2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE)最新文献_第9页

Program Committee of ICSE 2019 ICSE 2019项目委员会

2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE) Pub Date : 2019-05-01 DOI: 10.1109/icse.2019.00014

Jonathan Aldrich

{"title":"Program Committee of ICSE 2019","authors":"Jonathan Aldrich","doi":"10.1109/icse.2019.00014","DOIUrl":"https://doi.org/10.1109/icse.2019.00014","url":null,"abstract":"Jonathan Aldrich Carnegie Mellon University Aldeida Aleti Monash University Dalal Alrajeh Imperial College London Samik Basu Iowa State University Benoit Baudry KTH Gabriele Bavota Università della Svizzera Italiana (USI) Nelly Bencomo Aston University Ayse Bener Ryerson University Domenico Bianculli SnT Centre University of Luxembourg Christian Bird Microsoft Kelly Blincoe The University of Auckland Barbora Buhnova Masaryk University Marcel Böhme Monash University Maria Christakis MPI-SWS Siobhan Clarke Trinity College Dublin James Clause University of Delaware Marcelo d'Amorim Federal University of Pernambuco Cleidson De Souza Vale Institute of Technology and UFPA Robert Deline Microsoft Danny Dig Oregon State University Yvonne Dittrich IT University of Copenhagen Hakan Erdogmus Carnegie Mellon University Robert Feldt Blekinge Institute of Technology Maria Angela Ferrario Lancaster University Antonio Filieri Imperial College London Thomas Fritz University of Zurich, University of British Columbia Diego Garbervetsky Departamento de Computación. FCEyN. UBA Jaco Geldenhuys Stellenbosch University Milos Gligoric The University of Texas at Austin Mike Godfrey University of Waterloo Alex Groce Northern Arizona University John Grundy Monash University Lars Grunske Humboldt University Berlin Paul Grünbacher Johannes Kepler University Linz Arie Gurfinkel University of Waterloo William G.J. Halfond University of Southern California Tracy Hall Lancaster University Sylvain Hallé Université du Québec à Chicoutimi Dan Hao Peking University Mark Harman University College London Rachel Harrison University of Oxford Emily Hill Drew University","PeriodicalId":6736,"journal":{"name":"2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE)","volume":"04 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86079284","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

Message from the Web Chairs of ICSE 2019 来自ICSE 2019网络主席的信息

2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE) Pub Date : 2019-05-01 DOI: 10.1109/icse.2019.00010

引用次数: 0

Dynamic Slicing for Android Android的动态切片

2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE) Pub Date : 2019-05-01 DOI: 10.1109/ICSE.2019.00118

Tanzirul Azim, Arash Alavi, Iulian Neamtiu, Rajiv Gupta

{"title":"Dynamic Slicing for Android","authors":"Tanzirul Azim, Arash Alavi, Iulian Neamtiu, Rajiv Gupta","doi":"10.1109/ICSE.2019.00118","DOIUrl":"https://doi.org/10.1109/ICSE.2019.00118","url":null,"abstract":"Dynamic program slicing is useful for a variety of tasks, from testing to debugging to security. Prior slicing approaches have targeted traditional desktop/server platforms, rather than mobile platforms such as Android. Slicing mobile, event-based systems is challenging due to their asynchronous callback construction and the IPC (interprocess communication)- heavy, sensor-driven, timing-sensitive nature of the platform. To address these problems, we introduce AndroidSlicer1, the first slicing approach for Android. AndroidSlicer combines a novel asynchronous slicing approach for modeling data and control dependences in the presence of callbacks with lightweight and precise instrumentation; this allows slicing for apps running on actual phones, and without requiring the app's source code. Our slicer is capable of handling a wide array of inputs that Android supports without adding any noticeable overhead. Experiments on 60 apps from Google Play show that AndroidSlicer is effective (reducing the number of instructions to be examined to 0.3% of executed instructions) and efficient (app instrumentation and post-processing combined takes 31 seconds); all while imposing a runtime overhead of just 4%. We present three applications of AndroidSlicer that are particularly relevant in the mobile domain: (1) finding and tracking input parts responsible for an error/crash, (2) fault localization, i.e., finding the instructions responsible for an error/crash, and (3) reducing the regression test suite. Experiments with these applications on an additional set of 18 popular apps indicate that AndroidSlicer is effective for Android testing and debugging.","PeriodicalId":6736,"journal":{"name":"2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE)","volume":"25 1","pages":"1154-1164"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77872337","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: 18

Message from the Journal-First Chair of ICSE 2019 ICSE 2019第一届主席的讲话

2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE) Pub Date : 2019-05-01 DOI: 10.1109/icse.2019.00007

引用次数: 0

Supporting Analysts by Dynamic Extraction and Classification of Requirements-Related Knowledge 通过需求相关知识的动态提取和分类来支持分析人员

