2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE)最新文献_第2页

Model-Driven Fault Injection in Java Source Code Java源代码中的模型驱动故障注入

2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE) Pub Date : 2020-10-01 DOI: 10.1109/ISSRE5003.2020.00046

Elder Rodrigues, Leonardo Montecchi, A. Ceccarelli

{"title":"Model-Driven Fault Injection in Java Source Code","authors":"Elder Rodrigues, Leonardo Montecchi, A. Ceccarelli","doi":"10.1109/ISSRE5003.2020.00046","DOIUrl":"https://doi.org/10.1109/ISSRE5003.2020.00046","url":null,"abstract":"The injection of software faults in source code requires accurate knowledge of the programming language, both to craft faults and to identify injection locations. As such, fault injection and code mutation tools are typically tailored for a specific language and have limited extensibility. In this paper we present a model-driven approach to craft and inject software faults in source code. While its concrete application is presented for Java, the workflow we propose does not depend on a specific programming language. Following Model-Driven Engineering principles, the faults and the criteria to select injection locations are described using structured, machine-readable specifications based on a domain-specific language. Then, automated transformations craft artifacts based on OCL and Java, which represent the faults to be injected and are able to select the candidate injection locations. Finally, artifacts are executed against the target source code, performing the injection in the desired locations. We devise a supporting tool and exercise the approach injecting 13 different kinds of software faults in the Java source code of six different projects.","PeriodicalId":268556,"journal":{"name":"2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE)","volume":"306 4 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":"132556791","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

Connecting Software Reliability Growth Models to Software Defect Tracking 将软件可靠性增长模型与软件缺陷跟踪联系起来

2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE) Pub Date : 2020-10-01 DOI: 10.1109/ISSRE5003.2020.00022

Maskura Nafreen, Melanie Luperon, L. Fiondella, V. Nagaraju, Ying Shi, T. Wandji

{"title":"Connecting Software Reliability Growth Models to Software Defect Tracking","authors":"Maskura Nafreen, Melanie Luperon, L. Fiondella, V. Nagaraju, Ying Shi, T. Wandji","doi":"10.1109/ISSRE5003.2020.00022","DOIUrl":"https://doi.org/10.1109/ISSRE5003.2020.00022","url":null,"abstract":"Traditional software reliability growth models only consider defect discovery data, yet the practical concern of software engineers is the removal of these defects. Most attempts to model the relationship between defect discovery and resolution have been restricted to differential equation-based models associated with these two activities. However, defect tracking databases offer a practical source of information on the defect lifecycle suitable for more complete reliability and performance models. This paper explicitly connects software reliability growth models to software defect tracking. Data from a NASA project has been employed to develop differential equation-based models of defect discovery and resolution as well as distributional and Markovian models of defect resolution. The states of the Markov model represent thirteen unique stages of the NASA software defect lifecycle. Both state transition probabilities and transition time distributions are computed from the defect database. Illustrations compare the predictive and computational performance of alternative approaches. The results suggest that the simple distributional approach achieves the best tradeoff between these two performance measures, but that enhanced data collection practices could improve the utility of the more advanced approaches and the inferences they enable.","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":"124152994","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

Deep Learning Based Valid Bug Reports Determination and Explanation 基于深度学习的有效Bug报告、确定和解释

2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE) Pub Date : 2020-10-01 DOI: 10.1109/ISSRE5003.2020.00026

Jianjun He, Ling Xu, Yuanrui Fan, Zhou Xu, Meng Yan, Yan Lei

{"title":"Deep Learning Based Valid Bug Reports Determination and Explanation","authors":"Jianjun He, Ling Xu, Yuanrui Fan, Zhou Xu, Meng Yan, Yan Lei","doi":"10.1109/ISSRE5003.2020.00026","DOIUrl":"https://doi.org/10.1109/ISSRE5003.2020.00026","url":null,"abstract":"Bug reports are widely used by developers to fix bugs. Due to the lack of experience, reporters may submit numerous invalid bug reports. Manually determining valid bug reports is a laborious task. Automatically identifying valid bug reports can save time and effort for bug analysis. In this paper, we propose a deep learning-based approach to determine and explain valid bug reports using only textual information i.e., summaries and descriptions of bug reports. Convolutional neural network (CNN) is applied to capture their contextual and semantic features. Moreover, by analyzing the spatial structure of CNN, we backtrack the trained CNN model to get phrases that can explain valid bug reports determination. After inspecting the phrases manually, we summarize some valid bug report patterns. We evaluate our approach on five large-scale open-source projects containing a total of 540491 bug reports. On average, across the five projects, our approach achieves 0.85, 0.80, 0.69 and improves the state-of-the-art approach by 8.97%, 9.59%, 9.52% in terms of AUC, F1-score for valid bug reports, and F1-score for invalid bug reports, respectively. From the summarized patterns, we can find that determining valid bug reports is mainly due to three categories of patterns: Attachment, Environment, and Reproduce.","PeriodicalId":268556,"journal":{"name":"2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE)","volume":"21 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":"122171181","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

