{"id":1023021,"date":"2024-04-05T16:36:09","date_gmt":"2024-04-05T23:36:09","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&#038;p=1023021"},"modified":"2024-10-08T14:37:28","modified_gmt":"2024-10-08T21:37:28","slug":"diffy-data-driven-bug-finding-for-configurations","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/diffy-data-driven-bug-finding-for-configurations\/","title":{"rendered":"Diffy: Data-Driven Bug Finding for Configurations"},"content":{"rendered":"<p><span dir=\"ltr\" role=\"presentation\">Configuration errors remain a major cause of system failures and service outages. One promising approach <\/span><span dir=\"ltr\" role=\"presentation\">to identify configuration errors automatically is to learn common usage patterns (and anti-patterns) using <\/span><span dir=\"ltr\" role=\"presentation\">data-driven methods. However, existing data-driven learning approaches analyze only simple configurations <\/span><span dir=\"ltr\" role=\"presentation\">(<\/span><span dir=\"ltr\" role=\"presentation\">e.g.<\/span><span dir=\"ltr\" role=\"presentation\">, those with no hierarchical structure), identify only simple types of issues (<\/span><span dir=\"ltr\" role=\"presentation\">e.g.<\/span><span dir=\"ltr\" role=\"presentation\">, type errors), or require <\/span><span dir=\"ltr\" role=\"presentation\">extensive domain-specific tuning. In this paper, we present<\/span> <span dir=\"ltr\" role=\"presentation\">Diffy<\/span><span dir=\"ltr\" role=\"presentation\">, the first push-button configuration analyzer <\/span><span dir=\"ltr\" role=\"presentation\">that detects likely bugs in structured configurations. From example configurations,<\/span> <span dir=\"ltr\" role=\"presentation\">Diffy<\/span> <span dir=\"ltr\" role=\"presentation\">learns a common <\/span><span dir=\"ltr\" role=\"presentation\">template, with \u201choles\u201d that capture their variation. It then applies unsupervised learning to identify anomalous <\/span><span dir=\"ltr\" role=\"presentation\">template parameters as likely bugs. We evaluate<\/span> <span dir=\"ltr\" role=\"presentation\">Diffy<\/span> <span dir=\"ltr\" role=\"presentation\">on a large cloud provider\u2019s wide-area network, an <\/span><span dir=\"ltr\" role=\"presentation\">operational 5G network testbed, and MySQL configurations, demonstrating its versatility, performance, and <\/span><span dir=\"ltr\" role=\"presentation\">accuracy. During<\/span> <span dir=\"ltr\" role=\"presentation\">Diffy<\/span><span dir=\"ltr\" role=\"presentation\">\u2019s development, it caught and prevented a bug in a configuration timer value that had <\/span><span dir=\"ltr\" role=\"presentation\">previously caused an outage for the cloud provider.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Configuration errors remain a major cause of system failures and service outages. One promising approach to identify configuration errors automatically is to learn common usage patterns (and anti-patterns) using data-driven methods. However, existing data-driven learning approaches analyze only simple configurations (e.g., those with no hierarchical structure), identify only simple types of issues (e.g., type errors), [&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":[13547],"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":[248227,257266],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-1023021","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-systems-and-networking","msr-locale-en_us","msr-field-of-study-computer-network","msr-field-of-study-reliability-computer-networking"],"msr_publishername":"","msr_edition":"","msr_affiliation":"","msr_published_date":"2024-6-1","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"url","viewUrl":"false","id":"false","title":"https:\/\/dl.acm.org\/doi\/10.1145\/3656385","label_id":"243109","label":0}],"msr_related_uploader":"","msr_attachments":[{"id":1023030,"url":"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2024\/04\/diffy_pldi_2024.pdf"},{"id":1023027,"url":"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2024\/04\/Diffy___PLDI_2024.pdf"}],"msr-author-ordering":[{"type":"user_nicename","value":"Siva Kesava Reddy Kakarla","user_id":42540,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Siva Kesava Reddy Kakarla"},{"type":"user_nicename","value":"Francis Y. Yan","user_id":39558,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Francis Y. Yan"},{"type":"user_nicename","value":"Ryan Beckett","user_id":37775,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Ryan Beckett"}],"msr_impact_theme":[],"msr_research_lab":[199565],"msr_event":[],"msr_group":[144899,715138,1089753],"msr_project":[],"publication":[],"video":[],"download":[],"msr_publication_type":"inproceedings","related_content":[],"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/1023021"}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-research-item"}],"version-history":[{"count":2,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/1023021\/revisions"}],"predecessor-version":[{"id":1053747,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/1023021\/revisions\/1053747"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=1023021"}],"wp:term":[{"taxonomy":"msr-content-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-content-type?post=1023021"},{"taxonomy":"msr-research-highlight","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-highlight?post=1023021"},{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=1023021"},{"taxonomy":"msr-publication-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-publication-type?post=1023021"},{"taxonomy":"msr-product-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-product-type?post=1023021"},{"taxonomy":"msr-focus-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-focus-area?post=1023021"},{"taxonomy":"msr-platform","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-platform?post=1023021"},{"taxonomy":"msr-download-source","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-download-source?post=1023021"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=1023021"},{"taxonomy":"msr-post-option","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-post-option?post=1023021"},{"taxonomy":"msr-field-of-study","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-field-of-study?post=1023021"},{"taxonomy":"msr-conference","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-conference?post=1023021"},{"taxonomy":"msr-journal","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-journal?post=1023021"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=1023021"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=1023021"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}