Experience report: investigating bug fixes in machine learning frameworks/libraries
Machine learning (ML) techniques and algorithms have been successfully and widely used in various areas including software engineering tasks. Like other software projects, bugs are also common in ML projects and libraries. In order to more deeply understand the features related to bug fixing in ML p...
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| Veröffentlicht in: | Frontiers of Computer Science 2021-12, Vol.15 (6), p.156212, Article 156212 |
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| container_issue | 6 |
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| container_title | Frontiers of Computer Science |
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| creator | SUN, Xiaobing ZHOU, Tianchi WANG, Rongcun DUAN, Yucong BO, Lili CHANG, Jianming |
| description | Machine learning (ML) techniques and algorithms have been successfully and widely used in various areas including software engineering tasks. Like other software projects, bugs are also common in ML projects and libraries. In order to more deeply understand the features related to bug fixing in ML projects, we conduct an empirical study with 939 bugs from five ML projects by manually examining the bug categories, fixing patterns, fixing scale, fixing duration, and types of maintenance. The results show that (1) there are commonly seven types of bugs in ML programs; (2) twelve fixing patterns are typically used to fix the bugs in ML programs; (3) 68.80% of the patches belong to micro-scale-fix and small-scale-fix; (4) 66.77% of the bugs in ML programs can be fixed within one month; (5) 45.90% of the bug fixes belong to corrective activity from the perspective of software maintenance. Moreover, we perform a questionnaire survey and send them to developers or users of ML projects to validate the results in our empirical study. The results of our empirical study are basically consistent with the feedback from developers. The findings from the empirical study provide useful guidance and insights for developers and users to effectively detect and fix bugs in MLprojects. |
| doi_str_mv | 10.1007/s11704-020-9441-1 |
| format | Article |
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Like other software projects, bugs are also common in ML projects and libraries. In order to more deeply understand the features related to bug fixing in ML projects, we conduct an empirical study with 939 bugs from five ML projects by manually examining the bug categories, fixing patterns, fixing scale, fixing duration, and types of maintenance. The results show that (1) there are commonly seven types of bugs in ML programs; (2) twelve fixing patterns are typically used to fix the bugs in ML programs; (3) 68.80% of the patches belong to micro-scale-fix and small-scale-fix; (4) 66.77% of the bugs in ML programs can be fixed within one month; (5) 45.90% of the bug fixes belong to corrective activity from the perspective of software maintenance. Moreover, we perform a questionnaire survey and send them to developers or users of ML projects to validate the results in our empirical study. The results of our empirical study are basically consistent with the feedback from developers. 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Comput. Sci</addtitle><description>Machine learning (ML) techniques and algorithms have been successfully and widely used in various areas including software engineering tasks. Like other software projects, bugs are also common in ML projects and libraries. In order to more deeply understand the features related to bug fixing in ML projects, we conduct an empirical study with 939 bugs from five ML projects by manually examining the bug categories, fixing patterns, fixing scale, fixing duration, and types of maintenance. The results show that (1) there are commonly seven types of bugs in ML programs; (2) twelve fixing patterns are typically used to fix the bugs in ML programs; (3) 68.80% of the patches belong to micro-scale-fix and small-scale-fix; (4) 66.77% of the bugs in ML programs can be fixed within one month; (5) 45.90% of the bug fixes belong to corrective activity from the perspective of software maintenance. Moreover, we perform a questionnaire survey and send them to developers or users of ML projects to validate the results in our empirical study. The results of our empirical study are basically consistent with the feedback from developers. 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ZHOU, Tianchi ; WANG, Rongcun ; DUAN, Yucong ; BO, Lili ; CHANG, Jianming</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c365t-cc71f9b09778b0504f93d52822ae619284f8849622b3d7e32e29c8a9a336fd83</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Algorithms</topic><topic>Artificial intelligence</topic><topic>Automation</topic><topic>bug fixing</topic><topic>Computer Science</topic><topic>Documentation</topic><topic>empirical study</topic><topic>Engineering research</topic><topic>Fixing</topic><topic>Knowledge management</topic><topic>Libraries</topic><topic>Localization</topic><topic>Machine learning</topic><topic>machine learning project</topic><topic>Maintenance</topic><topic>questionnaire survey</topic><topic>Questionnaires</topic><topic>Research Article</topic><topic>Software engineering</topic><topic>Software upgrading</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>SUN, Xiaobing</creatorcontrib><creatorcontrib>ZHOU, Tianchi</creatorcontrib><creatorcontrib>WANG, Rongcun</creatorcontrib><creatorcontrib>DUAN, Yucong</creatorcontrib><creatorcontrib>BO, Lili</creatorcontrib><creatorcontrib>CHANG, Jianming</creatorcontrib><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><jtitle>Frontiers of Computer Science</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>SUN, Xiaobing</au><au>ZHOU, Tianchi</au><au>WANG, Rongcun</au><au>DUAN, Yucong</au><au>BO, Lili</au><au>CHANG, Jianming</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Experience report: investigating bug fixes in machine learning frameworks/libraries</atitle><jtitle>Frontiers of Computer Science</jtitle><stitle>Front. 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The results show that (1) there are commonly seven types of bugs in ML programs; (2) twelve fixing patterns are typically used to fix the bugs in ML programs; (3) 68.80% of the patches belong to micro-scale-fix and small-scale-fix; (4) 66.77% of the bugs in ML programs can be fixed within one month; (5) 45.90% of the bug fixes belong to corrective activity from the perspective of software maintenance. Moreover, we perform a questionnaire survey and send them to developers or users of ML projects to validate the results in our empirical study. The results of our empirical study are basically consistent with the feedback from developers. The findings from the empirical study provide useful guidance and insights for developers and users to effectively detect and fix bugs in MLprojects.</abstract><cop>Beijing</cop><pub>Higher Education Press</pub><doi>10.1007/s11704-020-9441-1</doi></addata></record> |
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| source | Springer Nature - Complete Springer Journals; ProQuest Central |
| subjects | Algorithms Artificial intelligence Automation bug fixing Computer Science Documentation empirical study Engineering research Fixing Knowledge management Libraries Localization Machine learning machine learning project Maintenance questionnaire survey Questionnaires Research Article Software engineering Software upgrading |
| title | Experience report: investigating bug fixes in machine learning frameworks/libraries |
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