An Empirical Study of Thresholds for Code Measures 代码度量阈值的实证研究

2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE) Pub Date : 2020-10-01 DOI: 10.1109/ISSRE5003.2020.00040

L. Lavazza, S. Morasca

{"title":"An Empirical Study of Thresholds for Code Measures","authors":"L. Lavazza, S. Morasca","doi":"10.1109/ISSRE5003.2020.00040","DOIUrl":"https://doi.org/10.1109/ISSRE5003.2020.00040","url":null,"abstract":"Background. Practical use of software code measures for faultiness estimation often requires setting thresholds on measures, to separate software modules that are likely to be faulty from modules that are likely to be non-faulty. Several threshold proposals exist in the literature. However, different proposals may recommend different threshold values for the same measure, so practitioners may be unsure which threshold value they should use.Objective. Our goal is to investigate whether it is possible to define a single threshold for a code measure, or at least a small range within which to select a threshold.Method. We carried out an empirical study based on two collections of datasets available in the SEACRAFT repository. For each dataset, we built all statistically significant univariate Binary Logistic Regression fault-proneness models, using a set of the most commonly used code measures as independent variables. We then derived thresholds for single software measures by setting an acceptability threshold to fault-proneness. We then checked whether the distribution of the thresholds obtained for the same measure is concentrated enough that it is possible to take a value of the code measure as a “universal” threshold for it. We repeated the same method with bivariate Binary Logistic Regression fault-proneness models.Results. The threshold distributions we obtained are quite dispersed, so it does not seem to be possible to define “universal” thresholds for the code measures we considered.Conclusions. According to the collected evidence, it appears hardly possible to effectively control software faultiness by recommending that the most commonly used code measures satisfy fixed thresholds.","PeriodicalId":268556,"journal":{"name":"2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE)","volume":"10 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":"123808982","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

Understanding Reproducibility and Characteristics of Flaky Tests Through Test Reruns in Java Projects 通过Java项目中的测试重播了解片状测试的可重复性和特征

2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE) Pub Date : 2020-10-01 DOI: 10.1109/ISSRE5003.2020.00045

Wing Lam, Stefan Winter, Angello Astorga, V. Stodden, D. Marinov

{"title":"Understanding Reproducibility and Characteristics of Flaky Tests Through Test Reruns in Java Projects","authors":"Wing Lam, Stefan Winter, Angello Astorga, V. Stodden, D. Marinov","doi":"10.1109/ISSRE5003.2020.00045","DOIUrl":"https://doi.org/10.1109/ISSRE5003.2020.00045","url":null,"abstract":"Flaky tests are tests that can non-deterministically pass and fail. They pose a major impediment to regression testing, because they provide an inconclusive assessment on whether recent code changes contain faults or not. Prior studies of flaky tests have proposed tools to detect flaky tests and identified various sources of flakiness in tests, e.g., order-dependent (OD) tests that deterministically fail for some order of tests in a test suite but deterministically pass for some other orders. Several of these studies have focused on OD tests. We focus on an important and under-explored source of flakiness in tests: non-order-dependent tests that can nondeterministically pass and fail even for the same order of tests. Instead of using specialized tools that aim to detect flaky tests, we run tests using the tool configured by the developers. Specifically, we perform our empirical evaluation on Java projects that rely on the Maven Surefire plugin to run tests. We re-execute each test suite 4000 times, potentially in different test-class orders, and we label tests as flaky if our runs have both pass and fail outcomes across these reruns. We obtain a dataset of 107 flaky tests and study various characteristics of these tests. We find that many tests previously called “non-order-dependent” actually do depend on the order and can fail with very different failure rates for different orders.","PeriodicalId":268556,"journal":{"name":"2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE)","volume":"21 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":"125638886","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: 37

An Exploratory Study of Bugs in Extended Reality Applications on the Web Web上扩展现实应用程序中bug的探索性研究

2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE) Pub Date : 2020-10-01 DOI: 10.1109/ISSRE5003.2020.00025

Shuqing Li, Yechang Wu, Yi Liu, Dinghua Wang, Ming Wen, Yida Tao, Yulei Sui, Yepang Liu

{"title":"An Exploratory Study of Bugs in Extended Reality Applications on the Web","authors":"Shuqing Li, Yechang Wu, Yi Liu, Dinghua Wang, Ming Wen, Yida Tao, Yulei Sui, Yepang Liu","doi":"10.1109/ISSRE5003.2020.00025","DOIUrl":"https://doi.org/10.1109/ISSRE5003.2020.00025","url":null,"abstract":"Extended Reality (XR) technologies are becoming increasingly popular in recent years. To help developers deploy XR applications on the Web, W3C released the WebXR Device API in 2019, which enable users to interact with browsers using XR devices. Given the convenience brought by WebXR, a growing number of WebXR projects have been deployed in practice. However, many WebXR applications are insufficiently tested before being released. They suffer from various bugs that can degrade user experience or cause undesirable consequences. Yet, the community has limited understanding towards the bugs in the WebXR ecosystem, which impedes the advance of techniques for assuring the reliability of WebXR applications. To bridge this gap, we conducted the first empirical study of WebXR bugs. We collected 368 real bugs from 33 WebXR projects hosted on GitHub. Via a seven-round manual analysis of these bugs, we built a taxonomy of WebXR bugs according to their symptoms and root causes. Furthermore, to understand the uniqueness of WebXR bugs, we compared them with bugs in conventional JavaScript programs and web applications. We believe that our findings can inspire future researches on relevant topics and we released our bug dataset to facilitate follow-up studies.","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":"117060998","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: 12

