ofou · December 18, 2024 18:36
diff --git a/sample.json b/sample.json
 {
    "idx": 1,
    "question": "What publicly available datasets are typically used for evaluating type inference systems in python?",
    "folder": "b76c543d4510f862",
    "key_ingredients": [
        "https://docs.google.com/document/d/1GrNP4h2LDOADgSSPJ6shTyTdkuaJk_KQksE9V-Uc7z0/edit?usp=drivesdk",
        "https://docs.google.com/document/d/1MJq20MMR3Qt5BozyrVlXlJdkbJE2jyi0iepfeyxp94Y/edit?usp=drivesdk"
    ],
    "sources": "https://docs.google.com/document/d/1sDKis_Xz08YbMnGLHbnzvOByFc3WuAhRUrpKkABaRbI/edit?usp=drivesdk",
    "src_answers": {
        "Perplexity": "### Publicly Available Datasets for Evaluating Type Inference Systems in Python\n\nSeveral publicly available datasets are commonly used for evaluating type inference systems in Python. These datasets provide a benchmark for assessing the performance of type inference tools and techniques.\n\n1. **ManyTypes4Py**:\n   - **Description**: ManyTypes4Py is a large Python dataset for machine learning-based type inference. It contains 5,382 Python projects with over 869,000 type annotations. The dataset is split into training, validation, and test sets by files to facilitate the training and evaluation of machine learning models.\n   - **Features**: The dataset includes a lightweight static analyzer pipeline to extract type information from abstract syntax trees (ASTs) and store the results in JSON-formatted files.\n\n2. **TypeEvalPy**:\n   - **Description**: TypeEvalPy is a micro-benchmarking framework for evaluating type inference tools. It contains 154 code snippets with 845 type annotations across 18 categories targeting various Python features.\n   - **Features**: The framework manages the execution of containerized tools, transforms inferred types into a standardized format, and produces meaningful metrics for assessment.\n\n3. **BigQuery Public Datasets**:\n   - **Description**: BigQuery provides a range of public datasets that can be used for various purposes, including type inference. These datasets are accessible through the Google Cloud Public Dataset Program and can be queried using SQL or GoogleSQL.\n   - **Features**: The datasets include a variety of data sources, such as weather information, GitHub repository data, and Wikipedia revision history.\n\nThese datasets are widely used in the research community to evaluate and compare the performance of different type inference tools and techniques for Python.",
        "Nora": [
            {
                "title": "Datasets for Type Inference in Python",
                "tldr": "Several publicly available datasets are commonly used for evaluating type inference systems in Python. These include specialized type inference datasets and general-purpose Python code repositories adapted for type inference tasks.",
                "text": "- Typilus Dataset: Accompanies the Typilus model and contains 600 Python projects  (1, Mir et al., 2021).\n\n- Python-150K: Published in 2016, this dataset includes 8,422 Python projects  (1, Mir et al., 2021).\n\n- ManyTypes4Py: A large-scale dataset containing over 5,200 Python repositories specifically designed for evaluating machine learning-based type inference (4, Kroi\u00df et al., 2021). It includes more than 180,000 methods from 4,000 Python projects, featuring 37,408 different variable types  (12, Wang et al., 2023).\n\n- Custom Datasets: Many researchers in the field of machine learning-based type inference for Python often create and present their own datasets tailored to their specific methods  (9, Gruner et al., 2022).\n\nThese datasets provide a range of options for researchers and developers working on type inference systems, offering diverse codebases and varying scales of data to evaluate their methods effectively.",
                "citations": [
                    {
                        "id": "(1, Mir et al., 2021)",
                        "corpus_id": 233210280,
                        "n_citations": 21,
                        "snippets": [
                            "There are several Python code corpora that can be used for machine learning-based type inference",
                            "The Typilus model [8] is accompanied by a dataset that contains 600 Python projects",
                            "Raychev et al. [16] published the Python-150K dataset in 2016, which contains 8,422 Python projects."
                        ]
                    },
                    {
                        "id": "(4, Kroi\u00df et al., 2021)",
                        "corpus_id": 243861147,
                        "n_citations": 21,
                        "snippets": [
                            "The remaining nine projects have been randomly selected from the MANYTYPES4PY dataset (Mir et al. 2021), a dataset of more than 5 200 Python repositories for evaluating machine learning-based type inference."
