Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering: Software Engineering in Practice最新文献

Piranha 水虎鱼

Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering: Software Engineering in Practice Pub Date : 2020-06-27 DOI: 10.1145/3377813.3381350

M. Ramanathan, Lazaro Clapp, R. Barik, Manu Sridharan

{"title":"Piranha","authors":"M. Ramanathan, Lazaro Clapp, R. Barik, Manu Sridharan","doi":"10.1145/3377813.3381350","DOIUrl":"https://doi.org/10.1145/3377813.3381350","url":null,"abstract":"Feature flags are commonly used in mobile app development and can introduce technical debt related to deleting their usage from the codebase. This can adversely affect the overall reliability of the apps and increase their maintenance complexity. Reducing this debt without imposing additional overheads on the developers necessitates the design of novel tools and automated workflows.In this paper, we describe the design and implementation of PIRANHA, an automated code refactoring tool which is used to automatically generate differential revisions (a.k. a diffs) to delete code corresponding to stale feature flags. PIRANHA takes as input the name of the flag, expected treatment behavior, and the name of the flag’s author. It analyzes the ASTs of the program to generate appropriate refactorings which are packaged into a diff. The diffis assigned to the author of the flag for further processing, who can land it after performing any additional refactorings.We have implemented PIRANHA to delete code in Objective-C, Java, and Swift programs, and deployed it to handle stale flags in multiple Uber apps. We present our experiences with the deployment of PIRANHA from Dec 2017 to May 2019, including the following highlights: (a) generated code cleanup diffs for 1381 flags (17% of total flags), (b) 65% of the diffs landed without any changes, (c) over 85% of the generated diffs compile and pass tests successfully, (d) around 80% of the diffs affect more than one file, (e) developers process more than 88% of the generated diffs, (f) 75% of the generated diffs are processed within a week, and (g) PIRANHA diffs have been interacted with by~200 developers across Uber. Piranha is available as open source at https://github.com/uber/ piranha.CCS CONCEPTS• Software and its engineering → Software maintenance tools.","PeriodicalId":253286,"journal":{"name":"Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering: Software Engineering in Practice","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114975108","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

DeCaf 无咖啡因咖啡

Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering: Software Engineering in Practice Pub Date : 2020-06-27 DOI: 10.1145/3377813.3381353

Chetan Bansal, Sundararajan Renganathan, Ashima Asudani, Olivier Midy, Mathru Janakiraman

{"title":"DeCaf","authors":"Chetan Bansal, Sundararajan Renganathan, Ashima Asudani, Olivier Midy, Mathru Janakiraman","doi":"10.1145/3377813.3381353","DOIUrl":"https://doi.org/10.1145/3377813.3381353","url":null,"abstract":"Large scale cloud services use Key Performance Indicators (KPIs) for tracking and monitoring performance. They usually have Service Level Objectives (SLOs) baked into the customer agreements which are tied to these KPIs. Dependency failures, code bugs, infrastructure failures, and other problems can cause performance regressions. It is critical to minimize the time and manual effort in diagnosing and triaging such issues to reduce customer impact. Large volume of logs and mixed type of attributes (categorical, continuous) in the logs makes diagnosis of regressions non-trivial.In this paper, we present the design, implementation and experience from building and deploying DeCaf, a system for automated diagnosis and triaging of KPI issues using service logs. It uses machine learning along with pattern mining to help service owners automatically root cause and triage performance issues. We present the learnings and results from case studies on two large scale cloud services in Microsoft where DeCaf successfully diagnosed 10 known and 31 unknown issues. DeCaf also automatically triages the identified issues by leveraging historical data. Our key insights are that for any such diagnosis tool to be effective in practice, it should a) scale to large volumes of service logs and attributes, b) support different types of KPIs and ranking functions, c) be integrated into the DevOps processes.","PeriodicalId":253286,"journal":{"name":"Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering: Software Engineering in Practice","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114631786","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: 27

