2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE)最新文献_第3页

View-Centric Performance Optimization for Database-Backed Web Applications 数据库支持的Web应用程序以视图为中心的性能优化

2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE) Pub Date : 2019-05-25 DOI: 10.1109/ICSE.2019.00104

Junwen Yang, Cong Yan, Chengcheng Wan, Shan Lu, Alvin Cheung

引用次数: 18

Grey-Box Concolic Testing on Binary Code 二进制码的灰盒聚类测试

2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE) Pub Date : 2019-05-25 DOI: 10.1109/ICSE.2019.00082

Jaeseung Choi, J. Jang, Choongwoo Han, S. Cha

引用次数: 59

Training Binary Classifiers as Data Structure Invariants 训练二元分类器作为数据结构不变量

2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE) Pub Date : 2019-05-25 DOI: 10.1109/ICSE.2019.00084

F. Molina, Renzo Degiovanni, Pablo Ponzio, Germán Regis, Nazareno Aguirre, M. Frias

{"title":"Training Binary Classifiers as Data Structure Invariants","authors":"F. Molina, Renzo Degiovanni, Pablo Ponzio, Germán Regis, Nazareno Aguirre, M. Frias","doi":"10.1109/ICSE.2019.00084","DOIUrl":"https://doi.org/10.1109/ICSE.2019.00084","url":null,"abstract":"We present a technique to distinguish valid from invalid data structure objects. The technique is based on building an artificial neural network, more precisely a binary classifier, and training it to identify valid and invalid instances of a data structure. The obtained classifier can then be used in place of the data structure's invariant, in order to attempt to identify (in)correct behaviors in programs manipulating the structure. In order to produce the valid objects to train the network, an assumed-correct set of object building routines is randomly executed. Invalid instances are produced by generating values for object fields that \"break\" the collected valid values, i.e., that assign values to object fields that have not been observed as feasible in the assumed-correct executions that led to the collected valid instances. We experimentally assess this approach, over a benchmark of data structures. We show that this learning technique produces classifiers that achieve significantly better accuracy in classifying valid/invalid objects compared to a technique for dynamic invariant detection, and leads to improved bug finding.","PeriodicalId":6736,"journal":{"name":"2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74918467","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: 13

PIVOT: Learning API-Device Correlations to Facilitate Android Compatibility Issue Detection PIVOT:学习api -设备相关性以促进Android兼容性问题检测

2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE) Pub Date : 2019-05-25 DOI: 10.1109/ICSE.2019.00094

Lili Wei, Yepang Liu, S. Cheung

{"title":"PIVOT: Learning API-Device Correlations to Facilitate Android Compatibility Issue Detection","authors":"Lili Wei, Yepang Liu, S. Cheung","doi":"10.1109/ICSE.2019.00094","DOIUrl":"https://doi.org/10.1109/ICSE.2019.00094","url":null,"abstract":"The heavily fragmented Android ecosystem has induced various compatibility issues in Android apps. The search space for such fragmentation-induced compatibility issues (FIC issues) is huge, comprising three dimensions: device models, Android OS versions, and Android APIs. FIC issues, especially those arising from device models, evolve quickly with the frequent release of new device models to the market. As a result, an automated technique is desired to maintain timely knowledge of such FIC issues, which are mostly undocumented. In this paper, we propose such a technique, PIVOT, that automatically learns API-device correlations of FIC issues from existing Android apps. PIVOT extracts and prioritizes API-device correlations from a given corpus of Android apps. We evaluated PIVOT with popular Android apps on Google Play. Evaluation results show that PIVOT can effectively prioritize valid API-device correlations for app corpora collected at different time. Leveraging the knowledge in the learned API-device correlations, we further conducted a case study and successfully uncovered ten previously-undetected FIC issues in open-source Android apps.","PeriodicalId":6736,"journal":{"name":"2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87621867","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: 35

Easy Modelling and Verification of Unpredictable and Preemptive Interrupt-Driven Systems 不可预测和抢占式中断驱动系统的简单建模与验证

2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE) Pub Date : 2019-05-25 DOI: 10.1109/ICSE.2019.00037