                        ]
                    },
                    {
                        "id": "(12, Wang et al., 2023)",
                        "corpus_id": 265509193,
                        "n_citations": 0,
                        "snippets": [
                            "ManyTypes4Py provided in the work by Mir et al. [20].The dataset includes +180k methods from 4,000 Python projects with 37,408 different variable types."
                        ]
                    },
                    {
                        "id": "(9, Gruner et al., 2022)",
                        "corpus_id": 251710434,
                        "n_citations": 2,
                        "snippets": [
                            "The authors of machine learning-based type inference methods for Python usually present their own datasets."
                        ]
                    }
                ]
            },
            {
                "title": "General-purpose Python Datasets Used for Type Inference",
                "tldr": "Several general-purpose Python datasets are commonly adapted for type inference tasks. These datasets range from large-scale code repositories to specialized collections of Python methods and files.",
                "text": "- ETH-Py150 (Python-150K): This dataset, published in 2016, contains 8,422 Python projects  (1, Mir et al., 2021). It is widely used for type inference experiments, including by Singh et al. in their research  (2, Singh et al., 2019).\n\n- CodeSearchNet-Python (CSN-Python): Originating from the CodeSearchNet corpus, this dataset consists of approximately 450,000 real-world Python methods. It has been used for various code-related tasks, including method name prediction  (3, Zhang et al., 2020).\n\n- Django and CoNaLa: These two Python datasets were used by Cao et al. in their primary experiments for type inference  (5, Cao et al., 2021).\n\n- PY150: This benchmarked dataset contains 150,000 Python program files in Abstract Syntax Tree (AST) formats. It is typically split into 100,000 files for training and 50,000 for testing  (7, Kuang et al., 2021).\n\nThese general-purpose datasets provide researchers with diverse Python codebases to evaluate and improve type inference systems, complementing the specialized datasets mentioned in the previous section.",
                "citations": [
                    {
                        "id": "(1, Mir et al., 2021)",
                        "corpus_id": 233210280,
                        "n_citations": 21,
                        "snippets": [
                            "There are several Python code corpora that can be used for machine learning-based type inference",
                            "The Typilus model [8] is accompanied by a dataset that contains 600 Python projects",
                            "Raychev et al. [16] published the Python-150K dataset in 2016, which contains 8,422 Python projects."
                        ]
                    },
                    {
                        "id": "(2, Singh et al., 2019)",
                        "corpus_id": 102481167,
                        "n_citations": 124,
                        "snippets": [
                            "We use the ETH-Py150 dataset for most of our experiments because we are targeting Python, and we use MSR-VarMisuse when comparing to the results of Allamanis et al. (2018)."
                        ]
                    },
                    {
                        "id": "(3, Zhang et al., 2020)",
                        "corpus_id": 227746086,
                        "n_citations": 45,
                        "snippets": [
                            "For method name prediction, we select CodeSearchNet-Python (CSN-Python) [58] and Java-small [6,58] datasets, and for code classification, we select Python800 and Java250 [33] datasets",
                            "CSN-Python originates from the CodeSearchNet corpus [21], consisting of around 450K real-world Python methods."
                        ]
                    },
                    {
                        "id": "(5, Cao et al., 2021)",
                        "corpus_id": 236459804,
                        "n_citations": 18,
                        "snippets": [
                            "For our primary experiments we considered two python datasets namely Django and CoNaLa."
                        ]
                    },
                    {
                        "id": "(7, Kuang et al., 2021)",
                        "corpus_id": 235474083,
                        "n_citations": 4,
                        "snippets": [
                            "The python dataset PY150 we used is a benchmarked dataset, which contains 150,000 program files in AST formats, with 100,000 used for training and the remaining 50,000 used for testing."
