{"title":"Bug报告收集系统(BRCS)","authors":"Arvinder Kaur, Shubhra Goyal Jindal","doi":"10.1109/CONFLUENCE.2017.7943241","DOIUrl":null,"url":null,"abstract":"Open source bug repositories such as Bugzilla and Jira contain substantial data of numerous projects. Each project has various types of issues such as bug reports, improvement to an existing feature, and new feature of the product and task that needs to be done. Each type of issue has various attributes and obtaining such massive data manually is a tedious and time consuming process and could also lead to error prone data. Our prime focus is to collect bug reports automatically to reduce errors made due to human mistakes and improves accuracy. This paper describes a bug report collection system which automates the process of collection of bug reports from the bug repository Jira. This tool is implemented in C# which extracts the data from Jira repository using REST APIs (application program interface). REST APIs provides access to resources via URI paths. Our application makes an HTTP request and parses the response into objects. This tool automatically extracts the information of more than 100 projects of Apache maintained by Jira repository and generates information in the forms of reports. The reports generated contains several bug attributes such as bug Id, One-line description of a bug, priority assigned to bugs, components to which bug belongs to, long description of a bug, affected version, assignee of a bug and several other attributes. These reports can be used for further analysis by using some or all the attributes related to a bug. Some potential applications could be classifying the various types of bugs such as security, memory and concurrency bugs; prioritization of the bugs; prediction of severity of bugs using machine learning etc. Thus, these generated reports are useful for the researchers as they can use to analyse them in different areas such as prioritize the bugs based on the priorities assigned and also classify which types of bugs are more frequent in which type of projects and can save manual effort as well as time.","PeriodicalId":6651,"journal":{"name":"2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence","volume":"06 1","pages":"697-701"},"PeriodicalIF":0.0000,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Bug report collection system (BRCS)\",\"authors\":\"Arvinder Kaur, Shubhra Goyal Jindal\",\"doi\":\"10.1109/CONFLUENCE.2017.7943241\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Open source bug repositories such as Bugzilla and Jira contain substantial data of numerous projects. Each project has various types of issues such as bug reports, improvement to an existing feature, and new feature of the product and task that needs to be done. Each type of issue has various attributes and obtaining such massive data manually is a tedious and time consuming process and could also lead to error prone data. Our prime focus is to collect bug reports automatically to reduce errors made due to human mistakes and improves accuracy. This paper describes a bug report collection system which automates the process of collection of bug reports from the bug repository Jira. This tool is implemented in C# which extracts the data from Jira repository using REST APIs (application program interface). REST APIs provides access to resources via URI paths. Our application makes an HTTP request and parses the response into objects. This tool automatically extracts the information of more than 100 projects of Apache maintained by Jira repository and generates information in the forms of reports. The reports generated contains several bug attributes such as bug Id, One-line description of a bug, priority assigned to bugs, components to which bug belongs to, long description of a bug, affected version, assignee of a bug and several other attributes. These reports can be used for further analysis by using some or all the attributes related to a bug. Some potential applications could be classifying the various types of bugs such as security, memory and concurrency bugs; prioritization of the bugs; prediction of severity of bugs using machine learning etc. Thus, these generated reports are useful for the researchers as they can use to analyse them in different areas such as prioritize the bugs based on the priorities assigned and also classify which types of bugs are more frequent in which type of projects and can save manual effort as well as time.\",\"PeriodicalId\":6651,\"journal\":{\"name\":\"2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence\",\"volume\":\"06 1\",\"pages\":\"697-701\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CONFLUENCE.2017.7943241\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CONFLUENCE.2017.7943241","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Open source bug repositories such as Bugzilla and Jira contain substantial data of numerous projects. Each project has various types of issues such as bug reports, improvement to an existing feature, and new feature of the product and task that needs to be done. Each type of issue has various attributes and obtaining such massive data manually is a tedious and time consuming process and could also lead to error prone data. Our prime focus is to collect bug reports automatically to reduce errors made due to human mistakes and improves accuracy. This paper describes a bug report collection system which automates the process of collection of bug reports from the bug repository Jira. This tool is implemented in C# which extracts the data from Jira repository using REST APIs (application program interface). REST APIs provides access to resources via URI paths. Our application makes an HTTP request and parses the response into objects. This tool automatically extracts the information of more than 100 projects of Apache maintained by Jira repository and generates information in the forms of reports. The reports generated contains several bug attributes such as bug Id, One-line description of a bug, priority assigned to bugs, components to which bug belongs to, long description of a bug, affected version, assignee of a bug and several other attributes. These reports can be used for further analysis by using some or all the attributes related to a bug. Some potential applications could be classifying the various types of bugs such as security, memory and concurrency bugs; prioritization of the bugs; prediction of severity of bugs using machine learning etc. Thus, these generated reports are useful for the researchers as they can use to analyse them in different areas such as prioritize the bugs based on the priorities assigned and also classify which types of bugs are more frequent in which type of projects and can save manual effort as well as time.