{"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\/pl\/blog\/news\/github-otkryl-narabotki-po-primeneniyu-mashinnogo-obucheniya-dlya-poiska-i-analiza-koda","title":{"rendered":"GitHub udost\u0119pni\u0142 prace nad zastosowaniem uczenia maszynowego do wyszukiwania i analizy kodu","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\/\">przedstawi\u0142<\/a><\/noindex> projekt <noindex><a rel=\"nofollow\" href=\"https:\/\/arxiv.org\/abs\/1909.09436\">CodeSearchNet<\/a><\/noindex>, w ramach kt\u00f3rego przygotowano modele uczenia maszynowego oraz zbiory danych, niezb\u0119dne do analizy, klasyfikacji i rozpoznawania kodu w r\u00f3\u017cnych j\u0119zykach programowania. CodeSearchNet, podobnie jak <noindex><a rel=\"nofollow\" href=\"https:\/\/en.wikipedia.org\/wiki\/ImageNet\">ImageNet<\/a><\/noindex>, zawiera du\u017c\u0105 kolekcj\u0119 fragment\u00f3w kodu, wzbogaconych adnotacjami formu\u0142uj\u0105cymi dzia\u0142ania wykonywane przez ten kod. Sk\u0142adniki do szkolenia modeli i przyk\u0142ady u\u017cycia CodeSearchNet zosta\u0142y napisane w j\u0119zyku Python z wykorzystaniem frameworka Tensorflow oraz <noindex><a rel=\"nofollow\" href=\"https:\/\/github.com\/github\/CodeSearchNet\">rozpowszechniany<\/a><\/noindex> na licencji MIT.<\/p>\n<p>Podczas tworzenia CodeSearchNet zastosowano technologie analizy tekstu w naturalnym j\u0119zyku, kt\u00f3re umo\u017cliwiaj\u0105 systemom uczenia maszynowego uwzgl\u0119dnienie nie tylko syntaktycznych aspekt\u00f3w, ale tak\u017ce znaczenia dzia\u0142a\u0144 wykonywanych przez kod. W GitHub system <noindex><a rel=\"nofollow\" href=\"https:\/\/github.blog\/2018-09-18-towards-natural-language-semantic-code-search\/\">jest stosowane<\/a><\/noindex> w eksperymentach dotycz\u0105cych organizacji semantycznego wyszukiwania kodu przy u\u017cyciu zapyta\u0144 w <noindex><a rel=\"nofollow\" href=\"https:\/\/github.com\/github\/CodeSearchNet\/blob\/master\/resources\/queries.csv\">j\u0119zyku naturalnym<\/a><\/noindex> (na przyk\u0142ad, gdy zapytanie brzmi &#171;sorte lista ci\u0105g\u00f3w&#187;, zwracany jest kod implementuj\u0105cy odpowiednie algorytmy).<\/p>\n<p>Proponowany zbi\u00f3r danych zawiera ponad 2 miliony par &#171;kod-komentarz&#187;, przygotowanych na podstawie oryginalnych tekst\u00f3w istniej\u0105cych otwartych bibliotek. Kod obejmuje pe\u0142ne teksty \u017ar\u00f3d\u0142owe pojedynczych funkcji lub metod, a komentarz opisuje dzia\u0142ania wykonywane przez funkcj\u0119 (dostarczana jest szczeg\u00f3\u0142owa dokumentacja). Obecnie zbiory danych zosta\u0142y przygotowane dla j\u0119zyk\u00f3w Python, JavaScript, Ruby, Go, Java i PHP. Przyk\u0142ady zastosowania zaproponowanych zbior\u00f3w danych do szkolenia r\u00f3\u017cnych typ\u00f3w sieci neuronowych, w tym <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) oraz <noindex><a rel=\"nofollow\" href=\"https:\/\/en.wikipedia.org\/wiki\/Convolutional_neural_network\">1D-CNN+Self-Attention Hybrid<\/a><\/noindex>.<\/p>\n<p>Aby rozwija\u0107 mechanizmy wyszukiwania w naturalnym j\u0119zyku, dodatkowo przygotowano zbi\u00f3r CodeSearchNet Challenge, kt\u00f3ry zawiera<br \/>\n<noindex><a rel=\"nofollow\" href=\"https:\/\/github.com\/github\/CodeSearchNet\/blob\/master\/resources\/queries.csv\">99 typowych<\/a><\/noindex> zapytaniach z oko\u0142o 4 tysi\u0105cami ekspertowych adnotacji, opisuj\u0105cych najbardziej prawdopodobne powi\u0105zania z kodem w zbiorze danych CodeSearchNet Corpus, obejmuj\u0105cym oko\u0142o 6 milion\u00f3w metod i funkcji (<noindex><a rel=\"nofollow\" href=\"https:\/\/github.com\/github\/CodeSearchNet#downloading-data-from-s3\">rozmiar zbioru<\/a><\/noindex> oko\u0142o 20 GB). CodeSearchNet Challenge mo\u017ce s\u0142u\u017cy\u0107 jako punkt odniesienia do oceny skuteczno\u015bci r\u00f3\u017cnych metod wyszukiwania kodu w naturalnym j\u0119zyku. Przy u\u017cyciu narz\u0119dzi <noindex><a rel=\"nofollow\" href=\"https:\/\/www.kubeflow.org\/\">Kubeflow<\/a><\/noindex> przygotowano<br \/>\n<noindex><a rel=\"nofollow\" href=\"https:\/\/github.com\/kubeflow\/examples\/tree\/master\/code_search\">przyk\u0142ad<\/a><\/noindex> silnika do wyszukiwania kodu.<\/p>\n<p><noindex><a rel=\"nofollow\" name=\"link\"><\/a><\/noindex><\/p>\n<p>\u0179r\u00f3d\u0142o: <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-news"],"aioseo_notices":[],"aioseo_head":"\n\t\t<!-- All in One SEO 5.0.1.1 - 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\" \/>\n\t<meta name=\"robots\" content=\"max-image-preview:large\" \/>\n\t<meta name=\"author\" content=\"Yuri Gagarin\"\/>\n\t<link rel=\"canonical\" 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