Publication Type

Journal Article

Version

publishedVersion

Publication Date

1-2025

Abstract

Currently, large pre-trained language models are widely applied in neural code completion systems. Though large code models significantly outperform their smaller counterparts, around 70% of displayed code completions from Github Copilot are not accepted by developers. Being reviewed but not accepted, their help to developer productivity is considerably limited and may conversely aggravate the workload of developers, as the code completions are automatically and actively generated in state-of-the-art code completion systems as developers type out once the service is enabled. Even worse, considering the high cost of the large code models, it is a huge waste of computing resources and energy, which severely goes against the sustainable development principle of AI technologies. However, such waste has never been realized, not to mention effectively addressed, in the research community for neural code completion. Hence, preventing such unhelpful code completions from happening in a cost-friendly way is of urgent need. To fill this significant gap, we first investigate the prompts of unhelpful code completions, called “low-return prompts.” We empirically identify four observable patterns in low-return prompts, each lacking necessary information, making it difficult to address through enhancements to the model’s accuracy alone. This demonstrates the feasibility of identifying such low-return prompts based on the prompts themselves. Motivated by this finding, we propose an early-rejection mechanism to turn down low-return prompts by foretelling the code completion qualities. The prompts that are estimated to receive unhelpful code completions will not be sent to the model. Furthermore, we investigated five types of estimators to demonstrate the feasibility of the mechanism. The experimental results show that the estimator can reject 20% of code completion requests with a 97.4% precision. To the best of our knowledge, it is the first systemic approach to address the problem of unhelpful code completions and this work also sheds light on an important research direction of large code models.

Keywords

deep learning, code completion, large language model, productivity

Discipline

Software Engineering

Research Areas

Software and Cyber-Physical Systems

Areas of Excellence

Digital transformation

Publication

ACM Transactions on Software Engineering and Methodology

Volume

34

Issue

1

First Page

1

Last Page

22

ISSN

1049-331X

Identifier

10.1145/3688831

Publisher

Association for Computing Machinery (ACM)

Additional URL

https://doi.org/10.1145/3688831

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