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Генерация безопасного кода с LLM: эмпирический опыт

Спрос на генерацию кода с помощью моделей машинного обучения становится все выше. Вместе с тем растут и сомнения в качестве и безопасности полученного кода. Согласно опросу Stack Overflow 57% пользователей недовольны результатом работы AI-помощников.

На Открытой Конференции коллеги из ИСП РАН представили доклад об исправлении уязвимостей в сгенерированном коде с помощью разработанного расширения CodePatchLLM.

Как это работает? Используя статический анализатор Svace, инструмент получает обратную связь об уязвимых функциях и корректирует результат работы модели. В результате CodePatchLLM устраняет до 85% уязвимостей в коде на Python и Java, при этом не теряя производительности.

Прочитать подробнее про расширение можно в статье, а оценить самостоятельно – на Github.

Тайм-код для просмотра доклада: 1:01:50. Полную запись докладов можно найти на сайте конференции ИСП РАН.

@codemining

Code Mining
Генерация безопасного кода с LLM: эмпирический опыт

Спрос на генерацию кода с помощью моделей машинного обучения становится все выше. Вместе с тем растут и сомнения в качестве и безопасности полученного кода. Согласно опросу Stack Overflow 57% пользователей недовольны результатом работы AI-помощников.

На Открытой Конференции коллеги из ИСП РАН представили доклад об исправлении уязвимостей в сгенерированном коде с помощью разработанного расширения CodePatchLLM.

Как это работает? Используя статический анализатор Svace, инструмент получает обратную связь об уязвимых функциях и корректирует результат работы модели. В результате CodePatchLLM устраняет до 85% уязвимостей в коде на Python и Java, при этом не теряя производительности.

Прочитать подробнее про расширение можно в статье, а оценить самостоятельно – на Github.

Тайм-код для просмотра доклада: 1:01:50. Полную запись докладов можно найти на сайте конференции ИСП РАН.

@codemining
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Генерация безопасного кода с LLM: эмпирический опыт

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