Pub Date : 2021-05-01DOI: 10.1109/ICSE-Companion52605.2021.00081
A. Alshammari, Christopher Morris, Michael C Hilton, Jonathan Bell
This is an extended abstract that describes the code and data artifact [1] of our paper “FlakeFlagger: Predicting Flakiness Without Rerunning Tests” [2]. The goal of our artifact is to let researchers reproduce our experiment on our provided flaky dataset or reuse our tool on different flaky tests datasets.
{"title":"FlakeFlagger: Predicting Flakiness Without Rerunning Tests","authors":"A. Alshammari, Christopher Morris, Michael C Hilton, Jonathan Bell","doi":"10.1109/ICSE-Companion52605.2021.00081","DOIUrl":"https://doi.org/10.1109/ICSE-Companion52605.2021.00081","url":null,"abstract":"This is an extended abstract that describes the code and data artifact [1] of our paper “FlakeFlagger: Predicting Flakiness Without Rerunning Tests” [2]. The goal of our artifact is to let researchers reproduce our experiment on our provided flaky dataset or reuse our tool on different flaky tests datasets.","PeriodicalId":136929,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122844282","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}
PLELog is a novel approach for log-based anomaly detection via probabilistic label estimation. It is designed to effectively detect anomalies in unlabeled logs and meanwhile avoid the manual labeling effort for training data generation. We use semantic information within log events as fixed-length vectors and apply HDBSCAN to automatically clustering log sequences. After that, we also propose a Probabilistic Label Estimation approach to reduce the noises introduced by error labeling and put "labeled" instances into an attention-based GRU network for training. We conducted an empirical study to evaluate the effectiveness of PLELog on two open-source log data (i.e., HDFS and BGL). The results demonstrate the effectiveness of PLELog. In particular, PLELog has been applied to two real-world systems from a university and a large corporation, further demonstrating its practicability.
{"title":"PLELog: Semi-Supervised Log-Based Anomaly Detection via Probabilistic Label Estimation","authors":"Lin Yang, Junjie Chen, Zan Wang, Weijing Wang, Jiajun Jiang, Xuyuan Dong, Wenbin Zhang","doi":"10.1109/ICSE-Companion52605.2021.00106","DOIUrl":"https://doi.org/10.1109/ICSE-Companion52605.2021.00106","url":null,"abstract":"PLELog is a novel approach for log-based anomaly detection via probabilistic label estimation. It is designed to effectively detect anomalies in unlabeled logs and meanwhile avoid the manual labeling effort for training data generation. We use semantic information within log events as fixed-length vectors and apply HDBSCAN to automatically clustering log sequences. After that, we also propose a Probabilistic Label Estimation approach to reduce the noises introduced by error labeling and put \"labeled\" instances into an attention-based GRU network for training. We conducted an empirical study to evaluate the effectiveness of PLELog on two open-source log data (i.e., HDFS and BGL). The results demonstrate the effectiveness of PLELog. In particular, PLELog has been applied to two real-world systems from a university and a large corporation, further demonstrating its practicability.","PeriodicalId":136929,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124576041","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}
Pub Date : 2021-05-01DOI: 10.1109/ICSE-Companion52605.2021.00140
R. Torkar, Carlo A. Furia, R. Feldt
n/a
{"title":"Bayesian Data Analysis for Software Engineering","authors":"R. Torkar, Carlo A. Furia, R. Feldt","doi":"10.1109/ICSE-Companion52605.2021.00140","DOIUrl":"https://doi.org/10.1109/ICSE-Companion52605.2021.00140","url":null,"abstract":"n/a","PeriodicalId":136929,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128120413","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}
Pub Date : 2021-05-01DOI: 10.1109/ICSE-Companion52605.2021.00142
Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson
Following various real-world incidents involving both purely digital and cyber-physical Artificial Intelligence (AI) systems, AI Ethics has become a prominent topic of discussion in both research and practice, accompanied by various calls for trustworthy AI systems. Failures are often costly, and many of them stem from issues that could have been avoided during development. For example, AI ethics issues, such as data privacy are currently highly topical. However, implementing AI ethics in practice remains a challenge for organizations. Various guidelines have been published to aid companies in doing so, but these have not seen widespread adoption and may feel impractical. In this technical briefing, we discuss how to implement AI ethics. We showcase a method developed for this purpose, ECCOLA, which is based on academic research. ECCOLA is intended to make AI ethics more practical for developers in order to make it easier to incorporate into AI development to create trustworthy AI systems. It is a sprint-based and adaptive tool designed for agile development that facilitates reflection within the development team and helps developers make ethics into tangible product backlog items.