Title Page i 第1页

2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE) Pub Date : 2020-10-01 DOI: 10.1109/issre5003.2020.00001

引用次数: 0

Locating the Clues of Declining Success Rate of Service Calls 寻找服务电话成功率下降的线索

2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE) Pub Date : 2020-10-01 DOI: 10.1109/ISSRE5003.2020.00039

Guoping Rong, Hao Wang, Yong You, He Zhang, Jialin Sun, Dong Shao, Yangchen Xu

{"title":"Locating the Clues of Declining Success Rate of Service Calls","authors":"Guoping Rong, Hao Wang, Yong You, He Zhang, Jialin Sun, Dong Shao, Yangchen Xu","doi":"10.1109/ISSRE5003.2020.00039","DOIUrl":"https://doi.org/10.1109/ISSRE5003.2020.00039","url":null,"abstract":"For many on-line systems with massive users, to provide services continuously and steadily is vital for business, which requires the anomalies of services should be located and resolved in a timely manner. As a common IT infrastructure, various APM (Application Performance Management) systems/frameworks have been adopted to monitor each call request to a service. Nevertheless, the call request may contain multidimensional attributes (e.g., City, ISP, Platform, etc.), which may further contain multiple values (e.g., ISP could be T-Mobile, CMCC, etc.). As a result, an anomaly such as DSR (Declining Success Rate) to service typically occurs with a combination of such attribute values, which creates major challenges to locate the root cause of the anomaly due to potentially huge numbers of the combinations. In this paper, we propose a novel method, ImpAPTr (Impact Analysis based on Pruning Tree), to identify the combination of dimensional attributes as the clues leading to the root cause of anomalies regarding DSR timely. In the evaluation with the simulated dataset, ImpAPTr detects valid clues in milliseconds with an accuracy of 99.37% (within the top 10 candidate results), 97.72% (top 5), and 94.51% (top 3), respectively, which outperforms previous approaches to a large degree. A field test with a production environment dataset indicates that ImpAPTr is able to detect valid clues in a few seconds.","PeriodicalId":268556,"journal":{"name":"2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE)","volume":"264 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":"115759686","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

LogTransfer: Cross-System Log Anomaly Detection for Software Systems with Transfer Learning LogTransfer:跨系统日志异常检测与迁移学习的软件系统

2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE) Pub Date : 2020-10-01 DOI: 10.1109/ISSRE5003.2020.00013

Rui Chen, Shenglin Zhang, Dongwen Li, Yuzhe Zhang, Fangrui Guo, Weibin Meng, Dan Pei, Yuzhi Zhang, Xu Chen, Yuqing Liu

{"title":"LogTransfer: Cross-System Log Anomaly Detection for Software Systems with Transfer Learning","authors":"Rui Chen, Shenglin Zhang, Dongwen Li, Yuzhe Zhang, Fangrui Guo, Weibin Meng, Dan Pei, Yuzhi Zhang, Xu Chen, Yuqing Liu","doi":"10.1109/ISSRE5003.2020.00013","DOIUrl":"https://doi.org/10.1109/ISSRE5003.2020.00013","url":null,"abstract":"System logs, which describe a variety of events of software systems, are becoming increasingly popular for anomaly detection. However, for a large software system, current unsupervised learning-based methods are suffering from low accuracy due to the high diversity of logs, while the supervised learning methods are nearly infeasible to be used in practice because it is time-consuming and labor-intensive to obtain sufficient labels for different types of software systems. In this paper, we propose a novel framework, LogTransfer, which applies transfer learning to transfer the anomalous knowledge of one type of software system (source system) to another (target system). We represent every template using Glove, which considers both global word co-occurrence and local context information, to address the challenge that different types of software systems are different in log syntax while the semantics of logs should be reserved. We apply an LSTM network to extract the sequential patterns of logs, and propose a novel transfer learning method sharing fully connected networks between source and target systems, to minimize the impact of noises in anomalous log sequences. Extensive experiments have been performed on switch logs of different vendors collected from a top global cloud service provider. LogTransfer achieves an averaged 0.84 F1-score and outperforms the state-of-the-art supervised and unsupervised log-based anomaly detection methods, which are consistent with the experiments conducted on the public HDFS and Hadoop application datasets.","PeriodicalId":268556,"journal":{"name":"2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE)","volume":"44 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":"114583031","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: 34

Message from the ISSRE 2020 General Chairs ISSRE 2020总主席致辞

2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE) Pub Date : 2020-10-01 DOI: 10.1109/issre5003.2020.00005

引用次数: 0