                        ]
                    }
                ]
            },
            {
                "title": "Code Generation Benchmarks Used for Evaluation",
                "tldr": "Several code generation benchmarks are commonly used to evaluate type inference systems in Python. These benchmarks include HumanEval, MBPP, and newer datasets like LeetcodeHardGym and DS-1000.",
                "text": "The evaluation of type inference systems in Python often extends to code generation tasks, utilizing several widely recognized benchmarks:\n\n- HumanEval: This benchmark is frequently used for assessing Python code generation capabilities  (8, Chen et al., 2022)  (10, Shinn et al., 2023)  (11, Mou et al., 2023). It has become a standard tool for evaluating the performance of code language models and type inference systems.\n\n- MBPP (Mostly Basic Python Programming): Another widely adopted benchmark for Python code writing tasks  (8, Chen et al., 2022)  (10, Shinn et al., 2023)  (11, Mou et al., 2023). MBPP is often used alongside HumanEval to provide a comprehensive evaluation of Python code generation capabilities.\n\n- GSM8K-Python: This dataset is mentioned as a widely evaluated benchmark for Python language tasks  (8, Chen et al., 2022), though it is less frequently cited in the provided references compared to HumanEval and MBPP.\n\n- LeetcodeHardGym: A newer dataset introduced for evaluating Python and Rust code writing abilities  (10, Shinn et al., 2023). This benchmark adds to the repertoire of tools available for assessing code generation and, by extension, type inference systems.\n\n- DS-1000: This benchmark is used in comprehensive evaluations of Code Language Models (LLMs)  (11, Mou et al., 2023), providing another dimension to the assessment of Python code generation and type inference capabilities.\n\nThese benchmarks offer a diverse range of challenges for evaluating type inference systems, from basic programming tasks to more complex problem-solving scenarios. By using a combination of these datasets, researchers can gain a more comprehensive understanding of their systems' performance across various coding scenarios.",
                "citations": [
                    {
                        "id": "(8, Chen et al., 2022)",
                        "corpus_id": 254070024,
                        "n_citations": 5,
                        "snippets": [
                            "For the Python language, widely evaluated datasets include Human-Eval, MBPP, and GSM8K-Python."
                        ]
                    },
                    {
                        "id": "(10, Shinn et al., 2023)",
                        "corpus_id": 258833055,
                        "n_citations": 455,
                        "snippets": [
                            "We evaluate the baseline and Reflexion approaches on Python and Rust code writing on MBPP [2], HumanEval [6], and LeetcodeHardGym, our new dataset."
                        ]
                    },
                    {
                        "id": "(11, Mou et al., 2023)",
                        "corpus_id": 258588247,
                        "n_citations": 380,
                        "snippets": [
                            "We first report performance on HumanEval (Chen et al., 2021) and MBPP (Austin et al., 2021), which are two widely used benchmarks of Python performance",
                            "We perform the most comprehensive evaluation of Code LLMs to date",
                            "on the HumanEval (Chen et al., 2021), MBPP (Austin et al., 2021), and DS-1000 (Lai et al., 2022) evaluation benchmarks."
                        ]
                    }
                ]
            }
        ],
        "GPT": "## Overview\n\nType inference systems in Python are designed to deduce variable types to facilitate tasks such as debugging, optimization, and providing auto-completion in Integrated Development Environments (IDEs). Evaluating the accuracy and performance of these systems requires robust and varied datasets. Several publicly available datasets have gained prominence for this purpose.\n\n## Popular Datasets for Evaluating Type Inference Systems in Python\n\n### 1. **PyPI Projects Dataset**\nThe Python Package Index (PyPI) is a vast repository of Python projects. Researchers often scrape and curate a collection of PyPI projects to create datasets for analysis. These datasets offer diverse coding styles and usages of Python's dynamic features, which makes them useful for evaluating type inference systems.\n\n#### Advantages:\n- **Diversity**: Contains a wide range of projects, including libraries, applications, and small utilities.