{"id":38448,"date":"2019-10-31T22:23:47","date_gmt":"2019-10-31T19:23:47","guid":{"rendered":"https:\/\/prohoster.info\/blog\/github-otkryl-narabotki-po-primeneniyu-mashinnogo-obucheniya-dlya-poiska-i-analiza-koda\/"},"modified":"2019-10-31T22:23:47","modified_gmt":"2019-10-31T19:23:47","slug":"github-otkryl-narabotki-po-primeneniyu-mashinnogo-obucheniya-dlya-poiska-i-analiza-koda","status":"publish","type":"post","link":"https:\/\/prohoster.info\/it\/blog\/novosti-interneta\/github-otkryl-narabotki-po-primeneniyu-mashinnogo-obucheniya-dlya-poiska-i-analiza-koda","title":{"rendered":"GitHub ha aperto progetti sull'uso del machine learning per la ricerca e l'analisi del codice","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"<p>GitHub <noindex><a rel=\"nofollow\" href=\"https:\/\/github.blog\/2019-09-26-introducing-the-codesearchnet-challenge\/\">ha presentato<\/a><\/noindex> progetto <noindex><a rel=\"nofollow\" href=\"https:\/\/arxiv.org\/abs\/1909.09436\">CodeSearchNet<\/a><\/noindex>, all'interno del quale sono stati preparati modelli di machine learning e dataset necessari per l'analisi, la classificazione e la comprensione del codice in vari linguaggi di programmazione. CodeSearchNet, analogamente a <noindex><a rel=\"nofollow\" href=\"https:\/\/en.wikipedia.org\/wiki\/ImageNet\">ImageNet<\/a><\/noindex>, include una vasta collezione di frammenti di codice, dotati di annotazioni che formalizzano le azioni eseguite dal codice. I componenti per l'addestramento dei modelli e gli esempi di utilizzo di CodeSearchNet sono scritti in Python utilizzando il framework Tensorflow e <noindex><a rel=\"nofollow\" href=\"https:\/\/github.com\/github\/CodeSearchNet\">distribuito<\/a><\/noindex> sotto licenza MIT.<\/p>\n<p>Nella creazione di CodeSearchNet sono state utilizzate tecnologie di analisi del linguaggio naturale, che permettono ai sistemi di machine learning di considerare non solo le caratteristiche sintattiche, ma anche il significato delle azioni eseguite dal codice. Su GitHub, il sistema <noindex><a rel=\"nofollow\" href=\"https:\/\/github.blog\/2018-09-18-towards-natural-language-semantic-code-search\/\">applicato<\/a><\/noindex> \u00e8 stato impiegato in esperimenti sull'organizzazione della ricerca semantica del codice utilizzando query in <noindex><a rel=\"nofollow\" href=\"https:\/\/github.com\/github\/CodeSearchNet\/blob\/master\/resources\/queries.csv\">linguaggio naturale<\/a><\/noindex> (ad esempio, quando si richiede \u00abordinamento della lista delle righe\u00bb, viene restituito un codice con l'implementazione degli algoritmi corrispondenti).<\/p>\n<p>Il set di dati proposto include oltre 2 milioni di associazioni \u00abcodice-commento\u00bb, preparate sulla base dei testi sorgenti di librerie pubbliche esistenti. Il codice copre il testo sorgente completo di singole funzioni o metodi, mentre il commento descrive le azioni svolte dalla funzione (\u00e8 fornita una documentazione dettagliata). Attualmente, i set di dati sono stati preparati per i linguaggi Python, JavaScript, Ruby, Go, Java e PHP. Sono forniti esempi di utilizzo dei set di dati proposti per l'addestramento di vari tipi di reti neurali, inclusi <noindex><a rel=\"nofollow\" href=\"https:\/\/en.wikipedia.org\/wiki\/Bag-of-words_model\">Neural-Bag-Of-Words<\/a><\/noindex>, <noindex><a rel=\"nofollow\" href=\"https:\/\/en.wikipedia.org\/wiki\/Recurrent_neural_network\">RNN<\/a><\/noindex>, <noindex><a rel=\"nofollow\" href=\"https:\/\/en.wikipedia.org\/wiki\/Transformer_(machine_learning_model)\">Self-Attention<\/a><\/noindex> (BERT) e <noindex><a rel=\"nofollow\" href=\"https:\/\/en.wikipedia.org\/wiki\/Convolutional_neural_network\">1D-CNN+Self-Attention Hybrid<\/a><\/noindex>.