We introduce DSCodeBench, a new benchmark designed to evaluate large language models (LLMs) on complicated and realistic data science code generation tasks. DSCodeBench consists of 1,000 carefully constructed problems sourced from realistic problems from GitHub across ten widely used Python data science libraries. DSCodeBench offers a more challenging and representative testbed, more complex code solutions, more comprehensive data science libraries, clearer and better structured problem descriptions, and stronger test suites. To construct the DSCodeBench, we develop a robust pipeline that combines task scope selection, code construction, test case generation, and problem description synthesis. The process is paired with rigorous manual editing to ensure alignment and enhance the reliability of the evaluation. Experimental result shows that DSCodeBench exhibits robust scaling behavior, where larger models systematically outperform smaller ones, validating its ability to distinguish model capabilities. The best LLM we test, GPT-4o, has a pass@1 of 0.392, indicating that LLMs still have a large room to improve for realistic data science code generation tasks. We believe DSCodeBench will serve as a rigorous and trustworthy foundation for advancing LLM-based data science programming.

This paper introduces DSrepair, a knowledge-enhanced program repair approach designed to repair the buggy code generated by LLMs in the data science domain. DSrepair uses knowledge graph based RAG for API knowledge retrieval and bug knowledge enrichment to construct repair prompts for LLMs. Specifically, to enable knowledge graph-based API retrieval, we construct DS-KG (Data Science Knowledge Graph) for widely used data science libraries. For bug knowledge enrichment, we employ an abstract syntax tree (AST) to localize errors at the AST node level. We evaluate DSrepair's effectiveness against five state-of-the-art LLM-based repair baselines using four advanced LLMs on the DS-1000 dataset. The results show that DSrepair outperforms all five baselines. Specifically, when compared to the second-best baseline, DSrepair achieves substantial improvements, fixing 44.4%, 14.2%, 20.6%, and 32.1% more buggy code snippets for each of the four evaluated LLMs, respectively. Additionally, it achieves greater efficiency, reducing the number of tokens required per code task by 17.49%, 34.24%, 24.71%, and 17.59%, respectively.

There has been a recent explosion of research on Large Language Models (LLMs) for software engineering tasks, in particular code generation. However, results from LLMs can be highly unstable; non-deterministically returning very different code for the same prompt. Such non-determinism affects the correctness and consistency of the generated code, undermines developers’ trust in LLMs, and yields low reproducibility in LLM-based papers. Nevertheless, there is no work investigating how serious this non-determinism threat is. To fill this gap, this article conducts an empirical study on the non-determinism of ChatGPT in code generation. We chose to study ChatGPT because it is already highly prevalent in the code generation research literature. We report results from a study of 829 code generation problems across three code generation benchmarks (i.e., CodeContests, APPS and HumanEval) with three aspects of code similarities: semantic similarity, syntactic similarity, and structural similarity. Our results reveal that ChatGPT exhibits a high degree of non-determinism under the default setting: the ratio of coding tasks with zero equal test output across different requests is 75.76%, 51.00% and 47.56% for three different code generation datasets (i.e., CodeContests, APPS and HumanEval), respectively. In addition, we find that setting the temperature to 0 does not guarantee determinism in code generation, although it indeed brings less non-determinism than the default configuration ( temperature \(=\) 1). In order to put LLM-based research on firmer scientific foundations, researchers need to take into account non-determinism in drawing their conclusions.