2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE)最新文献_第3页

How Far Have We Come in Detecting Anomalies in Distributed Systems? An Empirical Study with a Statement-level Fault Injection Method 在检测分布式系统中的异常方面，我们已经走了多远?语句级故障注入方法的实证研究

2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE) Pub Date : 2020-10-01 DOI: 10.1109/ISSRE5003.2020.00015

Yong Yang, Yifan Wu, K. Pattabiraman, Long Wang, Ying Li

{"title":"How Far Have We Come in Detecting Anomalies in Distributed Systems? An Empirical Study with a Statement-level Fault Injection Method","authors":"Yong Yang, Yifan Wu, K. Pattabiraman, Long Wang, Ying Li","doi":"10.1109/ISSRE5003.2020.00015","DOIUrl":"https://doi.org/10.1109/ISSRE5003.2020.00015","url":null,"abstract":"Anomaly detection in distributed systems has been a fertile research area, and a range of anomaly detectors have been proposed for distributed systems. Unfortunately, there is no systematic quantitative study of the efficacy of different anomaly detectors, which is of great importance to reveal the deficiencies of existing anomaly detectors and shed light on future research directions. In this paper, we investigate how various anomaly detectors behave on anomalies of different types and the reasons for the same, by extensively injecting software faults into three widely-used distributed systems. We use a statementlevel fault injection method to observe the anomalies, characterize these anomalies, and analyze the detection results from anomaly detectors of three categories. We find that: (1) the distributed systems’ own error reporting mechanisms are able to report most of the anomalies (from 82.1% to 92.8%) but they incur a high false alarm rate of 26.6%. (2) State-of-the-art anomaly detectors are able to detect the existence of anomalies with 99.08% precision and 90.60% recall, but there is still a long way to go to pinpoint the accurate location of the detected anomalies, and (3) Log-based anomaly detection techniques outperform other anomaly detection techniques, but not for all anomaly types.","PeriodicalId":268556,"journal":{"name":"2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE)","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122797362","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

Modelling Machine Learning Components for Mapping and Scheduling of AUTOSAR Runnables 为AUTOSAR可运行项的映射和调度建模机器学习组件

2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE) Pub Date : 2020-10-01 DOI: 10.1109/ISSRE5003.2020.00021

M. Copic, R. Leupers, G. Ascheid

{"title":"Modelling Machine Learning Components for Mapping and Scheduling of AUTOSAR Runnables","authors":"M. Copic, R. Leupers, G. Ascheid","doi":"10.1109/ISSRE5003.2020.00021","DOIUrl":"https://doi.org/10.1109/ISSRE5003.2020.00021","url":null,"abstract":"The race towards autonomous driving provoked a paradigm shift as safety became a critical objective in the development of novel functionalities. The safety-critical part of these functionalities is predominantly realized in software complying to the AUTOSAR standard in which code fragments called runnables are configured at design-time to run according to a certain order and on a certain core. As a key technology that enables autonomous driving, machine learning is expected to play a significant role in automotive applications. Since machine learning algorithms inherently exhibit faults, e.g. a classifier’s prediction is wrong with a relatively high rate, to enforce safety, fault tolerance techniques have to be used. Therefore, this paper proposes that this information is systematically used in the automatic configuration of an AUTOSAR system. Not to disrupt the usual software development process, the information is appended to already mapped and scheduled runnables. Then, a heuristic is presented to generate execution alternatives during design-time which are then selected at run-time to skip the intervals reserved for fault tolerance mechanisms in the prevailing case when no fault occurred. This novel idea considerably reduces execution time as demonstrated on real-world engine control software.","PeriodicalId":268556,"journal":{"name":"2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131310914","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

SwissLog: Robust and Unified Deep Learning Based Log Anomaly Detection for Diverse Faults SwissLog:基于深度学习的鲁棒统一日志异常检测

2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE) Pub Date : 2020-10-01 DOI: 10.1109/ISSRE5003.2020.00018

