# The Good, the Bad, and the Missing: Neural Code Generation for Machine Learning Tasks

arXiv: https://arxiv.org/abs/2305.09082 · HTML: https://mlsanity.com/paper/2305.09082/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 May 2023.

## Built on by (most cited first, 6 of 6)

- [On the Use of Agentic Coding: An Empirical Study of Pull Requests on GitHub](https://mlsanity.com/paper/2509.14745/) (2025)
- [Retrieval-Augmented Test Generation: How Far Are We?](https://mlsanity.com/paper/2409.12682/) (2024)
- [Benchmarking AI Models in Software Engineering: A Review, Search Tool, and Unified Approach for Elevating Benchmark Quality](https://mlsanity.com/paper/2503.05860/) (2025)
- [Deep-Bench: Deep Learning Benchmark Dataset for Code Generation](https://mlsanity.com/paper/2502.18726/) (2025)
- [CodePurify: Defend Backdoor Attacks on Neural Code Models via Entropy-based Purification](https://mlsanity.com/paper/2410.20136/) (2024)
- [MCGMark: An Encodable and Robust Online Watermark for Tracing LLM-Generated Malicious Code](https://mlsanity.com/paper/2408.01354/) (2024)

## Abstract (arXiv)

Machine learning (ML) has been increasingly used in a variety of domains, while solving ML programming tasks poses unique challenges because of the fundamentally different nature and construction from general programming tasks, especially for developers who do not have ML backgrounds. Automatic code generation that produces a code snippet from a natural language description can be a promising technique to accelerate ML programming tasks. In recent years, although many deep learning-based neural code generation models have been proposed with high accuracy, the fact that most of them are mainly evaluated on general programming tasks calls into question their effectiveness and usefulness in ML programming tasks. In this paper, we set out to investigate the effectiveness of existing neural code generation models on ML programming tasks. For our analysis, we select six state-of-the-art neural code generation models, and evaluate their performance on four widely used ML libraries, with newly-created 83K pairs of natural-language described ML programming tasks. Our empirical study reveals some good, bad, and missing aspects of neural code generation models on ML tasks, with a few major ones listed below. (Good) Neural code generation models perform significantly better on ML tasks than on non-ML tasks. (Bad) Most of the generated code is semantically incorrect. (Bad) Code generation models cannot significantly improve developers' completion time. (Good) The generated code can help developers write more correct code by providing developers with clues for using correct APIs. (Missing) The observation from our user study reveals the missing aspects of code generation for ML tasks, e.g., decomposing code generation for divide-and-conquer into two tasks: API sequence identification and API usage generation.