{"title":"Technical Briefing: Hands-On Session on the Development of Trustworthy AI Software","authors":"Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson","doi":"10.1109/ICSE-Companion52605.2021.00142","DOIUrl":"https://doi.org/10.1109/ICSE-Companion52605.2021.00142","url":null,"abstract":"Following various real-world incidents involving both purely digital and cyber-physical Artificial Intelligence (AI) systems, AI Ethics has become a prominent topic of discussion in both research and practice, accompanied by various calls for trustworthy AI systems. Failures are often costly, and many of them stem from issues that could have been avoided during development. For example, AI ethics issues, such as data privacy are currently highly topical. However, implementing AI ethics in practice remains a challenge for organizations. Various guidelines have been published to aid companies in doing so, but these have not seen widespread adoption and may feel impractical. In this technical briefing, we discuss how to implement AI ethics. We showcase a method developed for this purpose, ECCOLA, which is based on academic research. ECCOLA is intended to make AI ethics more practical for developers in order to make it easier to incorporate into AI development to create trustworthy AI systems. It is a sprint-based and adaptive tool designed for agile development that facilitates reflection within the development team and helps developers make ethics into tangible product backlog items.","PeriodicalId":136929,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133161069","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}
During software maintenance, developers may need to migrate an already in-use library to another library with similar functionalities. However, it is difficult to make the optimal migration decision with limited information, knowledge, or expertise. In this paper, we present MigrationAdvisor, an evidence-based tool to recommend library migration targets through intelligent analysis upon a large number of GitHub repositories and Java libraries. The migration advisories are provided through a search engine style web service where developers can seek migration suggestions for a specific library. We conduct systematic evaluations on the correctness of results, and evaluate the usefulness of the tool by collecting usage feedback from industry developers. Video: https://youtu.be/4I75W22TqwQ.
{"title":"MigrationAdvisor: Recommending Library Migrations from Large-Scale Open-Source Data","authors":"Hao He, Yulin Xu, Xiaoyan Cheng, Guangtai Liang, Minghui Zhou","doi":"10.1109/ICSE-Companion52605.2021.00023","DOIUrl":"https://doi.org/10.1109/ICSE-Companion52605.2021.00023","url":null,"abstract":"During software maintenance, developers may need to migrate an already in-use library to another library with similar functionalities. However, it is difficult to make the optimal migration decision with limited information, knowledge, or expertise. In this paper, we present MigrationAdvisor, an evidence-based tool to recommend library migration targets through intelligent analysis upon a large number of GitHub repositories and Java libraries. The migration advisories are provided through a search engine style web service where developers can seek migration suggestions for a specific library. We conduct systematic evaluations on the correctness of results, and evaluate the usefulness of the tool by collecting usage feedback from industry developers. Video: https://youtu.be/4I75W22TqwQ.","PeriodicalId":136929,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":"36 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131958657","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}
Pub Date : 2021-05-01DOI: 10.1109/ICSE-Companion52605.2021.00048
P. Valle
Autonomous Vehicles (AVs) will revolutionize the way people travel by car. However, in order to deploy autonomous vehicles, effective testing techniques are required. The driving quality of an AV should definitely be considered when testing such systems. However, as in other complex systems, determining the outcome of a test in the driving quality on an AV can be extremely complex. To solve this issue, in this paper we explore the application of Quality-of-Service (QoS) aware metamorphic testing to test AVs modeled in MATLAB/Simulink, one of the predominant modeling tools in the market. We first defined a set of QoS measures applied to AVs by considering as input a recent study. With them, we define metamorphic relations. Lastly we assess the approach in an AV modeled in Simulink by using mutation testing. The results suggests that our approach is effective at detecting faults.