Xiaoyun Li, Pengfei Chen, Linxiao Jing, Zilong He, Guangba Yu

引用次数: 56

Probabilistic Symbolic Analysis of Neural Networks 神经网络的概率符号分析

2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE) Pub Date : 2020-10-01 DOI: 10.1109/ISSRE5003.2020.00023

Hayes Converse, A. Filieri, D. Gopinath, C. Pasareanu

{"title":"Probabilistic Symbolic Analysis of Neural Networks","authors":"Hayes Converse, A. Filieri, D. Gopinath, C. Pasareanu","doi":"10.1109/ISSRE5003.2020.00023","DOIUrl":"https://doi.org/10.1109/ISSRE5003.2020.00023","url":null,"abstract":"Neural networks are powerful tools for automated decision-making, with applications ranging from image recognition to hiring decisions and safety-critical autonomous driving. However, due to their black-box nature and large scale, reasoning about their behavior is challenging. Statistical analysis is often used to infer probabilistic properties of a network, such as its robustness to noise and inaccurate inputs or the fairness of its decisions. While scalable, statistical methods can only provide probabilistic guarantees on the quality of their results and may underestimate the impact of low probability inputs leading to undesired behavior of the network.In this paper, we investigate the use of symbolic analysis and constraint solution space quantification to precisely quantify probabilistic properties in neural networks. We collect symbolic constraints corresponding to the network’s response to concrete inputs, while efficiently rejecting inputs whose responses have been seen before. We further propose a quantification procedure for the collected constraints, producing arbitrarily tight, sound interval bounds on the estimated probabilities. The proposed approach is an anytime algorithm, increasing in precision with more paths explored. We implemented our approach in SpaceScanner and demonstrate its potential in analyzing fairness, robustness, and sensitivity properties of neural networks.","PeriodicalId":268556,"journal":{"name":"2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128140991","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: 5

Cross-Project Aging-Related Bug Prediction Based on Joint Distribution Adaptation and Improved Subclass Discriminant Analysis 基于联合分布自适应和改进子类判别分析的跨项目老化相关Bug预测

2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE) Pub Date : 2020-10-01 DOI: 10.1109/ISSRE5003.2020.00038

Bin Xu, Dongdong Zhao, Kai Jia, Junwei Zhou, Jing Tian, Jianwen Xiang

{"title":"Cross-Project Aging-Related Bug Prediction Based on Joint Distribution Adaptation and Improved Subclass Discriminant Analysis","authors":"Bin Xu, Dongdong Zhao, Kai Jia, Junwei Zhou, Jing Tian, Jianwen Xiang","doi":"10.1109/ISSRE5003.2020.00038","DOIUrl":"https://doi.org/10.1109/ISSRE5003.2020.00038","url":null,"abstract":"Software aging, which is caused by Aging-Related Bugs (ARBs), refers to the phenomenon of performance degradation and eventual crash in long running systems. In order to discover and remove ARBs, ARB prediction is proposed. However, due to the low presence and reproducing difficulty of ARBs, it is usually difficult to collect sufficient ARB data within a project. Therefore, cross-project ARB prediction is proposed as a solution to build the target project’s ARB predictor by using the labeled data from the source project. A key point for cross-project ARB prediction is to reduce distribution difference between source and target project. However, existing approaches mainly focus on the marginal distribution difference while somehow overlook the conditional distribution difference, and they mainly use random oversampling to alleviate the class imbalance which may lead to overfitting. To address these problems, we propose a new crossproject ARB prediction approach based on Joint Distribution Adaptation (JDA) and Improved Subclass Discriminant Analysis (ISDA), called JDA-ISDA. The key idea of JDA-ISDA is first to use JDA to reduce the marginal distribution and conditional distribution difference jointly and then apply ISDA to alleviate the severe class imbalance problem. A set of experiments are carried out on two large open-source projects with six different machine learning (ML) classifiers. The experimental results demonstrate that compared with the state-of-the-art Transfer Learning based Aging-related bug Prediction (TLAP) and Supervised Representation Learning Approach (SRLA), JDA-ISDA is much more robust to different ML classifiers than TLAP, and the average improvement in terms of the balance value can be achieved up to 31.8%, and JDA-ISDA also outperforms TLAP and SRLA on average when logistic regression is chosen as the classifier for best performance prediction.","PeriodicalId":268556,"journal":{"name":"2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128474098","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: 7

