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Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering最新文献

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ImpAPTr
Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering Pub Date : 2020-12-21 DOI: 10.1145/3324884.3415301
Hao Wang, Guoping Rong, Yangchen Xu, Yong You
{"title":"ImpAPTr","authors":"Hao Wang, Guoping Rong, Yangchen Xu, Yong You","doi":"10.1145/3324884.3415301","DOIUrl":"https://doi.org/10.1145/3324884.3415301","url":null,"abstract":"As a common IT infrastructure, APM (Application Performance Management) systems have been widely adopted to monitor call requests to an on-line service. Usually, each request may contain multi-dimensional attributes (e.g., City, ISP, Platform, etc.), which may become the reason for a certain anomaly regarding DSR (De-clining Success Rate) of service calls either solely or as a combination. Moreover, each attribute may also have multiple values (e.g., ISP could be T-Mobile, Vodafone, CMCC, etc.), rendering intricate root causes and huge challenges to identify the root causes. In this paper, we propose a prototype tool, ImpAPTr (Impact Analysis based on Pruning Tree), to identify the combination of dimensional attributes as the clues to dig out the root causes of anomalies regarding DSR of a service call in a timely manner. ImpAPTr has been evaluated in MeiTuan, one of the biggest on-line service providers. Performance regarding the accuracy outperforms several previous tools in the same field.","PeriodicalId":267160,"journal":{"name":"Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114069774","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
SRRTA
Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering Pub Date : 2020-12-21 DOI: 10.1145/3324884.3418928
Jinhao Dong, Yiling Lou, Dan Hao
{"title":"SRRTA","authors":"Jinhao Dong, Yiling Lou, Dan Hao","doi":"10.1145/3324884.3418928","DOIUrl":"https://doi.org/10.1145/3324884.3418928","url":null,"abstract":"Regression testing is widely recognized as an important but time-consuming process. To alleviate this cost issue, test selection, reduction, and prioritization have been widely studied, and they share the commonality that they improve regression testing by optimizing the execution of the whole test suite. In this paper, we attempt to accelerate regression testing from a totally new perspective, i.e., skipping some execution of a new program by reusing program states of an old program. Following this intuition, we propose a state-reuse based acceleration approach SRRTA, consisting of two components: state storage and loading. With the former, SRRTA collects some program states during the execution of an old version through three heuristic-based storage strategies; with the latter, SRRTA loads the stored program states with efficiency optimization strategies. Through the preliminary study on commons-math, SRRTA reduces 82.7% of the regression testing time.","PeriodicalId":267160,"journal":{"name":"Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127983485","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
UnchartIt UnchartIt
Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering Pub Date : 2020-12-21 DOI: 10.1145/3324884.3416613
Daniel Ramos, J. Pereira, I. Lynce, Vasco M. Manquinho, R. Martins
{"title":"UnchartIt","authors":"Daniel Ramos, J. Pereira, I. Lynce, Vasco M. Manquinho, R. Martins","doi":"10.1145/3324884.3416613","DOIUrl":"https://doi.org/10.1145/3324884.3416613","url":null,"abstract":"Charts are commonly used for data visualization. Generating a chart usually involves performing data transformations, including data pre-processing and aggregation. These tasks can be cumbersome and time-consuming, even for experienced data scientists. Reproducing existing charts can also be a challenging task when information about data transformations is no longer available. In this paper, we tackle the problem of recovering data transformations from existing charts. Given an input table and a chart, our goal is to automatically recover the data transformation program underlying the chart. We divide our approach into four steps: (1) data extraction, (2) candidate generation, (3) candidate ranking, and (4) candidate disambiguation. We implemented our approach in a tool called UNCHARTIT and evaluated it on a set of 50 benchmarks from Kaggle. Experimental results show that UNCHARTIT successfully ranks the correct data transformation among the top-10 programs in 92% of the benchmarks. To disambiguate the top-ranking programs, we use our new interactive procedure, which successfully disambiguates 98% of the ambiguous benchmarks by asking on average fewer than 2 questions to the user.","PeriodicalId":267160,"journal":{"name":"Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116775642","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
Prober
Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering Pub Date : 2020-12-21 DOI: 10.1145/3324884.3416533
Hongyu Liu, Ruiqin Tian, Bin Ren, Tongping Liu
