manages variability, impacts, and decision points. It enables automation and tool support in aid of a multi-criteria decision making process involving different stakeholders within a feedback-driven software development process where feedback cycles aim to reduce uncertainty. We present the conceptual structure of the framework, discuss its potential benefits, and enumerate key challenges related to tool supported automation and analysis within MP-MODA.","PeriodicalId":197939,"journal":{"name":"Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128507143","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

Automatically Fixing Breaking Changes of Data Science Libraries 自动修复数据科学库的破坏性更改

Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering Pub Date : 2022-10-10 DOI: 10.1145/3551349.3559507

Hailie Mitchell

引用次数: 0

Privacy Analysis of Period Tracking Mobile Apps in the Post-Roe v. Wade Era 后罗伊诉韦德时代时期跟踪移动应用程序的隐私分析

Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering Pub Date : 2022-10-10 DOI: 10.1145/3551349.3561343

Zikan Dong, Liu Wang, Hao Xie, Guoai Xu, Haoyu Wang

{"title":"Privacy Analysis of Period Tracking Mobile Apps in the Post-Roe v. Wade Era","authors":"Zikan Dong, Liu Wang, Hao Xie, Guoai Xu, Haoyu Wang","doi":"10.1145/3551349.3561343","DOIUrl":"https://doi.org/10.1145/3551349.3561343","url":null,"abstract":"To help people manage their health, period tracking apps have become very popular in recent years. However, the U.S. Supreme Court overturned Roe v. Wade on June 24, 2022. Abortion will be banned in more and more states. Since the health data stored in the period tracking apps can be used to infer whether the user has had or is considering an abortion, mobile users are worrying that these apps may disclose their sensitive information, which can be used to prosecute users. Although period tracking apps have received attention from the research community, no existing work has performed a systematic privacy analysis of these apps, especially in the Post-Roe v. Wade era. To fill the void, this paper presents a comprehensive privacy analysis of popular period tracking apps. We first collect 35 popular period tracking apps from Google Play. Then, we analyze the sensitive user data collected by the period tracking apps using traffic analysis and static analysis. Further we inspect their privacy policies and check the consistency of the privacy policy with the app’s behavior. In addition, we analyze the app reviews to understand the users’ concerns about the period tracking apps. Our study reveals that some period tracking apps have indeed collected sensitive information and have the potential to share the data with third-party authorities. It is urgent for these apps to take action to protect user privacy, and mobile users should pay special attention to this kind of apps they used.","PeriodicalId":197939,"journal":{"name":"Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131312213","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: 4

Call Graph Evolution Analytics over a Version Series of an Evolving Software System 演化软件系统版本系列的呼叫图演化分析

Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering Pub Date : 2022-10-10 DOI: 10.1145/3551349.3559573

Animesh Chaturvedi

{"title":"Call Graph Evolution Analytics over a Version Series of an Evolving Software System","authors":"Animesh Chaturvedi","doi":"10.1145/3551349.3559573","DOIUrl":"https://doi.org/10.1145/3551349.3559573","url":null,"abstract":"Software evolution analytics can be supported by generating and comparing call graph evolution information over versions of a software system. Call Graph evolution analytics can assist a software engineer when maintaining or evolving a software system. This paper proposes Call Graph Evolution Analytics to extract information from a set of Evolving Call Graphs ECG = {CG1, CG2,...CGN} representing a Version Series VS = {V1, V2,...VN} of an evolving software system. This is done using Call Graph Evolution Rules (CGERs) and Call Graph Evolution Subgraphs (CGESs). Similar to association rule mining, the CGERs are used to capture co-occurrences of dependencies in the system. Like subgraph patterns in a call graph, the CGESs are used to capture evolution of dependency patterns in evolving call graphs. Call graph analytics on the evolution in these patterns can identify potentially affected dependencies (or procedure calls) that need attention. The experiments are done on the evolving call graphs of 10 large evolving systems to support dependency evolution management. This is demonstrated with detailed results for evolving call graphs of Maven-Core’s version series.","PeriodicalId":197939,"journal":{"name":"Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering","volume":"1989 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120849134","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