{"id":151819,"date":"2005-05-01T00:00:00","date_gmt":"2005-05-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/use-of-relative-code-churn-measures-to-predict-system-defect-density\/"},"modified":"2018-10-16T21:50:36","modified_gmt":"2018-10-17T04:50:36","slug":"use-of-relative-code-churn-measures-to-predict-system-defect-density","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/use-of-relative-code-churn-measures-to-predict-system-defect-density\/","title":{"rendered":"Use of Relative Code Churn Measures to Predict System Defect Density"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Software systems evolve over time due to changes in requirements, optimization of code, fixes for security and reliability bugs etc. Code churn, which measures the changes made to a component over a period of time, quantifies the extent of this change. We present a technique for early prediction of system defect density using a set of relative code churn measures that relate the amount of churn to other variables such as component size and the temporal extent of churn. Using statistical regression models, we show that while absolute measures of code churn are poor predictors of defect density, our set of relative measures of code churn is highly predictive of defect density. A case study performed on Windows Server 2003 indicates the validity of the relative code churn measures as early indicators of system defect density. Furthermore, our code churn metric suite is able to discriminate between fault and not fault-prone binaries with an accuracy of 89.0 percent.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Software systems evolve over time due to changes in requirements, optimization of code, fixes for security and reliability bugs etc. Code churn, which measures the changes made to a component over a period of time, quantifies the extent of this change. We present a technique for early prediction of system defect density using a set [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-151819","msr-research-item","type-msr-research-item","status-publish","hentry","msr-locale-en_us"],"msr_publishername":"Association for Computing Machinery, 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Nagappan","user_id":0,"rest_url":false},{"type":"text","value":"Thomas Ball","user_id":0,"rest_url":false},{"type":"user_nicename","value":"tball","user_id":33895,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=tball"},{"type":"user_nicename","value":"nachin","user_id":33044,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=nachin"}],"msr_impact_theme":[],"msr_research_lab":[],"msr_event":[],"msr_group":[],"msr_project":[170993],"publication":[],"video":[],"download":[],"msr_publication_type":"inproceedings","related_content":{"projects":[{"ID":170993,"post_title":"Tools for Software Engineers","post_name":"tools-for-software-engineers","post_type":"msr-project","post_date":"2012-06-29 06:20:39","post_modified":"2021-11-12 09:09:39","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/tools-for-software-engineers\/","post_excerpt":"The mission of Microsoft's One Engineering System (formerly known as Tools for Software Engineers) team is to enable the world's best product engineering teams with world-class tools and systems that help them ship products their customers love. 1ES provides tools and services to cover the full spectrum of the engineering life-cycle, from the developer desktop to product deployment. 1ES focuses on engineering solutions that mitigate the unique scale challenges that Microsoft teams face, both 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