{"title":"Metamorphic Testing of Autonomous Vehicles: A Case Study on Simulink","authors":"P. Valle","doi":"10.1109/ICSE-Companion52605.2021.00048","DOIUrl":"https://doi.org/10.1109/ICSE-Companion52605.2021.00048","url":null,"abstract":"Autonomous Vehicles (AVs) will revolutionize the way people travel by car. However, in order to deploy autonomous vehicles, effective testing techniques are required. The driving quality of an AV should definitely be considered when testing such systems. However, as in other complex systems, determining the outcome of a test in the driving quality on an AV can be extremely complex. To solve this issue, in this paper we explore the application of Quality-of-Service (QoS) aware metamorphic testing to test AVs modeled in MATLAB/Simulink, one of the predominant modeling tools in the market. We first defined a set of QoS measures applied to AVs by considering as input a recent study. With them, we define metamorphic relations. Lastly we assess the approach in an AV modeled in Simulink by using mutation testing. The results suggests that our approach is effective at detecting faults.","PeriodicalId":136929,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":"230 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124544173","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}
Pub Date : 2021-05-01DOI: 10.1109/ICSE-Companion52605.2021.00067
Nathan Cooper, Carlos Bernal-Cárdenas, Oscar Chaparro, Kevin Moran, D. Poshyvanyk
When a bug manifests in a user-facing application, it is likely to be exposed through the graphical user interface (GUI). Given the importance of visual information to the process of identifying and understanding such bugs, users are increasingly making use of screenshots and screen-recordings as a means to report issues to developers. Due to their graphical nature, screen-recordings present challenges for automated analysis that preclude the use of current duplicate bug report detection techniques. This paper describes in detail our reproduction package artifact for TANGO, a duplicate detection technique that operates purely on video-based bug reports by leveraging both visual and textual information to overcome these challenges and aid developers in this task. Specifically, this reproduction package contains the data and code that enables our TANGO’s empirical evaluation replication and future research in the area of duplicate video-based bug report detection.
{"title":"A Replication Package for It Takes Two to Tango: Combining Visual and Textual Information for Detecting Duplicate Video-Based Bug Reports","authors":"Nathan Cooper, Carlos Bernal-Cárdenas, Oscar Chaparro, Kevin Moran, D. Poshyvanyk","doi":"10.1109/ICSE-Companion52605.2021.00067","DOIUrl":"https://doi.org/10.1109/ICSE-Companion52605.2021.00067","url":null,"abstract":"When a bug manifests in a user-facing application, it is likely to be exposed through the graphical user interface (GUI). Given the importance of visual information to the process of identifying and understanding such bugs, users are increasingly making use of screenshots and screen-recordings as a means to report issues to developers. Due to their graphical nature, screen-recordings present challenges for automated analysis that preclude the use of current duplicate bug report detection techniques. This paper describes in detail our reproduction package artifact for TANGO, a duplicate detection technique that operates purely on video-based bug reports by leveraging both visual and textual information to overcome these challenges and aid developers in this task. Specifically, this reproduction package contains the data and code that enables our TANGO’s empirical evaluation replication and future research in the area of duplicate video-based bug report detection.","PeriodicalId":136929,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124327913","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}
Pub Date : 2021-05-01DOI: 10.1109/ICSE-Companion52605.2021.00083
Ansong Ni, Daniel Ramos, Aidan Z. H. Yang, I. Lynce, Vasco M. Manquinho, R. Martins, Claire Le Goues
This paper provides provides a guide to the replication package of SOAR: A Synthesis Approach for Data Science API Refactoring. Our replication package provides a reliable way of reproducing results of the paper using a virtual machine. The replication packages includes scripts to generate the tables and figures presented in results section of the paper. Details on how to use those scripts and run SOAR are explained throughout this guide.
