2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)最新文献_第9页

ProMal: Precise Window Transition Graphs for Android via Synergy of Program Analysis and Machine Learning 基于程序分析和机器学习的Android精确窗口转换图

2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) Pub Date : 2021-05-01 DOI: 10.1109/ICSE-Companion52605.2021.00061

Changlin Liu

{"title":"ProMal: Precise Window Transition Graphs for Android via Synergy of Program Analysis and Machine Learning","authors":"Changlin Liu","doi":"10.1109/ICSE-Companion52605.2021.00061","DOIUrl":"https://doi.org/10.1109/ICSE-Companion52605.2021.00061","url":null,"abstract":"Mobile apps have been an integral part in our daily life. As these apps become more complex, it is critical to provide automated analysis techniques to ensure the correctness, security, and performance of these apps. A key component for these automated analysis techniques is to create a graphical user interface (GUI) model of an app, i.e., a window transition graph (WTG), that models windows and transitions among the windows. While existing work has provided both static and dynamic analysis to build the WTG for an app, the constructed WTG misses many transitions or contains many infeasible transitions due to the coverage issues of dynamic analysis and over-approximation of the static analysis. We propose ProMal, a \"tribrid\" analysis that synergistically combines static analysis, dynamic analysis, and machine learning to construct a precise WTG. Specifically, ProMal first applies static analysis to build a static WTG, and then applies dynamic analysis to verify the transitions in the static WTG. For the unverified transitions, ProMal further provides machine learning techniques that leverage runtime information (i.e., screenshots, UI layouts, and text information) to predict whether they are feasible transitions. Our evaluations on 40 real-world apps demonstrate the superiority of ProMal in building WTGs over static analysis, dynamic analysis, and machine learning techniques when they are applied separately.","PeriodicalId":136929,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129226409","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

CodeShovel: A Reusable and Available Tool for Extracting Source Code Histories CodeShovel:一个可重用的、可用的提取源代码历史的工具

2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) Pub Date : 2021-05-01 DOI: 10.1109/ICSE-Companion52605.2021.00100

F. Grund, S. A. Chowdhury, N. Bradley, Braxton Hall, Reid Holmes

引用次数: 2

Artifact: Reducing DNN Properties to Enable Falsification with Adversarial Attacks 神器:减少DNN属性以允许对抗性攻击的伪造

2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) Pub Date : 2021-05-01 DOI: 10.1109/ICSE-Companion52605.2021.00068

David Shriver, Sebastian G. Elbaum, Matthew B. Dwyer

引用次数: 1

Learning to Boost the Efficiency of Modern Code Review 学习提高现代代码审查的效率

2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) Pub Date : 2021-04-16 DOI: 10.1109/ICSE-Companion52605.2021.00126

R. Heumüller

引用次数: 2

High-Quality Automated Program Repair 高质量的自动程序修复

2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) Pub Date : 2021-04-16 DOI: 10.1109/ICSE-Companion52605.2021.00134

Manish Motwani

{"title":"High-Quality Automated Program Repair","authors":"Manish Motwani","doi":"10.1109/ICSE-Companion52605.2021.00134","DOIUrl":"https://doi.org/10.1109/ICSE-Companion52605.2021.00134","url":null,"abstract":"Automatic program repair (APR) has recently gained attention because it proposes to fix software defects with no human intervention. To automatically fix defects, most APR tools use the developer-written tests to (a) localize the defect, and (b) generate and validate the automatically produced candidate patches based on the constraints imposed by the tests. While APR tools can produce patches that appear to fix the defect for 11–19% of the defects in real-world software, most of the patches produced are not correct or acceptable to developers because they overfit to the tests used during the repair process. This problem is known as the patch overfitting problem. To address this problem, I propose to equip APR tools with additional constraints derived from natural-language software artifacts such as bug reports and requirements specifications that describe the bug and intended software behavior but are not typically used by the APR tools. I hypothesize that patches produced by APR tools while using such additional constraints would be of higher quality. To test this hypothesis, I propose an automated and objective approach to evaluate the quality of patches, and propose two novel methods to improve the fault localization and developer-written test suites using natural-language software artifacts. Finally, I propose to use my patch evaluation methodology to analyze the effect of the improved fault localization and test suites on the quality of patches produced by APR tools for real-world defects.","PeriodicalId":136929,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126022945","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