AV-FUZZER: Finding Safety Violations in Autonomous Driving Systems AV-FUZZER:发现自动驾驶系统中的安全违规行为

2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE) Pub Date : 2020-10-01 DOI: 10.1109/ISSRE5003.2020.00012

Guanpeng Li, Yiran Li, Saurabh Jha, Timothy Tsai, Michael B. Sullivan, S. Hari, Z. Kalbarczyk, R. Iyer

{"title":"AV-FUZZER: Finding Safety Violations in Autonomous Driving Systems","authors":"Guanpeng Li, Yiran Li, Saurabh Jha, Timothy Tsai, Michael B. Sullivan, S. Hari, Z. Kalbarczyk, R. Iyer","doi":"10.1109/ISSRE5003.2020.00012","DOIUrl":"https://doi.org/10.1109/ISSRE5003.2020.00012","url":null,"abstract":"This paper proposes AV-FUZZER, a testing framework, to find the safety violations of an autonomous vehicle (AV) in the presence of an evolving traffic environment. We perturb the driving maneuvers of traffic participants to create situations in which an AV can run into safety violations. To optimally search for the perturbations to be introduced, we leverage domain knowledge of vehicle dynamics and genetic algorithm to minimize the safety potential of an AV over its projected trajectory. The values of the perturbation determined by this process provide parameters that define participants’ trajectories. To improve the efficiency of the search, we design a local fuzzer that increases the exploitation of local optima in the areas where highly likely safety-hazardous situations are observed. By repeating the optimization with significantly different starting points in the search space, AV-FUZZER determines several diverse AV safety violations. We demonstrate AV-FUZZER on an industrial-grade AV platform, Baidu Apollo, and find five distinct types of safety violations in a short period of time. In comparison, other existing techniques can find at most two. We analyze the safety violations found in Apollo and discuss their overarching causes.","PeriodicalId":268556,"journal":{"name":"2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122663195","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: 74

Correlating UI Contexts with Sensitive API Calls: Dynamic Semantic Extraction and Analysis 关联UI上下文与敏感API调用:动态语义提取和分析

2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE) Pub Date : 2020-10-01 DOI: 10.1109/ISSRE5003.2020.00031

Jie Liu, Dongjie He, Diyu Wu, Jingling Xue

{"title":"Correlating UI Contexts with Sensitive API Calls: Dynamic Semantic Extraction and Analysis","authors":"Jie Liu, Dongjie He, Diyu Wu, Jingling Xue","doi":"10.1109/ISSRE5003.2020.00031","DOIUrl":"https://doi.org/10.1109/ISSRE5003.2020.00031","url":null,"abstract":"The Android framework provides sensitive APIs for Android apps to access the user’s private information, e.g., SMS, call logs and locations. Whether a sensitive API call in an app is legitimate or not depends on whether the app has provided enough natural-language semantics to reflect the need for the permission. The prior efforts on analyzing description-to-permission fidelity in an app are all static. Some check whether the permissions requested (or sensitive APIs used) by the app are consistent with the functionalities described by the app. These app-level techniques are too coarse-grained, as they cannot tell if a sensitive API call under a certain runtime context, such as a UI state, is legitimate or not. Others attempt to establish this connection by performing a data-flow analysis, but such finegrained API-level static analyses are too imprecise to handle a variety of dynamic language features used in Android apps, including dynamic class loading, reflection and code obfuscation.We introduce APICOG, an automated fine-grained API-level approach, representing the first dynamic description-to-permission fidelity analysis for an Android app that can check if a sensitive API call is legitimate or not under a given runtime context. APICOG relates each sensitive API call with a UI state, called its UI context, under which the call is made via dynamic analysis and then extracts the text-based semantics for each UI context from its associated text- and image-typed attributes by applying a natural language processing (NLP) technique. Finally, APICOG relies on machine-learning to deduce if a sensitive API call under a UI context is legitimate or not. We have evaluated APICOG with thousands of Android apps drawn from a third-party market and a malware dataset, achieving an accuracy of 97.7%, a precision of 94.1% and a recall of 92.8% overall, outperforming the prior art in all the three metrics.","PeriodicalId":268556,"journal":{"name":"2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123015129","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: 3