{"title":"Prober","authors":"Hongyu Liu, Ruiqin Tian, Bin Ren, Tongping Liu","doi":"10.1145/3324884.3416533","DOIUrl":"https://doi.org/10.1145/3324884.3416533","url":null,"abstract":"Heap-based overflows are still not completely solved even after decades of research. This paper proposes Prober, a novel system aiming to detect and prevent heap overflows in the production environment. Prober leverages a key observation based on the analysis of dozens of real bugs: all heap overflows are related to arrays. Based on this observation, Prober only focuses on array-related heap objects, instead of all heap objects. Prober utilizes static analysis to label all susceptible call-stacks during the compilation, and then employs the page protection to detect any invalid accesses during the runtime. In addition to this, Prober integrates multiple existing methods together to ensure the efficiency of its detection. Overall, Prober introduces almost negligible performance overhead, with 1.5% on average. Prober not only stops possible attacks on time, but also reports the faulty instructions that could guide bug fixes. Prober is ready for deployment due to its effectiveness and low overhead.","PeriodicalId":267160,"journal":{"name":"Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114673931","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
FLUX 通量
Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering Pub Date : 2020-12-21 DOI: 10.1145/3324884.3418916
Chandan Sharma
{"title":"FLUX","authors":"Chandan Sharma","doi":"10.1145/3324884.3418916","DOIUrl":"https://doi.org/10.1145/3324884.3418916","url":null,"abstract":"With the influx of Web 3.0 the focus in Big Data Analytics has shifted towards modelling highly interconnected data and analysing relationships between them. Graph databases befit the requirements of Big Data Analytics yet organizations still depend on relational databases. A major roadblock in the industry wide adoption of graph databases is that a standard query language is still in its inception stage hence withholding interoperability between the two technologies. In this research we propose a tool FLUX for translating relational database queries to graph database queries.","PeriodicalId":267160,"journal":{"name":"Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering","volume":"126 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127567455","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
GUI2WiRe GUI2WiRe
Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering Pub Date : 2020-12-21 DOI: 10.1145/3324884.3415289
K. Kolthoff, Christian Bartelt, Simone Paolo Ponzetto
{"title":"GUI2WiRe","authors":"K. Kolthoff, Christian Bartelt, Simone Paolo Ponzetto","doi":"10.1145/3324884.3415289","DOIUrl":"https://doi.org/10.1145/3324884.3415289","url":null,"abstract":"High-fidelity Graphical User Interface (GUI) prototyping is a well-established and suitable method for enabling fruitful discussions, clarification and refinement of requirements formulated by customers. GUI prototypes can help to reduce misunderstandings between customers and developers, which may occur due to the ambiguity comprised in informal Natural Language (NL). However, a disadvantage of employing high-fidelity GUI prototypes is their time-consuming and expensive development. Common GUI prototyping tools are based on combining individual GUI components or manually crafted templates. In this work, we present GUI2WiRe, a tool that enables users to retrieve GUI prototypes from a semiautomatically created large-scale GUI repository for mobile applications matching user requirements specified in Natural Language (NLR). We extract multiple text segments from the GUI hierarchy data and employ various Information Retrieval (IR) models and Automatic Query Expansion (AQE) techniques to achieve ad-hoc GUI retrieval from NLR. Retrieved GUI prototypes mined from applications can be inserted in the graphical editor of GUI2WiRe to rapidly create wireframes. GUI components are extracted automatically from the GUI screenshots and basic editing functionality is provided to the user. Finally, a preview of the application is created from the wireframe to allow interactive exploration of the current design. We evaluated the applied IR and AQE approaches for their effectiveness in terms of GUI retrieval relevance on a manually annotated collection of NLR and discuss our planned user studies. Video presentation of GUI2WiRe: https://youtu.be/2nN-Xr2Hk7I","PeriodicalId":267160,"journal":{"name":"Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121888103","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
RepoSkillMiner
Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering Pub Date : 2020-12-21 DOI: 10.1145/3324884.3415305
Stratos Kourtzanidis, A. Chatzigeorgiou, Apostolos Ampatzoglou
{"title":"RepoSkillMiner","authors":"Stratos Kourtzanidis, A. Chatzigeorgiou, Apostolos Ampatzoglou","doi":"10.1145/3324884.3415305","DOIUrl":"https://doi.org/10.1145/3324884.3415305","url":null,"abstract":"A GitHub profile is becoming an essential part of a developer's resume enabling HR departments to extract someone's expertise, through automated analysis of his/her contribution to open-source projects. At the same time, having clear insights on the technologies used in a project can be very beneficial for resource allocation and project maintainability planning. In the literature, one can identify various approaches for identifying expertise on programming languages, based on the projects that developer contributed to. In this paper, we move one step further and introduce an approach (ac-companied by a tool) to identify low-level expertise on particular software frameworks and technologies apart, relying solely on GitHub data, using the GitHub API and Natural Language Processing (NLP)-using the Microsoft Language Understanding Intelligent Service (LUIS). In particular, we developed an NLP model in LUIS for named-entity recognition for three (3). NET technologies and two (2) front-end frameworks. Our analysis is based upon specific commit contents, in terms of the exact code chunks, which the committer added or changed. We evaluate the precision, recall and f-measure for the derived technologies/frameworks, by conducting a batch test in LUIS and report the results. The proposed approach is demonstrated through a fully functional web application named RepoSkillMiner.","PeriodicalId":267160,"journal":{"name":"Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129546448","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
