2021 IEEE 32nd International Symposium on Software Reliability Engineering (ISSRE)最新文献_第4页

External Reviewers ISSRE 2021 外部审稿人ISSRE 2021

2021 IEEE 32nd International Symposium on Software Reliability Engineering (ISSRE) Pub Date : 2021-10-01 DOI: 10.1109/issre52982.2021.00011

引用次数: 0

Improving Code Summarization Through Automated Quality Assurance 通过自动化质量保证改进代码总结

2021 IEEE 32nd International Symposium on Software Reliability Engineering (ISSRE) Pub Date : 2021-10-01 DOI: 10.1109/ISSRE52982.2021.00057

Yuxing Hu, Meng Yan, Zhongxin Liu, Qiuyuan Chen, Bei Wang

{"title":"Improving Code Summarization Through Automated Quality Assurance","authors":"Yuxing Hu, Meng Yan, Zhongxin Liu, Qiuyuan Chen, Bei Wang","doi":"10.1109/ISSRE52982.2021.00057","DOIUrl":"https://doi.org/10.1109/ISSRE52982.2021.00057","url":null,"abstract":"The code summarization task aims to generate brief descriptions of source code automatically. It is beneficial for developers to understand source code. However, almost all of current code summarization approaches may generate low-quality (BLEU4<40) summaries, which will mislead developers. Previous work has shown that it is possible to conduct quality assurance for document generation (QA4DG) and improve the practicability of document generation approaches. Code summarization can also be regarded as a document generation task. This work aims to investigate whether QA4DG approaches can be leveraged to improve code summarization. Specifically, we first investigate whether existing QA4DG approaches can be plugged in code summarization approaches. We find that an automated quality assurance framework for commit message generation named QACom performs best. In-spired by the idea behind QAcom, we propose an ensemble code summarization approach called Ensum. Precisely, given a code snippet, Ensum first uses current code summarization approaches to generate candidate summaries. Then, Ensum predicts the quality of each candidate summary using a collaborative filtering-based component and a retrieval-based component and selects the best candidate summary as the output. Experimental results on two public datasets show that Ensum outperforms three state-of-the-art single approaches and one ensemble approach for code summarization in terms of BLEU-4, METEOR, and ROUGE-L.","PeriodicalId":162410,"journal":{"name":"2021 IEEE 32nd International Symposium on Software Reliability Engineering (ISSRE)","volume":"159 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":"116412690","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

Dependency-aware Form Understanding 依赖性表单理解

2021 IEEE 32nd International Symposium on Software Reliability Engineering (ISSRE) Pub Date : 2021-10-01 DOI: 10.1109/ISSRE52982.2021.00026

Shaokun Zhang, Yuanchun Li, Weixiang Yan, Yao Guo, Xiangqun Chen

{"title":"Dependency-aware Form Understanding","authors":"Shaokun Zhang, Yuanchun Li, Weixiang Yan, Yao Guo, Xiangqun Chen","doi":"10.1109/ISSRE52982.2021.00026","DOIUrl":"https://doi.org/10.1109/ISSRE52982.2021.00026","url":null,"abstract":"Form understanding is an important task in many fields such as software testing, AI assistants, and improving accessibility. One key goal of understanding a complex set of forms is to identify the dependencies between form elements. However, it remains a challenge to capture the dependencies accurately due to the diversity of UI design patterns and the variety in development experiences. In this paper, we propose a deep-learning-based approach called DependEX, which integrates convolutional neural networks (CNNs) and transformers to help understand dependencies within forms. DependEX extracts semantic features from UI images using CNN-based models, captures contextual patterns using a multilayer transformer encoder module, and models dependencies between form elements using two embedding layers. We evaluate DependEX with a large-scale dataset from mobile Web applications. Experimental results show that our proposed model achieves over 92% accuracy in identifying dependencies between UI elements, which significantly outperforms other competitive methods, especially for heuristic-based methods. We also conduct case studies on automatic form filling and test case generation from natural language (NL) instructions, which demonstrates the applicability of our approach.","PeriodicalId":162410,"journal":{"name":"2021 IEEE 32nd International Symposium on Software Reliability Engineering (ISSRE)","volume":"8 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":"133677570","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: 2

Lessons Learned from the Development of a Mechanical Ventilator for COVID-19 新型冠状病毒肺炎机械呼吸机研制经验教训

