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A great part of software development involves conceptualizing orcommunicating the underlying procedures and logic that needs to be expressed inprograms. One major difficulty of programming is turning concept into code,especially when dealing with the APIs of unfamiliar libraries. Recently, therehas been a proliferation of machine learning methods for code generation andretrieval from natural language queries, but these have primarily beenevaluated purely based on retrieval accuracy or overlap of generated code withdeveloper-written code, and the actual effect of these methods on the developerworkflow is surprisingly unattested. We perform the first comprehensiveinvestigation of the promise and challenges of using such technology inside theIDE, asking "at the current state of technology does it improve developerproductivity or accuracy, how does it affect the developer experience, and whatare the remaining gaps and challenges?" We first develop a plugin for the IDEthat implements a hybrid of code generation and code retrieval functionality,and orchestrate virtual environments to enable collection of many user events.We ask developers with various backgrounds to complete 14 Python programmingtasks ranging from basic file manipulation to machine learning or datavisualization, with or without the help of the plugin. While qualitativesurveys of developer experience are largely positive, quantitative results withregards to increased productivity, code quality, or program correctness areinconclusive. Analysis identifies several pain points that could improve theeffectiveness of future machine learning based code generation/retrievaldeveloper assistants, and demonstrates when developers prefer code generationover code retrieval and vice versa. We release all data and software to pavethe road for future empirical studies and development of better models.

In-IDE Code Generation from Natural Language: Promise and Challenges