\n- **Size**: Large amount of code available for comprehensive evaluation.\n\n#### Example Usage:\nCheung et al.'s work on \"Static Typing and Python: MyPy, Gradual Typing, and the Future of Typing in Python\" [1] involves using a subset of PyPI projects to evaluate type inference and gradual typing.\n\n### 2. **Type4Py Dataset**\nType4Py is a dataset created primarily for type prediction in Python. It consists of type-annotated Python code sourced from GitHub. The dataset includes function signatures, variable annotations, and type usage patterns.\n\n#### Advantages:\n- **Type Annotations**: Provides already annotated samples which are beneficial for assessment.\n- **Standardized**: Designed with type inference validation in mind.\n\n#### Example Usage:\nThe authors of Type4Py utilized this dataset to train and evaluate their machine learning model for type prediction in Python [2].\n\n### 3. **The Py150 Dataset**\nThis dataset includes 150 thousand Python files sourced from GitHub repositories. It is specifically curated for the purpose of evaluating machine learning models on source code tasks, including type inference.\n\n#### Advantages:\n- **Well Organized**: Curated with a focus on creating a benchmark dataset.\n- **Metadata**: Provides additional information like repository names, commit history, and file statistics.\n\n#### Example Usage:\nRaychev et al. used the Py150 dataset for evaluating DeepTyper, a deep learning-based system for type inference [3].\n\n### 4. **The ETH Py150 Open Dataset**\nETH Py150 is another extensive dataset derived from GitHub. It includes Python scripts from various projects, with metadata and parsed Abstract Syntax Trees (ASTs).\n\n#### Advantages:\n- **Detailed Information**: Includes ASTs and additional metadata for more nuanced inference.\n- **Size and Variety**: Large number of files from different domains.\n\n#### Example Usage:\nThe dataset has been extensively used to benchmark different type inference methods and machine learning approaches, including work by Hellendoorn et al. in their research on source code models [4].\n\n### 5. **Corpus of Python Programs (CoPP)**\nThe CoPP dataset compiles Python programs from diverse sources such as educational institutions, open-source repositories, and competitive coding platforms. It's designed to reflect a wide array of coding styles and problem domains.\n\n#### Advantages:\n- **Educational Use**: Includes beginner to advanced level codes, catering to different competencies.\n- **Variety**: Drawn from multiple sources ensuring a broad spectrum of coding paradigms.\n\n#### Example Usage:\nCoPP is used in type inference experiments to provide a holistic evaluation across different levels of coding sophistication, as noted in several educational and research studies [5].\n\n## Key Aspects to Consider During Evaluation\n\n### **Accuracy**\nThe correctness of the inferred types compared to the ground truth.\n\n### **Performance**\nThe computation resources and time required to run the type inference.\n\n### **Coverage**\nThe range and variety of code constructs and libraries handled by the inference system.\n\n## Conclusion\n\nPublicly available datasets such as those compiled from PyPI, Type4Py, and Py150 provide rich resources for evaluating type inference systems in Python. Each dataset has its own strengths and usage contexts, making them valuable for different facets of type inference evaluation.\n\n## References\n\n1. Cheung, A. et al. (2020). \"Static Typing and Python: MyPy, Gradual Typing, and the Future of Typing in Python.\"\n2. Type4Py Dataset: https://type4py.com\n3. Raychev, V. et al. (2016). \"DeepTyper: Predicting Type Annotations with Deep Learning.\"\n4. Hellendoorn, V. J., et al. (2018), \"Deep Learning for Source Code Modeling: A Comprehensive Survey.\"\n5. Corpus of Python Programs (CoPP). https://github.com/CoPP-Datasets\n\nThese references help ensure that type inference systems evaluated have a robust foundation and are up to real-world coding standards and complexities.",
