Defining ethically sourced code generation
This work addresses ethical concerns in code generation for AI developers and practitioners, but it is incremental as it builds on existing ethical frameworks in other domains like speech and image generation.
The paper tackles the problem of ethical issues in code generation models by introducing the concept of Ethically Sourced Code Generation (ES-CodeGen) and developing a taxonomy through a literature review of 803 papers and a survey of 32 practitioners, resulting in 11 dimensions including a new one on code quality.
Several code generation models have been proposed to help reduce time and effort in solving software-related tasks. To ensure responsible AI, there are growing interests over various ethical issues (e.g., unclear licensing, privacy, fairness, and environment impact). These studies have the overarching goal of ensuring ethically sourced generation, which has gained growing attentions in speech synthesis and image generation. In this paper, we introduce the novel notion of Ethically Sourced Code Generation (ES-CodeGen) to refer to managing all processes involved in code generation model development from data collection to post-deployment via ethical and sustainable practices. To build a taxonomy of ES-CodeGen, we perform a two-phase literature review where we read 803 papers across various domains and specific to AI-based code generation. We identified 71 relevant papers with 10 initial dimensions of ES-CodeGen. To refine our dimensions and gain insights on consequences of ES-CodeGen, we surveyed 32 practitioners, which include six developers who submitted GitHub issues to opt-out from the Stack dataset (these impacted users have real-world experience of ethically sourcing issues in code generation models). The results lead to 11 dimensions of ES-CodeGen with a new dimension on code quality as practitioners have noted its importance. We also identified consequences, artifacts, and stages relevant to ES-CodeGen. Our post-survey reflection showed that most practitioners tend to ignore social-related dimensions despite their importance. Most practitioners either agreed or strongly agreed that our survey help improve their understanding of ES-CodeGen. Our study calls for attentions of various ethical issues towards ES-CodeGen.