Using AI-based coding assistants in practice: State of affairs, perceptions, and ways forward

Abstract

Context:

The last several years saw the emergence of AI assistants for code — multi-purpose AI-based helpers in software engineering. As they become omnipresent in all aspects of software development, it becomes critical to understand their usage patterns.

Objective:

We aim to better understand how specifically developers are using AI assistants, why they are not using them in certain parts of their development workflow, and what needs to be improved in the future.

Methods:

In this work, we carried out a large-scale survey aimed at how AI assistants are used, focusing on specific software development activities and stages. We collected opinions of 481 programmers on five broad activities: (a) implementing new features, (b) writing tests, (c) bug triaging, (d) refactoring, and (e) writing natural-language artifacts, as well as their individual stages.

Results:

Our results provide a novel comparison of different stages where AI assistants are used that is both comprehensive and detailed. It highlights specific activities that developers find less enjoyable and want to delegate to an AI assistant, e.g., writing tests and natural-language artifacts. We also determine more granular stages where AI assistants are used, such as generating tests and generating docstrings, as well as less studied parts of the workflow, such as generating test data. Among the reasons for not using assistants, there are general aspects like trust and company policies, as well as more concrete issues like the lack of project-size context, which can be the focus of the future research.

Conclusion:

The provided analysis highlights stages of software development that developers want to delegate and that are already popular for using AI assistants, which can be a good focus for features aimed to help developers right now. The main reasons for not using AI assistants can serve as a guideline for future work.