2019 IEEE 30th International Symposium on Software Reliability Engineering (ISSRE)最新文献_第5页

Spotting Problematic Code Lines using Nonintrusive Programmers' Biofeedback 使用非侵入式程序员的生物反馈发现有问题的代码行

2019 IEEE 30th International Symposium on Software Reliability Engineering (ISSRE) Pub Date : 2019-10-01 DOI: 10.1109/ISSRE.2019.00019

R. Couceiro, P. Carvalho, M. C. Branco, H. Madeira, R. Barbosa, J. Durães, G. Duarte, J. Castelhano, C. Duarte, C. Teixeira, N. Laranjeiro, J. Medeiros

{"title":"Spotting Problematic Code Lines using Nonintrusive Programmers' Biofeedback","authors":"R. Couceiro, P. Carvalho, M. C. Branco, H. Madeira, R. Barbosa, J. Durães, G. Duarte, J. Castelhano, C. Duarte, C. Teixeira, N. Laranjeiro, J. Medeiros","doi":"10.1109/ISSRE.2019.00019","DOIUrl":"https://doi.org/10.1109/ISSRE.2019.00019","url":null,"abstract":"Recent studies have shown that programmers' cognitive load during typical code development activities can be assessed using wearable and low intrusive devices that capture peripheral physiological responses driven by the autonomic nervous system. In particular, measures such as heart rate variability (HRV) and pupillography can be acquired by nonintrusive devices and provide accurate indication of programmers' cognitive load and attention level in code related tasks, which are known elements of human error that potentially lead to software faults. This paper presents an experimental study designed to evaluate the possibility of using HRV and pupillography together with eye tracking to identify and annotate specific code lines (or even finer grain lexical tokens) of the program under development (or under inspection) with information on the cognitive load of the programmer while dealing with such lines of code. The experimental data is discussed in the paper to assess different alternatives for using code annotations representing programmers' cognitive load while producing or reading code. In particular, we propose the use of biofeedback code highlighting techniques to provide online programmer's warnings for potentially problematic code lines that may need a second look at (to remove possible bugs), and biofeedback-driven software testing to optimize testing effort, focusing the tests on code areas with higher bug probability","PeriodicalId":254749,"journal":{"name":"2019 IEEE 30th International Symposium on Software Reliability Engineering (ISSRE)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132110444","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: 10

Message from Program Co-chairs 项目联合主席的信息

2019 IEEE 30th International Symposium on Software Reliability Engineering (ISSRE) Pub Date : 2019-10-01 DOI: 10.1109/ICECCS.2012.47

I. Perseil, Marc Pouzet, K. Breitman

引用次数: 0

Enhancing Failure Propagation Analysis in Cloud Computing Systems 增强云计算系统中的故障传播分析

2019 IEEE 30th International Symposium on Software Reliability Engineering (ISSRE) Pub Date : 2019-08-30 DOI: 10.1109/ISSRE.2019.00023

Domenico Cotroneo, L. Simone, Pietro Liguori, R. Natella, N. Bidokhti

引用次数: 11

Analyzing the Context of Bug-Fixing Changes in the OpenStack Cloud Computing Platform OpenStack云计算平台的bug修复变更背景分析

2019 IEEE 30th International Symposium on Software Reliability Engineering (ISSRE) Pub Date : 2019-08-29 DOI: 10.1109/ISSRE.2019.00041

Domenico Cotroneo, L. Simone, Antonio Ken Iannillo, R. Natella, Stefano Rosiello, N. Bidokhti

{"title":"Analyzing the Context of Bug-Fixing Changes in the OpenStack Cloud Computing Platform","authors":"Domenico Cotroneo, L. Simone, Antonio Ken Iannillo, R. Natella, Stefano Rosiello, N. Bidokhti","doi":"10.1109/ISSRE.2019.00041","DOIUrl":"https://doi.org/10.1109/ISSRE.2019.00041","url":null,"abstract":"Many research areas in software engineering, such as mutation testing, automatic repair, fault localization, and fault injection, rely on empirical knowledge about recurring bug-fixing code changes. Previous studies in this field focus on what has been changed due to bug-fixes, such as in terms of code edit actions. However, such studies did not consider where the bug-fix change was made (i.e., the context of the change), but knowing about the context can potentially narrow the search space for many software engineering techniques (e.g., by focusing mutation only on specific parts of the software). Furthermore, most previous work on bug-fixing changes focused on C and Java projects, but there is little empirical evidence about Python software. Therefore, in this paper we perform a thorough empirical analysis of bug-fixing changes in three OpenStack projects, focusing on both the what and the where of the changes. We observed that all the recurring change patterns are not oblivious with respect to the surrounding code, but tend to occur in specific code contexts.","PeriodicalId":254749,"journal":{"name":"2019 IEEE 30th International Symposium on Software Reliability Engineering (ISSRE)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124652991","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: 6