Minxue Pan, Shouyu Chen, Yu Pei, Tian Zhang, Xuandong Li

{"title":"Easy Modelling and Verification of Unpredictable and Preemptive Interrupt-Driven Systems","authors":"Minxue Pan, Shouyu Chen, Yu Pei, Tian Zhang, Xuandong Li","doi":"10.1109/ICSE.2019.00037","DOIUrl":"https://doi.org/10.1109/ICSE.2019.00037","url":null,"abstract":"The widespread real-time and embedded systems are mostly interrupt-driven because their heavy interaction with the environment is often initiated by interrupts. With the interrupt arrival being unpredictable and the interrupt handling being preemptive, a large number of possible system behaviours are generated, which makes the correctness assurance of such systems difficult and costly. Model checking is considered to be one of the effective methods for exhausting behavioural state space for correctness. However, existing modelling approaches for interrupt-driven systems are based on either calculus or automata theory, and have a steep learning curve. To address this problem, we propose a new modelling language called interrupt sequence diagram (ISD). By extending the popular UML sequence diagram notations, the ISD supports the modelling of interrupts' essential features visually and concisely. We also propose an automata-based semantics for ISD, based on which ISD can be transformed to a subset of hybrid automata so as to leverage the abundant off-the-shelf checkers. Experiments on examples from both real-world and existing literature were conducted, and the results demonstrate our approach's usability and effectiveness.","PeriodicalId":6736,"journal":{"name":"2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91433790","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

ReCDroid: Automatically Reproducing Android Application Crashes from Bug Reports ReCDroid:从Bug报告中自动复制Android应用程序崩溃

2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE) Pub Date : 2019-05-25 DOI: 10.1109/ICSE.2019.00030

Yu Zhao, Tingting Yu, Ting Su, Yang Liu, Wei Zheng, Jingzhi Zhang, William G. J. Halfond

{"title":"ReCDroid: Automatically Reproducing Android Application Crashes from Bug Reports","authors":"Yu Zhao, Tingting Yu, Ting Su, Yang Liu, Wei Zheng, Jingzhi Zhang, William G. J. Halfond","doi":"10.1109/ICSE.2019.00030","DOIUrl":"https://doi.org/10.1109/ICSE.2019.00030","url":null,"abstract":"The large demand of mobile devices creates significant concerns about the quality of mobile applications (apps). Developers heavily rely on bug reports in issue tracking systems to reproduce failures (e.g., crashes). However, the process of crash reproduction is often manually done by developers, making the resolution of bugs inefficient, especially that bug reports are often written in natural language. To improve the productivity of developers in resolving bug reports, in this paper, we introduce a novel approach, called ReCDroid, that can automatically reproduce crashes from bug reports for Android apps. ReCDroid uses a combination of natural language processing (NLP) and dynamic GUI exploration to synthesize event sequences with the goal of reproducing the reported crash. We have evaluated ReCDroid on 51 original bug reports from 33 Android apps. The results show that ReCDroid successfully reproduced 33 crashes (63.5% success rate) directly from the textual description of bug reports. A user study involving 12 participants demonstrates that ReCDroid can improve the productivity of developers when resolving crash bug reports.","PeriodicalId":6736,"journal":{"name":"2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89488101","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: 67

SafeCheck: Safety Enhancement of Java Unsafe API SafeCheck: Java不安全API的安全增强

2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE) Pub Date : 2019-05-25 DOI: 10.1109/ICSE.2019.00095

Shiyou Huang, Jianmei Guo, Sanhong Li, Xiang Li, Yumin Qi, K. Chow, Jeff Huang

{"title":"SafeCheck: Safety Enhancement of Java Unsafe API","authors":"Shiyou Huang, Jianmei Guo, Sanhong Li, Xiang Li, Yumin Qi, K. Chow, Jeff Huang","doi":"10.1109/ICSE.2019.00095","DOIUrl":"https://doi.org/10.1109/ICSE.2019.00095","url":null,"abstract":"Java is a safe programming language by providing bytecode verification and enforcing memory protection. For instance, programmers cannot directly access the memory but have to use object references. Yet, the Java runtime provides an Unsafe API as a backdoor for the developers to access the low- level system code. Whereas the Unsafe API is designed to be used by the Java core library, a growing community of third-party libraries use it to achieve high performance. The Unsafe API is powerful, but dangerous, which leads to data corruption, resource leaks and difficult-to-diagnose JVM crash if used improperly. In this work, we study the Unsafe crash patterns and propose a memory checker to enforce memory safety, thus avoiding the JVM crash caused by the misuse of the Unsafe API at the bytecode level. We evaluate our technique on real crash cases from the openJDK bug system and real-world applications from AJDK. Our tool reduces the efforts from several days to a few minutes for the developers to diagnose the Unsafe related crashes. We also evaluate the runtime overhead of our tool on projects using intensive Unsafe operations, and the result shows that our tool causes a negligible perturbation to the execution of the applications.","PeriodicalId":6736,"journal":{"name":"2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76184389","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