        "Claude": "To answer this query about publicly available datasets for evaluating type inference systems in Python, I'll provide a comprehensive overview divided into relevant sections. \n\n1. Introduction to Type Inference Evaluation Datasets\n\nType inference is a crucial aspect of programming language analysis, particularly for dynamically typed languages like Python. Evaluating type inference systems requires diverse and representative datasets. While there isn't a single standardized dataset universally used across all type inference research, several datasets have gained prominence in the field.\n\n2. Popular Datasets for Python Type Inference Evaluation\n\n2.1 Python Corpus from TypeScript\n\nOne of the most widely used datasets is derived from the TypeScript type inference project. This dataset includes a large corpus of Python code extracted from popular open-source projects.\n\nCitation: [1] Allamanis, M., et al. (2020). A Type-Based Dataset for Python. In Proceedings of the ACM on Programming Languages, 4(POPL), 1-29.\n\n2.2 ManyTypes4Py\n\nManyTypes4Py is a large-scale benchmark dataset specifically designed for machine learning-based type inference in Python.\n\nCitation: [2] Mir, A. M., et al. (2021). A Large-Scale Dataset for Type Inference in Python. In Proceedings of the 18th International Conference on Mining Software Repositories (MSR '21), 399\u2013410.\n\n2.3 PyTyper Dataset\n\nThe PyTyper dataset is another significant collection used for evaluating type inference systems, containing a diverse set of Python projects with type annotations.\n\nCitation: [3] Hassan, M., et al. (2018). MaxSMT-Based Type Inference for Python 3. In International Conference on Computer Aided Verification, 12\u201319.\n\n3. GitHub-based Datasets\n\nMany researchers create custom datasets by mining GitHub repositories. These datasets often include popular, well-maintained Python projects with type annotations.\n\n3.1 Typeshed\n\nWhile not strictly a dataset, Typeshed is a collection of type stubs for Python's standard library and third-party packages. It's often used as a reference or supplement in type inference evaluations.\n\nCitation: [4] The Typeshed Project. (n.d.). GitHub repository. https://github.com/python/typeshed\n\n4. Synthetic Datasets\n\nSome researchers create synthetic datasets to test specific aspects of type inference systems. These datasets are generated to cover edge cases or specific language features that might be underrepresented in real-world code.\n\n5. Benchmarks from Type Checking Tools\n\nDatasets and benchmarks from popular type checking tools like mypy, pytype, and pyre are sometimes used for evaluating type inference systems.\n\nCitation: [5] The mypy Project. (n.d.). GitHub repository. https://github.com/python/mypy\n\n6. Considerations for Dataset Selection\n\nWhen evaluating type inference systems, researchers often consider the following factors in dataset selection:\n\n- Size and diversity of the codebase\n- Presence of type annotations (for ground truth)\n- Coverage of Python language features\n- Representation of different programming paradigms and styles\n- Inclusion of both standard library and third-party package usage\n\n7. Conclusion\n\nWhile there isn't a single, universally accepted dataset for evaluating Python type inference systems, researchers typically use a combination of the aforementioned datasets. The choice often depends on the specific aspects of type inference being studied and the evaluation metrics being used. It's common for researchers to use multiple datasets or create custom datasets to ensure comprehensive evaluation of their type inference systems."
    }
 }
	{
	"idx": 1,
	"question": "What publicly available datasets are typically used for evaluating type inference systems in python?",
	"folder": "b76c543d4510f862",
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	"Perplexity": "### Publicly Available Datasets for Evaluating Type Inference Systems in Python\n\nSeveral publicly available datasets are commonly used for evaluating type inference systems in Python. These datasets provide a benchmark for assessing the performance of type inference tools and techniques.\n\n1. ManyTypes4Py:\n - Description: ManyTypes4Py is a large Python dataset for machine learning-based type inference. It contains 5,382 Python projects with over 869,000 type annotations. The dataset is split into training, validation, and test sets by files to facilitate the training and evaluation of machine learning models.\n - Features: The dataset includes a lightweight static analyzer pipeline to extract type information from abstract syntax trees (ASTs) and store the results in JSON-formatted files.\n\n2. TypeEvalPy:\n - Description: TypeEvalPy is a micro-benchmarking framework for evaluating type inference tools. It contains 154 code snippets with 845 type annotations across 18 categories targeting various Python features.\n - Features: The framework manages the execution of containerized tools, transforms inferred types into a standardized format, and produces meaningful metrics for assessment.\n\n3. BigQuery Public Datasets:\n - Description: BigQuery provides a range of public datasets that can be used for various purposes, including type inference. These datasets are accessible through the Google Cloud Public Dataset Program and can be queried using SQL or GoogleSQL.\n - Features: The datasets include a variety of data sources, such as weather information, GitHub repository data, and Wikipedia revision history.\n\nThese datasets are widely used in the research community to evaluate and compare the performance of different type inference tools and techniques for Python.",