A Model using Agile Methodologies for Defining Metrics to be used by the Public Sector in Brazil to set Remuneration for Outsourced Software Development 一个使用敏捷方法定义度量标准的模型，将被巴西公共部门用于设置外包软件开发的报酬

2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) Pub Date : 2021-04-13 DOI: 10.1109/ICSE-Companion52605.2021.00125

Washington Almeida

{"title":"A Model using Agile Methodologies for Defining Metrics to be used by the Public Sector in Brazil to set Remuneration for Outsourced Software Development","authors":"Washington Almeida","doi":"10.1109/ICSE-Companion52605.2021.00125","DOIUrl":"https://doi.org/10.1109/ICSE-Companion52605.2021.00125","url":null,"abstract":"The process of contracting software factorieswithin the scope of the Federal Public Administration (APF, inPortuguese) in Brazil has undergone some changes due tolegislative alterations and a model has been proposed to improvethe rendering of services regarding delivering results. Softwarefactory contracts based on predictive processes and on themetrics of Function Points (FP) are the target of criticisms andhave problems in achieving results, as verified in previousresearch. The initial objective of the study now proposed will beto define a process model for formulating metrics that can beused in agile contracts as opposed to those of standardized FPwhich have already proved to be quite problematic and difficultfor the contractor to manage. Thus, in line with the theme ofagile contracts for software development companies, the studyproposed will seek to better understand the problem of softwaredevelopment and its maintenance by public bodies by means ofcontracts using agile methodologies and appropriate metrics forremunerating these services.","PeriodicalId":136929,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134176937","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

Group Recommendation Techniques for Feature Modeling and Configuration 特征建模和配置的组推荐技术

2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) Pub Date : 2021-04-13 DOI: 10.1109/ICSE-Companion52605.2021.00123

Viet-Man Le

引用次数: 2

Unburdening Onboarding in Software Product Lines 卸下软件产品线中的入职负担

2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) Pub Date : 2021-04-12 DOI: 10.1109/ICSE-Companion52605.2021.00121

Raul Medeiros

引用次数: 0

Extractive Multi Product-Line Engineering 采掘多产品线工程

2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) Pub Date : 2021-04-12 DOI: 10.1109/ICSE-Companion52605.2021.00122

Kamil Rosiak

引用次数: 1

NodeSRT: A Selective Regression Testing Tool for Node.js Application nodest:用于Node.js应用程序的选择性回归测试工具

2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) Pub Date : 2021-03-31 DOI: 10.1109/ICSE-Companion52605.2021.00055

Yufeng Chen

{"title":"NodeSRT: A Selective Regression Testing Tool for Node.js Application","authors":"Yufeng Chen","doi":"10.1109/ICSE-Companion52605.2021.00055","DOIUrl":"https://doi.org/10.1109/ICSE-Companion52605.2021.00055","url":null,"abstract":"Node.js is one of the most popular frameworks for building web applications. As software systems mature, the cost of running their entire regression test suite can become significant. Selective Regression Testing (SRT) is a technique that executes only a subset of tests the regression test suite can detect software failures more efficiently. Previous SRT studies mainly focused on standard desktop applications. Node.js applications are considered hard to perform test reduction because of Node's asynchronous, event-driven programming model and because JavaScript is a dynamic programming language. In this paper, we present NodeSRT, a Selective Regression Testing framework for Node.js applications. By performing static and dynamic analysis, NodeSRT identifies the relationship between changed methods and tests, then reduces the regression test suite to only tests that are affected by the change to improve the execution time of the regression test suite. To evaluate our selection technique, we applied NodeSRT to two open-source projects: Uppy and Simorgh, then compared our approach with the retest-all strategy and current industry-standard SRT technique: Jest OnlyChange. The results demonstrate that NodeSRT correctly selects affected tests based on changes and is 250% faster, 450% more precise than the Jest OnlyChange.","PeriodicalId":136929,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126406046","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