2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE) Pub Date : 2019-05-01 DOI: 10.1109/ICSE.2019.00057

Zahra Shakeri Hossein Abad, V. Gervasi, D. Zowghi, B. Far

{"title":"Supporting Analysts by Dynamic Extraction and Classification of Requirements-Related Knowledge","authors":"Zahra Shakeri Hossein Abad, V. Gervasi, D. Zowghi, B. Far","doi":"10.1109/ICSE.2019.00057","DOIUrl":"https://doi.org/10.1109/ICSE.2019.00057","url":null,"abstract":"In many software development projects, analysts are required to deal with systems' requirements from unfamiliar domains. Familiarity with the domain is necessary in order to get full leverage from interaction with stakeholders and for extracting relevant information from the existing project documents. Accurate and timely extraction and classification of requirements knowledge support analysts in this challenging scenario. Our approach is to mine real-time interaction records and project documents for the relevant phrasal units about the requirements related topics being discussed during elicitation. We propose to use both generative and discriminating methods. To extract the relevant terms, we leverage the flexibility and power of Weighted Finite State Transducers (WFSTs) in dynamic modelling of natural language processing tasks. We used an extended version of Support Vector Machines (SVMs) with variable-sized feature vectors to efficiently and dynamically extract and classify requirements-related knowledge from the existing documents. To evaluate the performance of our approach intuitively and quantitatively, we used edit distance and precision/recall metrics. We show in three case studies that the snippets extracted by our method are intuitively relevant and reasonably accurate. Furthermore, we found that statistical and linguistic parameters such as smoothing methods, and words contiguity and order features can impact the performance of both extraction and classification tasks.","PeriodicalId":6736,"journal":{"name":"2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE)","volume":"63 1","pages":"442-453"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76220008","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

A Novel Neural Source Code Representation Based on Abstract Syntax Tree 一种基于抽象语法树的神经网络源代码表示方法

2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE) Pub Date : 2019-05-01 DOI: 10.1109/ICSE.2019.00086

Jian Zhang, Xu Wang, Hongyu Zhang, Hailong Sun, Kaixuan Wang, Xudong Liu

{"title":"A Novel Neural Source Code Representation Based on Abstract Syntax Tree","authors":"Jian Zhang, Xu Wang, Hongyu Zhang, Hailong Sun, Kaixuan Wang, Xudong Liu","doi":"10.1109/ICSE.2019.00086","DOIUrl":"https://doi.org/10.1109/ICSE.2019.00086","url":null,"abstract":"Exploiting machine learning techniques for analyzing programs has attracted much attention. One key problem is how to represent code fragments well for follow-up analysis. Traditional information retrieval based methods often treat programs as natural language texts, which could miss important semantic information of source code. Recently, state-of-the-art studies demonstrate that abstract syntax tree (AST) based neural models can better represent source code. However, the sizes of ASTs are usually large and the existing models are prone to the long-term dependency problem. In this paper, we propose a novel AST-based Neural Network (ASTNN) for source code representation. Unlike existing models that work on entire ASTs, ASTNN splits each large AST into a sequence of small statement trees, and encodes the statement trees to vectors by capturing the lexical and syntactical knowledge of statements. Based on the sequence of statement vectors, a bidirectional RNN model is used to leverage the naturalness of statements and finally produce the vector representation of a code fragment. We have applied our neural network based source code representation method to two common program comprehension tasks: source code classification and code clone detection. Experimental results on the two tasks indicate that our model is superior to state-of-the-art approaches.","PeriodicalId":6736,"journal":{"name":"2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE)","volume":"40 1","pages":"783-794"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78771330","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: 402

SMOKE: Scalable Path-Sensitive Memory Leak Detection for Millions of Lines of Code SMOKE:数百万行代码的可扩展路径敏感内存泄漏检测

2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE) Pub Date : 2019-05-01 DOI: 10.1109/ICSE.2019.00025