<\/p>\n<p>Per sviluppare meccanismi di ricerca in linguaggio naturale, \u00e8 stato preparato un set aggiuntivo chiamato CodeSearchNet Challenge, che include<br \/>\n<noindex><a rel=\"nofollow\" href=\"https:\/\/github.com\/github\/CodeSearchNet\/blob\/master\/resources\/queries.csv\">99 tipologie di<\/a><\/noindex> query con circa 4.000 annotazioni esperte, descriventi le associazioni pi\u00f9 probabili al codice nel set di dati CodeSearchNet Corpus, che comprende circa 6 milioni di metodi e funzioni (<noindex><a rel=\"nofollow\" href=\"https:\/\/github.com\/github\/CodeSearchNet#downloading-data-from-s3\">dimensione del set<\/a><\/noindex> circa 20 GB). CodeSearchNet Challenge pu\u00f2 fungere da benchmark per valutare l'efficacia di diversi metodi di ricerca di codice in linguaggio naturale. Usando il toolkit <noindex><a rel=\"nofollow\" href=\"https:\/\/www.kubeflow.org\/\">Kubeflow<\/a><\/noindex> \u00e8 stato preparato<br \/>\n<noindex><a rel=\"nofollow\" href=\"https:\/\/github.com\/kubeflow\/examples\/tree\/master\/code_search\">esempio<\/a><\/noindex> un motore per la ricerca di codice.<\/p>\n<p><noindex><a rel=\"nofollow\" name=\"link\"><\/a><\/noindex><\/p>\n<p>Fonte: <a \ncontent=\"nofollow\" rel=\"nofollow\" href=\"https:\/\/www.opennet.ru\/opennews\/art.shtml?num=51570\">opennet.ru<\/a><\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"<p>GitHub \u043f\u0440\u0435\u0434\u0441\u0442\u0430\u0432\u0438\u043b \u043f\u0440\u043e\u0435\u043a\u0442 CodeSearchNet, \u0432 \u0440\u0430\u043c\u043a\u0430\u0445 \u043a\u043e\u0442\u043e\u0440\u043e\u0433\u043e \u043f\u043e\u0434\u0433\u043e\u0442\u043e\u0432\u043b\u0435\u043d\u044b \u043c\u043e\u0434\u0435\u043b\u0438 \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u0433\u043e \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f \u0438 \u043d\u0430\u0431\u043e\u0440\u044b \u0434\u0430\u043d\u043d\u044b\u0445, \u043d\u0435\u043e\u0431\u0445\u043e\u0434\u0438\u043c\u044b\u0435 \u0434\u043b\u044f \u0440\u0430\u0437\u0431\u043e\u0440\u0430, \u043a\u043b\u0430\u0441\u0441\u0438\u0444\u0438\u043a\u0430\u0446\u0438\u0438 \u0438 \u0430\u043d\u0430\u043b\u0438\u0437\u0430 \u043a\u043e\u0434\u0430 \u043d\u0430 \u0440\u0430\u0437\u043b\u0438\u0447\u043d\u044b\u0445 \u044f\u0437\u044b\u043a\u0430\u0445 \u043f\u0440\u043e\u0433\u0440\u0430\u043c\u043c\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u044f. CodeSearchNet, \u043f\u043e \u0430\u043d\u0430\u043b\u043e\u0433\u0438\u0438 \u0441 ImageNet, \u0432\u043a\u043b\u044e\u0447\u0430\u0435\u0442 \u0431\u043e\u043b\u044c\u0448\u0443\u044e \u043a\u043e\u043b\u043b\u0435\u043a\u0446\u0438\u044e \u043e\u0442\u0440\u044b\u0432\u043a\u043e\u0432 \u043a\u043e\u0434\u0430, \u0441\u043d\u0430\u0431\u0436\u0451\u043d\u043d\u044b\u0445 \u0430\u043d\u043d\u043e\u0442\u0430\u0446\u0438\u044f\u043c\u0438, \u0444\u043e\u0440\u043c\u0430\u043b\u0438\u0437\u0443\u044e\u0449\u0438\u043c\u0438 \u0432\u044b\u043f\u043e\u043b\u043d\u044f\u0435\u043c\u044b\u0435 \u043a\u043e\u0434\u043e\u043c \u0434\u0435\u0439\u0441\u0442\u0432\u0438\u044f. \u041a\u043e\u043c\u043f\u043e\u043d\u0435\u043d\u0442\u044b \u0434\u043b\u044f \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f \u043c\u043e\u0434\u0435\u043b\u0435\u0439 \u0438 \u043f\u0440\u0438\u043c\u0435\u0440\u044b \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u044f CodeSearchNet \u043d\u0430\u043f\u0438\u0441\u0430\u043d\u044b \u043d\u0430 \u044f\u0437\u044b\u043a\u0435 Python \u0441 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u0435\u043c [&hellip;]<\/p>\n","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[702],"tags":[],"class_list":["post-38448","post","type-post","status-publish","format-standard","hentry","category-novosti-interneta"],"aioseo_notices":[],"aioseo_head":"\n\t\t<!