Microsoft Technology Licensing, LLC

Shuo Zhang

Neelakantan Sundaresan

Shengyu Fu

Christian Alma Bird

Nina Wang

Alexey Svyatkovskiy

Mark Alistair Wilson-Thomas

Michele Tufano

Ying Zhao

David J Poeschl

Zhongyan Guan

David Poeschl

An Artificial Intelligence (AI) driven pull request summarization system utilizes a large language model to classify the code changes of a pull request and to generate a summary of the changes contained in the pull request. The large language model predicts the pull request classification and summarization given a prompt that includes the top-k code changes in the pull request of a repository. The code changes are prioritized based on the most impact that a code change has on the files, methods and classes in the repository. Each of the top-k code changes is linked to a related open issue of the repository, if any. A suggested code reviewer for a code change is then selected from an author or commentator associated with the linked open issue.

AI-driven pull request summarization

A natural language code search service provides idioms or frequently-occurring code patterns for a code fragment based on similar type usage and method/API invocation usage. The search service uses a data mining technique that mines code snippets found from various websites and code snippets generated from a neural model to detect idioms in the code snippets that were previously unknown and which can be reused. A search is initiated through a natural language query within a code development tool or application thereby avoiding the need to switch out of the current application to perform the search.

Natural language code search

A data mining technique is used to find large frequently-occurring source code patterns from methods/APIs that can be used in code development. Simplified trees that represent the syntactic structure and type and method usage of a source code fragment, such as a method, are mined to find closed and maximal frequent subtrees which represent the largest frequently-occurring source code patterns or idioms associated with a particular type and method usage. These idioms are then used in an idiom web service and/or a code completion system to assist users in the development of source code programs.

Frequent source code pattern mining

A code completion tool uses machine learning models to more precisely predict the likelihood of the parameters of a method invocation. A score is computed for each candidate variable that is used to rank the viability of a variable as the intended parameter. The score is a weighted sum of a scope factor, an edit distance factor and a declaration proximity factor. The factors are based on a scope model, a method overload model, and a weight file trained offline on a training set of source code programs utilizing various method invocations.

Code completion of method parameters with machine learning

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Shuo Zhang