	"Nora": [
	{
	"title": "Datasets for Type Inference in Python",
	"tldr": "Several publicly available datasets are commonly used for evaluating type inference systems in Python. These include specialized type inference datasets and general-purpose Python code repositories adapted for type inference tasks.",
	"text": "- Typilus Dataset: Accompanies the Typilus model and contains 600 Python projects (1, Mir et al., 2021).\n\n- Python-150K: Published in 2016, this dataset includes 8,422 Python projects (1, Mir et al., 2021).\n\n- ManyTypes4Py: A large-scale dataset containing over 5,200 Python repositories specifically designed for evaluating machine learning-based type inference (4, Kroi\u00df et al., 2021). It includes more than 180,000 methods from 4,000 Python projects, featuring 37,408 different variable types (12, Wang et al., 2023).\n\n- Custom Datasets: Many researchers in the field of machine learning-based type inference for Python often create and present their own datasets tailored to their specific methods (9, Gruner et al., 2022).\n\nThese datasets provide a range of options for researchers and developers working on type inference systems, offering diverse codebases and varying scales of data to evaluate their methods effectively.",
	"citations": [
	{
	"id": "(1, Mir et al., 2021)",
	"corpus_id": 233210280,
	"n_citations": 21,
	"snippets": [
	"There are several Python code corpora that can be used for machine learning-based type inference",
	"The Typilus model [8] is accompanied by a dataset that contains 600 Python projects",
	"Raychev et al. [16] published the Python-150K dataset in 2016, which contains 8,422 Python projects."
	]
	},
	{
	"id": "(4, Kroi\u00df et al., 2021)",
	"corpus_id": 243861147,
	"n_citations": 21,
	"snippets": [
	"The remaining nine projects have been randomly selected from the MANYTYPES4PY dataset (Mir et al. 2021), a dataset of more than 5 200 Python repositories for evaluating machine learning-based type inference."
	]
	},
	{
	"id": "(12, Wang et al., 2023)",
	"corpus_id": 265509193,
	"n_citations": 0,
	"snippets": [
	"ManyTypes4Py provided in the work by Mir et al. [20].The dataset includes +180k methods from 4,000 Python projects with 37,408 different variable types."
	]
	},
	{
	"id": "(9, Gruner et al., 2022)",
	"corpus_id": 251710434,
	"n_citations": 2,
	"snippets": [
	"The authors of machine learning-based type inference methods for Python usually present their own datasets."
	]
	}
	]
	},
	{
	"title": "General-purpose Python Datasets Used for Type Inference",
	"tldr": "Several general-purpose Python datasets are commonly adapted for type inference tasks. These datasets range from large-scale code repositories to specialized collections of Python methods and files.",
	"text": "- ETH-Py150 (Python-150K): This dataset, published in 2016, contains 8,422 Python projects (1, Mir et al., 2021). It is widely used for type inference experiments, including by Singh et al. in their research (2, Singh et al., 2019).\n\n- CodeSearchNet-Python (CSN-Python): Originating from the CodeSearchNet corpus, this dataset consists of approximately 450,000 real-world Python methods. It has been used for various code-related tasks, including method name prediction (3, Zhang et al., 2020).\n\n- Django and CoNaLa: These two Python datasets were used by Cao et al. in their primary experiments for type inference (5, Cao et al., 2021).\n\n- PY150: This benchmarked dataset contains 150,000 Python program files in Abstract Syntax Tree (AST) formats. It is typically split into 100,000 files for training and 50,000 for testing (7, Kuang et al., 2021).\n\nThese general-purpose datasets provide researchers with diverse Python codebases to evaluate and improve type inference systems, complementing the specialized datasets mentioned in the previous section.",
	"citations": [
	{
	"id": "(1, Mir et al., 2021)",
	"corpus_id": 233210280,
	"n_citations": 21,
	"snippets": [
	"There are several Python code corpora that can be used for machine learning-based type inference",
	"The Typilus model [8] is accompanied by a dataset that contains 600 Python projects",
	"Raychev et al. [16] published the Python-150K dataset in 2016, which contains 8,422 Python projects."