Gang Fan, Rongxin Wu, Qingkai Shi, Xiao Xiao, Jinguo Zhou, Charles Zhang

{"title":"SMOKE: Scalable Path-Sensitive Memory Leak Detection for Millions of Lines of Code","authors":"Gang Fan, Rongxin Wu, Qingkai Shi, Xiao Xiao, Jinguo Zhou, Charles Zhang","doi":"10.1109/ICSE.2019.00025","DOIUrl":"https://doi.org/10.1109/ICSE.2019.00025","url":null,"abstract":"Detecting memory leak at industrial scale is still not well addressed, in spite of the tremendous effort from both industry and academia in the past decades. Existing work suffers from an unresolved paradox - a highly precise analysis limits its scalability and an imprecise one seriously hurts its precision or recall. In this work, we present SMOKE, a staged approach to resolve this paradox. In the ?rst stage, instead of using a uniform precise analysis for all paths, we use a scalable but imprecise analysis to compute a succinct set of candidate memory leak paths. In the second stage, we leverage a more precise analysis to verify the feasibility of those candidates. The ?rst stage is scalable, due to the design of a new sparse program representation, the use-?ow graph (UFG), that models the problem as a polynomial-time state analysis. The second stage analysis is both precise and ef?cient, due to the smaller number of candidates and the design of a dedicated constraint solver. Experimental results show that SMOKE can ?nish checking industrial-sized projects, up to 8MLoC, in forty minutes with an average false positive rate of 24.4%. Besides, SMOKE is signi?cantly faster than the state-of-the-art research techniques as well as the industrial tools, with the speedup ranging from 5.2X to 22.8X. In the twenty-nine mature and extensively checked benchmark projects, SMOKE has discovered thirty previously unknown memory leaks which were con?rmed by developers, and one even assigned a CVE ID.","PeriodicalId":6736,"journal":{"name":"2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE)","volume":"20 1","pages":"72-82"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84163253","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: 46

Latent Patterns in Activities: A Field Study of How Developers Manage Context 活动中的潜在模式:开发人员如何管理上下文的实地研究

2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE) Pub Date : 2019-05-01 DOI: 10.1109/ICSE.2019.00051

Souti Chattopadhyay, Nicholas Nelson, Yenifer Ramirez Gonzalez, Annel Amelia Leon, Rahul Pandita, A. Sarma

引用次数: 16

Automated Reporting of Anti-Patterns and Decay in Continuous Integration 持续集成中反模式和衰减的自动报告

2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE) Pub Date : 2019-05-01 DOI: 10.1109/ICSE.2019.00028

Carmine Vassallo, Sebastian Proksch, H. Gall, M. D. Penta

{"title":"Automated Reporting of Anti-Patterns and Decay in Continuous Integration","authors":"Carmine Vassallo, Sebastian Proksch, H. Gall, M. D. Penta","doi":"10.1109/ICSE.2019.00028","DOIUrl":"https://doi.org/10.1109/ICSE.2019.00028","url":null,"abstract":"Continuous Integration (CI) is a widely-used software engineering practice. The software is continuously built so that changes can be easily integrated and issues such as unmet quality goals or style inconsistencies get detected early. Unfortunately, it is not only hard to introduce CI into an existing project, but it is also challenging to live up to the CI principles when facing tough deadlines or business decisions. Previous work has identified common anti-patterns that reduce the promised benefits of CI. Typically, these anti-patterns slowly creep into a project over time before they are identified. We argue that automated detection can help with early identification and prevent such a process decay. In this work, we further analyze this assumption and survey 124 developers about CI anti-patterns. From the results, we build CI-Odor, a reporting tool for CI processes that detects the existence of four relevant anti-patterns by analyzing regular build logs and repository information. In a study on the 18,474 build logs of 36 popular JAVA projects, we reveal the presence of 3,823 high-severity warnings spread across projects. We validate our reports in a survey among 13 original developers of these projects and through general feedback from 42 developers that confirm the relevance of our reports.","PeriodicalId":6736,"journal":{"name":"2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE)","volume":"53 1","pages":"105-115"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84458613","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: 41

Detecting Incorrect Build Rules 检测错误的构建规则

2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE) Pub Date : 2019-05-01 DOI: 10.1109/ICSE.2019.00125

N. Licker, A. Rice

{"title":"Detecting Incorrect Build Rules","authors":"N. Licker, A. Rice","doi":"10.1109/ICSE.2019.00125","DOIUrl":"https://doi.org/10.1109/ICSE.2019.00125","url":null,"abstract":"Automated build systems are routinely used by software engineers to minimize the number of objects that need to be recompiled after incremental changes to the source files of a project. In order to achieve efficient and correct builds, developers must provide the build tools with dependency information between the files and modules of a project, usually expressed in a macro language specific to each build tool. In order to guarantee correctness, the authors of these tools are responsible for enumerating all the files whose contents an output depends on. Unfortunately, this is a tedious process and not all dependencies are captured in practice, which leads to incorrect builds. We automatically uncover such missing dependencies through a novel method that we call build fuzzing. The correctness of build definitions is verified by modifying files in a project, triggering incremental builds and comparing the set of changed files to the set of expected changes. These sets are determined using a dependency graph inferred by tracing the system calls executed during a clean build. We evaluate our method by exhaustively testing build rules of open-source projects, uncovering issues leading to race conditions and faulty builds in 31 of them. We provide a discussion of the bugs we detect, identifying anti-patterns in the use of the macro languages. We fix some of the issues in projects where the features of build systems allow a clean solution.","PeriodicalId":6736,"journal":{"name":"2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE)","volume":"7 1","pages":"1234-1244"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88228724","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