Google AI Introduces DIDACT for Training ML Machine Learning Models for Software Engineering Tasks

Building software doesn’t happen in one giant leap. Step by step, improve until it’s ready to be merged into a code repository: edit, run unit tests, fix build errors, respond to code reviews, edit again, satisfy linters, and fix additional bugs .

A new work from Google presents DIDACT, a technique for training large machine learning (ML) models in the context of software engineering. DIDACT is unusual in that it derives training data from the final software development product and the entire process. The model can learn the dynamics of software development and become more in line with how developers spend their time if it is exposed to the contexts developers observe. At the same time, they work, together with their actions, in reaction to those settings. The team uses Google’s software development tools to significantly increase the volume and variety of data on developer activity compared to previous research.

Google’s software engineers can benefit from DIDACT’s ML models as they tap into the interactions between engineers and tools to make suggestions or improve the actions they take when working on software engineering projects. To achieve this, the team established a series of tasks based on the actions of an individual developer, such as fixing a failed build, anticipating and responding to a code review comment, renaming a variable, editing a file, etc. Each task is approached using the same formalism, which takes a status (a code file), an intent (annotations unique to the job, including code review comments or compiler errors), and returns an action (the actual solution to the problem). With the help of the state-intent-action formalism, users can generically represent various tasks. This action can be thought of as a miniature programming language that can be expanded to accommodate new features. Includes code formatting, comments, variable renaming, error highlighting, etc. This scripting language is known as DevScript.

DIDACT performs well in one-time care activities. Some unexpected talents emerge thanks to the multimodal character of DIDACT, which evokes behaviors that emerge on larger scales. History enhancement is one such feature that can be used upon request. Based on their previous actions, the model can offer a more informed recommendation to the developer. Code completion with increased history is a good example of an activity that demonstrates this potential.

The ability of models to infer the correct next steps in video editing is greatly enhanced by the availability of context. Based on past changes, the model can decide where to make the next change, making change prediction an even more powerful task with increased history. An example is when a developer deletes a function parameter: (1) The model uses history to correctly predict an update to the docstring (2) which removes the deleted parameter (without the human developer manually hovering over it) and to update a statement in function (3) in a syntactically (and probably semantically) correct way. Without context, the model wouldn’t know whether the developer intentionally removed a function parameter (as part of a larger change) or accidentally (in which case it should be reverted).

The model has further potential. For example, the model is given an empty file and asked to predict what changes need to be made next until it has written a whole lot of code. The researchers say that surprisingly, the model wrote the code in a logical, step-by-step way that a programmer would understand. The process started by developing a functional skeleton that included imports, flags, and a main function. Later, it expanded to allow things like reading and writing to files and filtering lines using a user-supplied regular expression, requiring changes throughout the file, such as adding new flags.

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