JSidentify

Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering: Software Engineering in Practice Pub Date : 2020-06-27 DOI: 10.1145/3377813.3381352

Q. Xia, Zhongzhu Zhou, Zhihao Li, B. Xu, W. Zou, Zishun Chen, Huafeng Ma, Gangqiang Liang, Haochuan Lu, Shiyu Guo, Ting Xiong, Yuetang Deng, Tao Xie

{"title":"JSidentify","authors":"Q. Xia, Zhongzhu Zhou, Zhihao Li, B. Xu, W. Zou, Zishun Chen, Huafeng Ma, Gangqiang Liang, Haochuan Lu, Shiyu Guo, Ting Xiong, Yuetang Deng, Tao Xie","doi":"10.1145/3377813.3381352","DOIUrl":"https://doi.org/10.1145/3377813.3381352","url":null,"abstract":"Online mini games are lightweight game apps, typically implemented in JavaScript (JS), that run inside another host mobile app (such as WeChat, Baidu, and Alipay). These mini games do not need to be downloaded or upgraded through an app store, making it possible for one host mobile app to perform the aggregated services of many apps. Hundreds of millions of users play tens of thousands of mini games, which make a great profit, and consequently are popular targets of plagiarism. In cases of plagiarism, deeply obfuscated code cloned from the original code often embodies malicious code segments and copyright infringements, posing great challenges for existing plagiarism detection tools. To address these challenges, in this paper, we design and implement JSidentify, a hybrid framework to detect plagiarism among online mini games. JSidentify includes three techniques based on different levels of code abstraction. JSidentify applies the included techniques in the constructed priority list one by one to reduce overall detection time. Our evaluation results show that JSidentify outperforms other existing related state-of-the-art approaches and achieves the best precision and recall with affordable detection time when detecting plagiarism among online mini games and clones among general JS programs. Our deployment experience of JSidentify also shows that JSidentify is indispensable in the daily operations of online mini games in WeChat.","PeriodicalId":253286,"journal":{"name":"Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering: Software Engineering in Practice","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114509329","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

Precfix

Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering: Software Engineering in Practice Pub Date : 2020-06-27 DOI: 10.1145/3377813.3381356

Xindong Zhang, Chenguang Zhu, Yi Li, Jianmei Guo, Lihua Liu, Haobo Gu

{"title":"Precfix","authors":"Xindong Zhang, Chenguang Zhu, Yi Li, Jianmei Guo, Lihua Liu, Haobo Gu","doi":"10.1145/3377813.3381356","DOIUrl":"https://doi.org/10.1145/3377813.3381356","url":null,"abstract":"Patch recommendation is the process of identifying errors in software systems and suggesting suitable fixes for them. Patch recommendation can significantly improve developer productivity by reducing both the debugging and repairing time. Existing techniques usually rely on complete test suites and detailed debugging reports, which are often absent in practical industrial settings. In this paper, we propose Precfix, a pragmatic approach targeting large-scale industrial codebase and making recommendations based on previously observed debugging activities. Precfix collects defect-patch pairs from development histories, performs clustering, and extracts generic reusable patching patterns as recommendations. We conducted experimental study on an industrial codebase with 10K projects involving diverse defect patterns. We managed to extract 3K templates of defect-patch pairs, which have been successfully applied to the entire codebase. Our approach is able to make recommendations within milliseconds and achieves a false positive rate of 22% confirmed by manual review. The majority (10/12) of the interviewed developers appreciated Precfix, which has been rolled out to Alibaba to support various critical businesses.","PeriodicalId":253286,"journal":{"name":"Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering: Software Engineering in Practice","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127828490","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: 8

Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering: Software Engineering in Practice ACM/IEEE第42届软件工程国际会议论文集:实践中的软件工程

Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering: Software Engineering in Practice Pub Date : 1900-01-01 DOI: 10.1145/3377813

引用次数: 1