Fault Triggers in the TensorFlow Framework: An Experience Report TensorFlow框架中的故障触发器:经验报告

2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE) Pub Date : 2020-10-01 DOI: 10.1109/ISSRE5003.2020.00010

Xiaoting Du, Guanping Xiao, Yulei Sui

{"title":"Fault Triggers in the TensorFlow Framework: An Experience Report","authors":"Xiaoting Du, Guanping Xiao, Yulei Sui","doi":"10.1109/ISSRE5003.2020.00010","DOIUrl":"https://doi.org/10.1109/ISSRE5003.2020.00010","url":null,"abstract":"TensorFlow is one of the most popular machine learning frameworks for developing machine learning algorithms. Because of the popularity and large-scale use of TensorFlow, even a single bug may lead to severe consequences and impact a large number of users. With a growing number of safety-critical systems built upon TensorFlow, its reliability is becoming increasingly important. An essential step to ensure TensorFlow’s reliability is to understand the characteristics of bugs that occurred in TensorFlow. This paper presents the first comprehensive empirical study on fault triggering conditions in TensorFlow. 2,285 bug reports from TensorFlow’s GitHub repository are collected. A bug classification is performed based on fault triggering conditions, followed by the frequency distribution of different types of bugs and the evolution features of varying bug types over time. Then the relationships between bug types and fixing time are also investigated. In addition, the root causes of Bohrbugs and Mandelbugs are studied. Five root causes are discovered. Furthermore, the analysis of regression bugs in TensorFlow is conducted. We have revealed 10 important findings based on our empirical results. There are 8 implications based on these findings are provided for developers and users.","PeriodicalId":268556,"journal":{"name":"2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115188964","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: 16

HINDBR: Heterogeneous Information Network Based Duplicate Bug Report Prediction 基于异构信息网络的重复Bug报告预测

2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE) Pub Date : 2020-10-01 DOI: 10.1109/ISSRE5003.2020.00027

Guanping Xiao, Xiaoting Du, Yulei Sui, Tao Yue

{"title":"HINDBR: Heterogeneous Information Network Based Duplicate Bug Report Prediction","authors":"Guanping Xiao, Xiaoting Du, Yulei Sui, Tao Yue","doi":"10.1109/ISSRE5003.2020.00027","DOIUrl":"https://doi.org/10.1109/ISSRE5003.2020.00027","url":null,"abstract":"Duplicate bug reports often exist in bug tracking systems (BTSs). Almost all the existing approaches for automatically detecting duplicate bug reports are based on text similarity. A recent study found that such approaches may become ineffective in detecting duplicates in bug reports submitted after the just-in-time (JIT) retrieval, which is now a built-in feature of modern BTSs (e.g., Bugzilla). This is mainly because the embedded JIT feature suggests possible duplicates in a bug database when a bug reporter types in the new summary field, therefore minimizing the submission of textually similar reports. Although JIT filtering seems effective, a number of bug report duplicates remain undetected. Our hypothesis is that we can detect them using a semantic similarity-based approach.This paper presents HINDBR, a novel deep neural network (DNN) that accurately detects semantically similar duplicate bug reports using a heterogeneous information network (HIN). Instead of matching text similarity alone, HINDBR embeds semantic relations of bug reports into a low-dimensional embedding space where two duplicate bug reports represented by two vectors are close to each other in the latent space. Results show that HINDBR is effective.","PeriodicalId":268556,"journal":{"name":"2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE)","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117109424","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: 14

ISSRE 2020 Program Committees ISSRE 2020计划委员会

2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE) Pub Date : 2020-10-01 DOI: 10.1109/issre5003.2020.00007

Nuno Antunes

引用次数: 0