	]
	},
	{
	"id": "(2, Singh et al., 2019)",
	"corpus_id": 102481167,
	"n_citations": 124,
	"snippets": [
	"We use the ETH-Py150 dataset for most of our experiments because we are targeting Python, and we use MSR-VarMisuse when comparing to the results of Allamanis et al. (2018)."
	]
	},
	{
	"id": "(3, Zhang et al., 2020)",
	"corpus_id": 227746086,
	"n_citations": 45,
	"snippets": [
	"For method name prediction, we select CodeSearchNet-Python (CSN-Python) [58] and Java-small [6,58] datasets, and for code classification, we select Python800 and Java250 [33] datasets",
	"CSN-Python originates from the CodeSearchNet corpus [21], consisting of around 450K real-world Python methods."
	]
	},
	{
	"id": "(5, Cao et al., 2021)",
	"corpus_id": 236459804,
	"n_citations": 18,
	"snippets": [
	"For our primary experiments we considered two python datasets namely Django and CoNaLa."
	]
	},
	{
	"id": "(7, Kuang et al., 2021)",
	"corpus_id": 235474083,
	"n_citations": 4,
	"snippets": [
	"The python dataset PY150 we used is a benchmarked dataset, which contains 150,000 program files in AST formats, with 100,000 used for training and the remaining 50,000 used for testing."
	]
	}
	]
	},
	{
	"title": "Code Generation Benchmarks Used for Evaluation",
	"tldr": "Several code generation benchmarks are commonly used to evaluate type inference systems in Python. These benchmarks include HumanEval, MBPP, and newer datasets like LeetcodeHardGym and DS-1000.",
	"text": "The evaluation of type inference systems in Python often extends to code generation tasks, utilizing several widely recognized benchmarks:\n\n- HumanEval: This benchmark is frequently used for assessing Python code generation capabilities (8, Chen et al., 2022) (10, Shinn et al., 2023) (11, Mou et al., 2023). It has become a standard tool for evaluating the performance of code language models and type inference systems.\n\n- MBPP (Mostly Basic Python Programming): Another widely adopted benchmark for Python code writing tasks (8, Chen et al., 2022) (10, Shinn et al., 2023) (11, Mou et al., 2023). MBPP is often used alongside HumanEval to provide a comprehensive evaluation of Python code generation capabilities.\n\n- GSM8K-Python: This dataset is mentioned as a widely evaluated benchmark for Python language tasks (8, Chen et al., 2022), though it is less frequently cited in the provided references compared to HumanEval and MBPP.\n\n- LeetcodeHardGym: A newer dataset introduced for evaluating Python and Rust code writing abilities (10, Shinn et al., 2023). This benchmark adds to the repertoire of tools available for assessing code generation and, by extension, type inference systems.\n\n- DS-1000: This benchmark is used in comprehensive evaluations of Code Language Models (LLMs) (11, Mou et al., 2023), providing another dimension to the assessment of Python code generation and type inference capabilities.\n\nThese benchmarks offer a diverse range of challenges for evaluating type inference systems, from basic programming tasks to more complex problem-solving scenarios. By using a combination of these datasets, researchers can gain a more comprehensive understanding of their systems' performance across various coding scenarios.",
	"citations": [
	{
	"id": "(8, Chen et al., 2022)",
	"corpus_id": 254070024,
	"n_citations": 5,
	"snippets": [
	"For the Python language, widely evaluated datasets include Human-Eval, MBPP, and GSM8K-Python."
	]
	},
	{
	"id": "(10, Shinn et al., 2023)",
	"corpus_id": 258833055,
	"n_citations": 455,
	"snippets": [
	"We evaluate the baseline and Reflexion approaches on Python and Rust code writing on MBPP [2], HumanEval [6], and LeetcodeHardGym, our new dataset."
	]
	},
	{
	"id": "(11, Mou et al., 2023)",
	"corpus_id": 258588247,
	"n_citations": 380,
	"snippets": [
	"We first report performance on HumanEval (Chen et al., 2021) and MBPP (Austin et al., 2021), which are two widely used benchmarks of Python performance",
	"We perform the most comprehensive evaluation of Code LLMs to date",
	"on the HumanEval (Chen et al., 2021), MBPP (Austin et al., 2021), and DS-1000 (Lai et al., 2022) evaluation benchmarks."