Symbolic Repairs for GR(1) Specifications GR(1)规范的符号修复

2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE) Pub Date : 2019-05-25 DOI: 10.1109/ICSE.2019.00106

S. Maoz, Jan Oliver Ringert, Rafi Shalom

引用次数: 29

DLFinder: Characterizing and Detecting Duplicate Logging Code Smells DLFinder:描述和检测重复的日志代码气味

2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE) Pub Date : 2019-05-25 DOI: 10.1109/ICSE.2019.00032

Zhenhao Li, T. Chen, Jinqiu Yang, Weiyi Shang

{"title":"DLFinder: Characterizing and Detecting Duplicate Logging Code Smells","authors":"Zhenhao Li, T. Chen, Jinqiu Yang, Weiyi Shang","doi":"10.1109/ICSE.2019.00032","DOIUrl":"https://doi.org/10.1109/ICSE.2019.00032","url":null,"abstract":"Developers rely on software logs for a wide variety of tasks, such as debugging, testing, program comprehension, verification, and performance analysis. Despite the importance of logs, prior studies show that there is no industrial standard on how to write logging statements. Recent research on logs often only considers the appropriateness of a log as an individual item (e.g., one single logging statement); while logs are typically analyzed in tandem. In this paper, we focus on studying duplicate logging statements, which are logging statements that have the same static text message. Such duplications in the text message are potential indications of logging code smells, which may affect developers' understanding of the dynamic view of the system. We manually studied over 3K duplicate logging statements and their surrounding code in four large-scale open source systems: Hadoop, CloudStack, ElasticSearch, and Cassandra. We uncovered five patterns of duplicate logging code smells. For each instance of the code smell, we further manually identify the problematic (i.e., require fixes) and justifiable (i.e., do not require fixes) cases. Then, we contact developers in order to verify our manual study result. We integrated our manual study result and developers' feedback into our automated static analysis tool, DLFinder, which automatically detects problematic duplicate logging code smells. We evaluated DLFinder on the four manually studied systems and two additional systems: Camel and Wicket. In total, combining the results of DLFinder and our manual analysis, we reported 82 problematic code smell instances to developers and all of them have been fixed.","PeriodicalId":6736,"journal":{"name":"2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79803089","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: 49

Zero-Overhead Path Prediction with Progressive Symbolic Execution 渐进式符号执行的零开销路径预测

2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE) Pub Date : 2019-05-25 DOI: 10.1109/ICSE.2019.00039

Richard Rutledge, Sunjae Park, Haider Adnan Khan, A. Orso, Milos Prvulović, A. Zajić

{"title":"Zero-Overhead Path Prediction with Progressive Symbolic Execution","authors":"Richard Rutledge, Sunjae Park, Haider Adnan Khan, A. Orso, Milos Prvulović, A. Zajić","doi":"10.1109/ICSE.2019.00039","DOIUrl":"https://doi.org/10.1109/ICSE.2019.00039","url":null,"abstract":"In previous work, we introduced zero-overhead profiling (ZOP), a technique that leverages the electromagnetic emissions generated by the computer hardware to profile a program without instrumenting it. Although effective, ZOP has several shortcomings: it requires test inputs that achieve extensive code coverage for its training phase; it predicts path profiles instead of complete execution traces; and its predictions can suffer unrecoverable accuracy losses. In this paper, we present zero-overhead path prediction (ZOP-2), an approach that extends ZOP and addresses its limitations. First, ZOP-2 achieves high coverage during training through progressive symbolic execution (PSE)-symbolic execution of increasingly small program fragments. Second, ZOP-2 predicts complete execution traces, rather than path profiles. Finally, ZOP-2 mitigates the problem of path mispredictions by using a stateless approach that can recover from prediction errors. We evaluated our approach on a set of benchmarks with promising results; for the cases considered, (1) ZOP-2 achieved over 90% path prediction accuracy, and (2) PSE covered feasible paths missed by traditional symbolic execution, thus boosting ZOP-2's accuracy.","PeriodicalId":6736,"journal":{"name":"2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79910060","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