Edge4Sys
Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering Pub Date : 2020-12-21 DOI: 10.1145/3324884.3418908
Han Gao, Yi Xu, Xiao Liu, Jia Xu, Tianxiang Chen, Bowen Zhou, Rui Li, Xuejun Li
{"title":"Edge4Sys","authors":"Han Gao, Yi Xu, Xiao Liu, Jia Xu, Tianxiang Chen, Bowen Zhou, Rui Li, Xuejun Li","doi":"10.1145/3324884.3418908","DOIUrl":"https://doi.org/10.1145/3324884.3418908","url":null,"abstract":"At present, most of the smart systems are based on cloud computing, and massive data generated at the smart end device will need to be transferred to the cloud where AI models are deployed. Therefore, a big challenge for smart system engineers is that cloud based smart systems often face issues such as network congestion and high latency. In recent years, mobile edge computing (MEC) is becoming a promising solution which supports computation-intensive tasks such as deep learning through computation offloading to the servers located at the local network edge. To take full advantage of MEC, an effective collaboration between the end device and the edge server is essential. In this paper, as an initial investigation, we propose Edge4Sys, a Device-Edge Collaborative Framework for MEC based Smart System. Specifically, we employ the deep learning based user identification process in a MEC-based UAV (Unmanned Aerial Vehicle) delivery system as a case study to demonstrate the effectiveness of the proposed framework which can significantly reduce the network traffic and the response time.","PeriodicalId":267160,"journal":{"name":"Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115860365","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
STIFA
Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering Pub Date : 2020-12-21 DOI: 10.1145/3324884.3415300
Zhenfei Cao, Xu Wang, Shengcheng Yu, Yexiao Yun, Chunrong Fang
{"title":"STIFA","authors":"Zhenfei Cao, Xu Wang, Shengcheng Yu, Yexiao Yun, Chunrong Fang","doi":"10.1145/3324884.3415300","DOIUrl":"https://doi.org/10.1145/3324884.3415300","url":null,"abstract":"Crowdsourced mobile testing has been widely used due to its convenience and high efficiency [10]. Crowdsourced workers complete testing tasks and record results in test reports. However, the problem of duplicate reports has prevented the efficiency of crowd-sourced mobile testing from further improving. Existing crowd-sourced testing report analysis techniques usually leverage screenshots and text descriptions independently, but fail to recognize the link between these two types of information. In this paper, we present a crowdsourced mobile testing report selection tool, namely STIFA, to extract image and text feature information in reports and establish an image-text-fusion bug context. Based on text and image fusion analysis results, STIFA performs cluster analysis and report selection. To evaluate, we employed STIFA to analyze 150 reports from 2 apps. The results show that STIFA can extract, on average, 95.23% text feature information and 84.15% image feature information. Besides, STIFA reaches an accuracy of 87.64% in detecting duplicate reports. The demo can be found at https://youtu.be/Gw6ptqyQbQY.","PeriodicalId":267160,"journal":{"name":"Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114438030","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
Sosed 紧急求救信号
Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering Pub Date : 2020-12-21 DOI: 10.1145/3324884.3415291
Egor Bogomolov, Yaroslav Golubev, Artyom Lobanov, V. Kovalenko, T. Bryksin
{"title":"Sosed","authors":"Egor Bogomolov, Yaroslav Golubev, Artyom Lobanov, V. Kovalenko, T. Bryksin","doi":"10.1145/3324884.3415291","DOIUrl":"https://doi.org/10.1145/3324884.3415291","url":null,"abstract":"In this paper, we present Sosed, a tool for discovering similar software projects. We use fastText to compute the embeddings of subto-kens into a dense space for 120,000 GitHub projects in 200 languages. Then, we cluster embeddings to identify groups of semantically similar subtokens that reflect topics in source code. We use a dataset of 9 million GitHub projects as a reference search base. To identify similar projects, we compare the distributions of clusters among their subtokens. The tool receives an arbitrary project as input, extracts subtokens in 16 most popular programming languages, computes cluster distribution, and finds projects with the closest distribution in the search base. We labeled subtoken clusters with short descriptions to enable Sosed to produce interpretable output. Sosed is available at https://github.com/JetBrains-Research/sosed/. The tool demo is available at https://www.youtube.com/watch?v=LYLkztCGRt8. The multi-language extractor of subtokens is available separately at https://github.com/JetBrains-Research/buckwheat/.","PeriodicalId":267160,"journal":{"name":"Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124849480","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: 4
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