{"title":"Replication of SOAR: A Synthesis Approach for Data Science API Refactoring","authors":"Ansong Ni, Daniel Ramos, Aidan Z. H. Yang, I. Lynce, Vasco M. Manquinho, R. Martins, Claire Le Goues","doi":"10.1109/ICSE-Companion52605.2021.00083","DOIUrl":"https://doi.org/10.1109/ICSE-Companion52605.2021.00083","url":null,"abstract":"This paper provides provides a guide to the replication package of SOAR: A Synthesis Approach for Data Science API Refactoring. Our replication package provides a reliable way of reproducing results of the paper using a virtual machine. The replication packages includes scripts to generate the tables and figures presented in results section of the paper. Details on how to use those scripts and run SOAR are explained throughout this guide.","PeriodicalId":136929,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117157073","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}
Pub Date : 2021-05-01DOI: 10.1109/ICSE-Companion52605.2021.00066
Chengcheng Wan, Shicheng Liu, H. Hoffmann, M. Maire, Shan Lu
This artifact aims to provide benchmark suite, data, and script used in our study "Are Machine Learning Cloud APIs Used Correctly?". We collected a suite of 360 non-trivial applications that use ML cloud APIs for manual study. We also developed checkers and tool to detect and fix API mis-uses. We hope this artifact can motivate and help future research to further tackle ML API mis-uses. All related data are available online.
{"title":"A Replication of Are Machine Learning Cloud APIs Used Correctly","authors":"Chengcheng Wan, Shicheng Liu, H. Hoffmann, M. Maire, Shan Lu","doi":"10.1109/ICSE-Companion52605.2021.00066","DOIUrl":"https://doi.org/10.1109/ICSE-Companion52605.2021.00066","url":null,"abstract":"This artifact aims to provide benchmark suite, data, and script used in our study \"Are Machine Learning Cloud APIs Used Correctly?\". We collected a suite of 360 non-trivial applications that use ML cloud APIs for manual study. We also developed checkers and tool to detect and fix API mis-uses. We hope this artifact can motivate and help future research to further tackle ML API mis-uses. All related data are available online.","PeriodicalId":136929,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129596171","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}
Pub Date : 2021-05-01DOI: 10.1109/ICSE-Companion52605.2021.00060
Fei Shao
In order to prevent information retrieval (IR) and robotic process automation (RPA) tools from functioning improperly due to website evolution, it is important to develop web monitoring tools to monitor changes in a website and report them to the developers and testers. Existing monitoring tools commonly make use of DOM-tree based similarity and visual analysis between different versions of web pages. However, DOM-tree based similarity suffers are prone to false positives, since they cannot identify content-based changes (i.e., contents refreshed every time a web page is retrieved) and GUI widget evolution (e.g., moving a button). Such imprecision adversely affect IR tools or test scripts. To address this problem, we propose approach, WebEvo, that first performs DOM-based change detection, and then leverages historic pages to identify the regions that represent content-based changes, which can be safely ignored. Further, to identify refactoring changes that preserve semantics and appearances of GUI widgets, WebEvo adapts computer vision (CV) techniques to identify the mappings of the GUI widgets from the old web page to the new web page on an element-by-element basis. We evaluated WebEvo on 10 real-world websites from 8 popular categories to demonstrate the superiority of WebEvo over the existing work that relies on DOM-tree based detection or whole-page visual comparison.
{"title":"WebEvo: Taming Web Application Evolution via Semantic Structure Change Detection","authors":"Fei Shao","doi":"10.1109/ICSE-Companion52605.2021.00060","DOIUrl":"https://doi.org/10.1109/ICSE-Companion52605.2021.00060","url":null,"abstract":"In order to prevent information retrieval (IR) and robotic process automation (RPA) tools from functioning improperly due to website evolution, it is important to develop web monitoring tools to monitor changes in a website and report them to the developers and testers. Existing monitoring tools commonly make use of DOM-tree based similarity and visual analysis between different versions of web pages. However, DOM-tree based similarity suffers are prone to false positives, since they cannot identify content-based changes (i.e., contents refreshed every time a web page is retrieved) and GUI widget evolution (e.g., moving a button). Such imprecision adversely affect IR tools or test scripts. To address this problem, we propose approach, WebEvo, that first performs DOM-based change detection, and then leverages historic pages to identify the regions that represent content-based changes, which can be safely ignored. Further, to identify refactoring changes that preserve semantics and appearances of GUI widgets, WebEvo adapts computer vision (CV) techniques to identify the mappings of the GUI widgets from the old web page to the new web page on an element-by-element basis. We evaluated WebEvo on 10 real-world websites from 8 popular categories to demonstrate the superiority of WebEvo over the existing work that relies on DOM-tree based detection or whole-page visual comparison.","PeriodicalId":136929,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129733501","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}
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