2021 IEEE 32nd International Symposium on Software Reliability Engineering (ISSRE) Pub Date : 2021-10-01 DOI: 10.1109/ISSRE52982.2021.00016

A. Bombarda, S. Bonfanti, C. Galbiati, A. Gargantini, Patrizio Pelliccione, E. Riccobene, Masayuki Wada

{"title":"Lessons Learned from the Development of a Mechanical Ventilator for COVID-19","authors":"A. Bombarda, S. Bonfanti, C. Galbiati, A. Gargantini, Patrizio Pelliccione, E. Riccobene, Masayuki Wada","doi":"10.1109/ISSRE52982.2021.00016","DOIUrl":"https://doi.org/10.1109/ISSRE52982.2021.00016","url":null,"abstract":"During the COVID-19 pandemic, many researchers all over the world have offered their time and competencies to face the heavy consequences of the disease. This is the case of a group of physicists, engineers, and physicians that around the middle of March 2020 started to develop a simplified mechanical lung ventilator, called MVM (Mechanical Ventilator Milano), to answer the high request of ventilators for Acute Respiratory Distress Syndrome (ARDS) in intensive care units. A prototype was ready in around one month. Since medical software malfunctions can lead to injuries or death of patients, before marketing MVM ventilators and distributing them in hospitals, software certification in accordance with the IEC 62304 standard was mandatory to guarantee system reliability. The team was then complemented by computer scientists specifically devoted to this task. The software re-engineering process, which lasted around two months from the end of the prototype, brought to a strong re-implementation of the device software components, which involved all the stakeholders in a continuous integration setting. In this paper, we report the experience of the MVM control SW re-engineering necessary to show evidence that the SW adheres to the standards and to consequently obtain the certification. We share results and lessons learned from this social project, where more than 100 volunteer researchers worked towards software certification at the extreme of their strength to get a real device finished in a rush since strongly required to support physicians in treating COVID-19 patients.","PeriodicalId":162410,"journal":{"name":"2021 IEEE 32nd International Symposium on Software Reliability Engineering (ISSRE)","volume":"37 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":"124828158","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

How Long Will it Take to Mitigate this Incident for Online Service Systems? 在线服务系统需要多长时间才能缓解此事件?

2021 IEEE 32nd International Symposium on Software Reliability Engineering (ISSRE) Pub Date : 2021-10-01 DOI: 10.1109/ISSRE52982.2021.00017

Weijing Wang, Junjie Chen, Lin Yang, Hongyu Zhang, Pu Zhao, Bo Qiao, Yu Kang, Qingwei Lin, S. Rajmohan, Feng Gao, Zhangwei Xu, Yingnong Dang, D. Zhang

{"title":"How Long Will it Take to Mitigate this Incident for Online Service Systems?","authors":"Weijing Wang, Junjie Chen, Lin Yang, Hongyu Zhang, Pu Zhao, Bo Qiao, Yu Kang, Qingwei Lin, S. Rajmohan, Feng Gao, Zhangwei Xu, Yingnong Dang, D. Zhang","doi":"10.1109/ISSRE52982.2021.00017","DOIUrl":"https://doi.org/10.1109/ISSRE52982.2021.00017","url":null,"abstract":"Online service systems may encounter a large number of incidents, which should be mitigated as soon as possible to minimize the service disruption time and ensure high service availability. The ability to predict TTM (Time To Mitigation) of incidents can help service teams better organize the mainte-nance efforts. Although there are many traditional bug-fixing time prediction methods, we find that there are not readily available for incident- TTM prediction due to the characteristics of incidents. To better understand how incidents are mitigated, we conduct the first empirical study of incident TTM on 20 large-scale online service systems in Microsoft. We investigate the time distribution in the main stages of the incident life cycle and explore factors affecting TTM. Based on our empirical findings, we propose TTMPred, a deep-learning-based approach for incident- TTM prediction in a continuous triage scenario. Our model designs a two-level attention-based bidirectional GRU model to capture both the semantic information in text data and the temporal information in incremental discussions. And based on a novel continuous loss function, it builds a regression model to achieve accurate TTM prediction as much as possible at each time point of prediction. Our experiments on four large-scale online service systems in Microsoft show that TTMPred is effective and significantly outperforms the compared approaches. For example, TTMPred improves the state-of-the-art regression-based approach by 25.66% on average in terms of MAE (Mean Absolute Error).","PeriodicalId":162410,"journal":{"name":"2021 IEEE 32nd International Symposium on Software Reliability Engineering (ISSRE)","volume":"24 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":"121745873","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