Assessing the Safety and Reliability of Autonomous Vehicles from Road Testing 从道路测试中评估自动驾驶汽车的安全性和可靠性

2019 IEEE 30th International Symposium on Software Reliability Engineering (ISSRE) Pub Date : 2019-07-20 DOI: 10.1109/ISSRE.2019.00012

Xingyu Zhao, V. Robu, D. Flynn, K. Salako, L. Strigini

{"title":"Assessing the Safety and Reliability of Autonomous Vehicles from Road Testing","authors":"Xingyu Zhao, V. Robu, D. Flynn, K. Salako, L. Strigini","doi":"10.1109/ISSRE.2019.00012","DOIUrl":"https://doi.org/10.1109/ISSRE.2019.00012","url":null,"abstract":"There is an urgent societal need to assess whether autonomous vehicles (AVs) are safe enough. From published quantitative safety and reliability assessments of AVs, we know that, given the goal of predicting very low rates of accidents, road testing alone requires infeasible numbers of miles to be driven. However, previous analyses do not consider any knowledge prior to road testing – knowledge which could bring substantial advantages if the AV design allows strong expectations of safety before road testing. We present the advantages of a new variant of Conservative Bayesian Inference (CBI), which uses prior knowledge while avoiding optimistic biases. We then study the trend of disengagements (take-overs by human drivers) by applying Software Reliability Growth Models (SRGMs) to data from Waymo's public road testing over 51 months, in view of the practice of software updates during this testing. Our approach is to not trust any specific SRGM, but to assess forecast accuracy and then improve forecasts. We show that, coupled with accuracy assessment and recalibration techniques, SRGMs could be a valuable test planning aid.","PeriodicalId":254749,"journal":{"name":"2019 IEEE 30th International Symposium on Software Reliability Engineering (ISSRE)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133268754","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: 42

TamperNN: Efficient Tampering Detection of Deployed Neural Nets TamperNN:部署神经网络的有效篡改检测

2019 IEEE 30th International Symposium on Software Reliability Engineering (ISSRE) Pub Date : 2019-03-01 DOI: 10.1109/ISSRE.2019.00049

E. L. Merrer, Gilles Trédan

{"title":"TamperNN: Efficient Tampering Detection of Deployed Neural Nets","authors":"E. L. Merrer, Gilles Trédan","doi":"10.1109/ISSRE.2019.00049","DOIUrl":"https://doi.org/10.1109/ISSRE.2019.00049","url":null,"abstract":"Neural networks are powering the deployment of embedded devices and Internet of Things. Applications range from personal assistants to critical ones such as self-driving cars. It has been shown recently that models obtained from neural nets can be trojaned; an attacker can then trigger an arbitrary model behavior facing crafted inputs. This has a critical impact on the security and reliability of those deployed devices. We introduce novel algorithms to detect the tampering with deployed models, classifiers in particular. In the remote interaction setup we consider, the proposed strategy is to identify markers of the model input space that are likely to change class if the model is attacked, allowing a user to detect a possible tampering. This setup makes our proposal compatible with a wide range of scenarios, such as embedded models, or models exposed through prediction APIs. We experiment those tampering detection algorithms on the canonical MNIST dataset, over three different types of neural nets, and facing five different attacks (trojaning, quantization, fine-tuning, compression and watermarking). We then validate over five large models (VGG16, VGG19, ResNet, MobileNet, DenseNet) with a state of the art dataset (VGGFace2), and report results demonstrating the possibility of an efficient detection of model tampering.","PeriodicalId":254749,"journal":{"name":"2019 IEEE 30th International Symposium on Software Reliability Engineering (ISSRE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130191025","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: 6

TripleAgent: Monitoring, Perturbation and Failure-Obliviousness for Automated Resilience Improvement in Java Applications TripleAgent: Java应用程序中自动弹性改进的监控、扰动和故障遗忘

2019 IEEE 30th International Symposium on Software Reliability Engineering (ISSRE) Pub Date : 2018-12-27 DOI: 10.1109/ISSRE.2019.00021

Long Zhang, Monperrus Martin

引用次数: 7