-- All in One SEO 4.9.10 - aioseo.com -->\n\t<meta name=\"description\" content=\"GitHub \u043f\u0440\u0435\u0434\u0441\u0442\u0430\u0432\u0438\u043b \u043f\u0440\u043e\u0435\u043a\u0442 CodeSearchNet, \u0432 \u0440\u0430\u043c\u043a\u0430\u0445 \u043a\u043e\u0442\u043e\u0440\u043e\u0433\u043e \u043f\u043e\u0434\u0433\u043e\u0442\u043e\u0432\u043b\u0435\u043d\u044b \u043c\u043e\u0434\u0435\u043b\u0438 \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u0433\u043e \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f \u0438 \u043d\u0430\u0431\u043e\u0440\u044b \u0434\u0430\u043d\u043d\u044b\u0445, \u043d\u0435\u043e\u0431\u0445\u043e\u0434\u0438\u043c\u044b\u0435 \u0434\u043b\u044f \u0440\u0430\u0437\u0431\u043e\u0440\u0430, \u043a\u043b\u0430\u0441\u0441\u0438\u0444\u0438\u043a\u0430\u0446\u0438\u0438 \u0438 \u0430\u043d\u0430\u043b\u0438\u0437\u0430 \u043a\u043e\u0434\u0430 \u043d\u0430 \u0440\u0430\u0437\u043b\u0438\u0447\u043d\u044b\u0445 \u044f\u0437\u044b\u043a\u0430\u0445 \u043f\u0440\u043e\u0433\u0440\u0430\u043c\u043c\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u044f. 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CodeSearchNet, \u043f\u043e \u0430\u043d\u0430\u043b\u043e\u0433\u0438\u0438 \u0441 ImageNet, \u0432\u043a\u043b\u044e\u0447\u0430\u0435\u0442 \u0431\u043e\u043b\u044c\u0448\u0443\u044e \u043a\u043e\u043b\u043b\u0435\u043a\u0446\u0438\u044e \u043e\u0442\u0440\u044b\u0432\u043a\u043e\u0432 \u043a\u043e\u0434\u0430, \u0441\u043d\u0430\u0431\u0436\u0451\u043d\u043d\u044b\u0445 \u0430\u043d\u043d\u043e\u0442\u0430\u0446\u0438\u044f\u043c\u0438, \u0444\u043e\u0440\u043c\u0430\u043b\u0438\u0437\u0443\u044e\u0449\u0438\u043c\u0438 \u0432\u044b\u043f\u043e\u043b\u043d\u044f\u0435\u043c\u044b\u0435 \u043a\u043e\u0434\u043e\u043c \u0434\u0435\u0439\u0441\u0442\u0432\u0438\u044f. \u041a\u043e\u043c\u043f\u043e\u043d\u0435\u043d\u0442\u044b \u0434\u043b\u044f \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f \u043c\u043e\u0434\u0435\u043b\u0435\u0439 \u0438 \u043f\u0440\u0438\u043c\u0435\u0440\u044b \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u044f CodeSearchNet \u043d\u0430\u043f\u0438\u0441\u0430\u043d\u044b \u043d\u0430 \u044f\u0437\u044b\u043a\u0435 Python \u0441 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u0435\u043c\" \/>\n\t\t<meta property=\"og:url\" content=\"https:\/\/prohoster.info\/it\/blog\/novosti-interneta\/github-otkryl-narabotki-po-primeneniyu-mashinnogo-obucheniya-dlya-poiska-i-analiza-koda\" \/>\n\t\t<meta property=\"og:image\" content=\"https:\/\/prohoster.info\/wp-content\/uploads\/2021\/11\/logo-350.jpg\" \/>\n\t\t<meta property=\"og:image:secure_url\" content=\"https:\/\/prohoster.info\/wp-content\/uploads\/2021\/11\/logo-350.jpg\" \/>\n\t\t<meta property=\"og:image:width\" content=\"350\" \/>\n\t\t<meta property=\"og:image:height\" content=\"350\" \/>\n\t\t<meta property=\"article:published_time\" content=\"2019-10-31T19:23:47+00:00\" \/>\n\t\t<meta property=\"article:modified_time\" content=\"2019-10-31T19:23:47+00:00\" \/>\n\t\t<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/prohoster\" \/>\n\t\t<meta property=\"article:author\" content=\"https:\/\/www.facebook.com\/prohoster\" \/>\n\t\t<!-- All in One SEO -->\n\n","aioseo_head_json":{"title":"\ud83e\udd47GitHub ha rilasciato i risultati sull'uso del machine learning per la ricerca e l'analisi del codice | ProHoster","description":"GitHub ha presentato il progetto CodeSearchNet, che include modelli di apprendimento automatico e set di dati necessari per l'analisi, classificazione e valutazione del codice in vari linguaggi di programmazione. Come ImageNet, CodeSearchNet comprende una vasta collezione di frammenti di codice accompagnati da annotazioni che formalizzano le azioni eseguite dal codice. 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