ReFixar: Multi-version Reasoning for Automated Repair of Regression Errors 修正:自动修复回归错误的多版本推理

2021 IEEE 32nd International Symposium on Software Reliability Engineering (ISSRE) Pub Date : 2021-10-01 DOI: 10.1109/ISSRE52982.2021.00028

X. Le, Quang Loc Le

{"title":"ReFixar: Multi-version Reasoning for Automated Repair of Regression Errors","authors":"X. Le, Quang Loc Le","doi":"10.1109/ISSRE52982.2021.00028","DOIUrl":"https://doi.org/10.1109/ISSRE52982.2021.00028","url":null,"abstract":"Software programs evolve naturally as part of the ever-changing customer needs and fast-paced market. Software evolution, however, often introduces regression bugs, which un-duly break previously working functionalities of the software. To repair regression bugs, one needs to know when and where a bug emerged from, e.g., the bug-inducing code changes, to narrow down the search space. Unfortunately, existing state-of-the-art automated program repair (APR) techniques have not yet fully exploited this information, rendering them less efficient and effective to navigate through a potentially large search space containing many plausible but incorrect solutions. In this work, we revisit APR on repairing regression errors in Java programs. We empirically show that existing state-of-the-art APR techniques do not perform well on regression bugs due to their algorithm design and lack of knowledge on bug inducing changes. We subsequently present ReFixar, a novel repair technique that leverages software evolution history to generate high quality patches for Java regression bugs. The key novelty that empowers ReFixar to more efficiently and effectively traverse the search space is two-fold: (1) A systematic way for multi-version reasoning to capture how a software evolves through its history, and (2) A novel search algorithm over a set of generic repair templates, derived from the principle of incorrectness logic and informed by both past bug fixes and their bug-inducing code changes; this enables ReFixar to achieve a balance of both genericity and specificity, i.e., generic common fix patterns of bugs and their specific contexts. We compare ReFixar against the state-of-the-art APR techniques on a data set of 51 real regression bugs from 28 large real-world programs. Experiments show that ReFixar significantly outperforms the best baseline by a large margin, i.e., ReFixar can fix correctly 24 bugs while the best baseline can only correctly fix 9 bugs.","PeriodicalId":162410,"journal":{"name":"2021 IEEE 32nd International Symposium on Software Reliability Engineering (ISSRE)","volume":"17 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":"115300255","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: 2

ISSRE 2021 Keynotes

2021 IEEE 32nd International Symposium on Software Reliability Engineering (ISSRE) Pub Date : 2021-10-01 DOI: 10.1109/issre52982.2021.00012

引用次数: 0

PyMTDEvaluator: A Tool for Time-Based Moving Target Defense Evaluation: Tool description paper PyMTDEvaluator:一个基于时间的移动目标防御评估工具:工具描述论文

2021 IEEE 32nd International Symposium on Software Reliability Engineering (ISSRE) Pub Date : 2021-10-01 DOI: 10.1109/ISSRE52982.2021.00045

Matheus Torquato, Paulo Maciel, M. Vieira

引用次数: 2

An Efficient Approximation for Quantitative Analysis of Dynamic Fault Trees 动态故障树定量分析的一种有效逼近方法

2021 IEEE 32nd International Symposium on Software Reliability Engineering (ISSRE) Pub Date : 2021-10-01 DOI: 10.1109/ISSRE52982.2021.00035

Luyao Ye, Erqing Li, Dongdong Zhao, Shengwu Xiong, Siwei Zhou, Jianwen Xiang

引用次数: 0

One Step Further: Investigating Problematic Files of Architecture Anti-patterns 更进一步:调查架构反模式的有问题文件

2021 IEEE 32nd International Symposium on Software Reliability Engineering (ISSRE) Pub Date : 2021-10-01 DOI: 10.1109/ISSRE52982.2021.00060

Jingwen Liu, Wuxia Jin, Qiong Feng, Xinyu Zhang, Yi-Yo Dai

引用次数: 2