DS-1000: A Natural and Reliable Benchmark for Data Science Code Generation (2211.11501v1)

Abstract: We introduce DS-1000, a code generation benchmark with a thousand data science problems spanning seven Python libraries, such as NumPy and Pandas. Compared to prior works, DS-1000 incorporates three core features. First, our problems reflect diverse, realistic, and practical use cases since we collected them from StackOverflow. Second, our automatic evaluation is highly specific (reliable) -- across all Codex-002-predicted solutions that our evaluation accept, only 1.8% of them are incorrect; we achieve this with multi-criteria metrics, checking both functional correctness by running test cases and surface-form constraints by restricting API usages or keywords. Finally, we proactively defend against memorization by slightly modifying our problems to be different from the original StackOverflow source; consequently, models cannot answer them correctly by memorizing the solutions from pre-training. The current best public system (Codex-002) achieves 43.3% accuracy, leaving ample room for improvement. We release our benchmark at https://ds1000-code-gen.github.io.

• The paper introduces DS-1000, a benchmark drawn from real StackOverflow queries to evaluate data science code generation tasks realistically.
• The paper employs robust evaluation methods combining functional tests and API constraints, achieving a 1.8% false acceptance rate in Codex-002 assessments.
• The paper integrates slight modifications to prevent memorization, ensuring that models are tested on novel, perturbed problem instances.

DS-1000: A Comprehensive Benchmark for Code Generation in Data Science

The research paper presents DS-1000, a novel code generation benchmark specifically designed for data science applications. This benchmark encompasses one thousand distinct data science problems, aggregated from user queries on StackOverflow, and covers seven prevalent Python libraries, including NumPy, Pandas, TensorFlow, PyTorch, SciPy, Scikit-learn, and Matplotlib. The construction of DS-1000 aims to address the lacunae in existing benchmarks concerning realism, evaluation reliability, and susceptibility to model memorization.

Features and Methodology

DS-1000 differentiates itself through three pivotal features:

1. Realism and Diversity: The problems originate from real-world challenges encountered by users in StackOverflow discussions, thereby ensuring the practical significance and diversity of the dataset. This contrasts with previous datasets that often derive from competitive programming problems, which may not fully capture the intricacies of everyday data science coding tasks.
2. Reliable Evaluation Metrics: The evaluation methodology is rigorously designed to minimize false positives. Only 1.8% of Codex-002's solutions erroneously accepted by the evaluation scheme are incorrect. This is achieved through a multifaceted evaluation approach that combines functional correctness tests with surface-form constraints. For example, test cases verify the operational validity of solutions, while additional checks ensure compliance with specific API usage requirements, addressing inefficiencies that might arise from improper implementations.
3. Memorization Defense: A significant concern in benchmarking pre-trained code models is their potential memorization of known solutions from the training corpus. To combat this, DS-1000 incorporates slight modifications to the original StackOverflow problems prior to inclusion in the benchmark, effectively preventing the models from correctly answering purely based on memorization.

Benchmark Statistics and Model Evaluation

The benchmark provides a comprehensive suite of 1,000 problems, composed of 452 original and 548 perturbed instances, designed to prevent memorization biases. Across these problems, models are evaluated with an average of 1.6 test cases per problem, and approximately 19.4% include additional surface-form constraints.

In an empirical evaluation, several state-of-the-art code generation models are benchmarked using DS-1000. Codex-002, among tested models, achieves an accuracy of 43.3%, signifying a considerable margin for improvement in this domain. This performance spectrum across different models showcases the benchmark's ability to differentiate modeling capabilities effectively.

Implications and Future Directions

The implications of DS-1000 are manifold. Practically, it provides a robust benchmark that reflects realistic data science programming scenarios, essential for advancing the field of automatic code generation. Theoretically, it offers insight into the current capabilities and limitations of pre-trained models when applied to diverse, real-world tasks.

Looking forward, DS-1000 paves the way for future research to enhance the natural language processing models' understanding and generation of code, particularly with structured datasets not predisposed to memorization. Additionally, the benchmark challenges researchers to innovate in building models that can generalize better across different problem domains and perform robustly under AI-driven programming assistance use cases.

In conclusion, DS-1000 stands as a significant contribution to the field of automated code generation, promising to drive advancements in building models that can effectively bridge the gap between natural language problem specifications and their corresponding code implementations in practical, data science-centric contexts.