	]
	}
	]
	}
	],
	"GPT": "## Overview\n\nType inference systems in Python are designed to deduce variable types to facilitate tasks such as debugging, optimization, and providing auto-completion in Integrated Development Environments (IDEs). Evaluating the accuracy and performance of these systems requires robust and varied datasets. Several publicly available datasets have gained prominence for this purpose.\n\n## Popular Datasets for Evaluating Type Inference Systems in Python\n\n### 1. PyPI Projects Dataset\nThe Python Package Index (PyPI) is a vast repository of Python projects. Researchers often scrape and curate a collection of PyPI projects to create datasets for analysis. These datasets offer diverse coding styles and usages of Python's dynamic features, which makes them useful for evaluating type inference systems.\n\n#### Advantages:\n- Diversity: Contains a wide range of projects, including libraries, applications, and small utilities.\n- Size: Large amount of code available for comprehensive evaluation.\n\n#### Example Usage:\nCheung et al.'s work on \"Static Typing and Python: MyPy, Gradual Typing, and the Future of Typing in Python\" [1] involves using a subset of PyPI projects to evaluate type inference and gradual typing.\n\n### 2. Type4Py Dataset\nType4Py is a dataset created primarily for type prediction in Python. It consists of type-annotated Python code sourced from GitHub. The dataset includes function signatures, variable annotations, and type usage patterns.\n\n#### Advantages:\n- Type Annotations: Provides already annotated samples which are beneficial for assessment.\n- Standardized: Designed with type inference validation in mind.\n\n#### Example Usage:\nThe authors of Type4Py utilized this dataset to train and evaluate their machine learning model for type prediction in Python [2].\n\n### 3. The Py150 Dataset\nThis dataset includes 150 thousand Python files sourced from GitHub repositories. It is specifically curated for the purpose of evaluating machine learning models on source code tasks, including type inference.\n\n#### Advantages:\n- Well Organized: Curated with a focus on creating a benchmark dataset.\n- Metadata: Provides additional information like repository names, commit history, and file statistics.\n\n#### Example Usage:\nRaychev et al. used the Py150 dataset for evaluating DeepTyper, a deep learning-based system for type inference [3].\n\n### 4. The ETH Py150 Open Dataset\nETH Py150 is another extensive dataset derived from GitHub. It includes Python scripts from various projects, with metadata and parsed Abstract Syntax Trees (ASTs).\n\n#### Advantages:\n- Detailed Information: Includes ASTs and additional metadata for more nuanced inference.\n- Size and Variety: Large number of files from different domains.\n\n#### Example Usage:\nThe dataset has been extensively used to benchmark different type inference methods and machine learning approaches, including work by Hellendoorn et al. in their research on source code models [4].\n\n### 5. Corpus of Python Programs (CoPP)\nThe CoPP dataset compiles Python programs from diverse sources such as educational institutions, open-source repositories, and competitive coding platforms. It's designed to reflect a wide array of coding styles and problem domains.\n\n#### Advantages:\n- Educational Use: Includes beginner to advanced level codes, catering to different competencies.\n- Variety: Drawn from multiple sources ensuring a broad spectrum of coding paradigms.\n\n#### Example Usage:\nCoPP is used in type inference experiments to provide a holistic evaluation across different levels of coding sophistication, as noted in several educational and research studies [5].\n\n## Key Aspects to Consider During Evaluation\n\n### Accuracy\nThe correctness of the inferred types compared to the ground truth.\n\n### Performance\nThe computation resources and time required to run the type inference.\n\n### Coverage\nThe range and variety of code constructs and libraries handled by the inference system.\n\n## Conclusion\n\nPublicly available datasets such as those compiled from PyPI, Type4Py, and Py150 provide rich resources for evaluating type inference systems in Python. Each dataset has its own strengths and usage contexts, making them valuable for different facets of type inference evaluation.\n\n## References\n\n1. Cheung, A. et al. (2020). \"Static Typing and Python: MyPy, Gradual Typing, and the Future of Typing in Python.\"\n2. Type4Py Dataset: https://type4py.com\n3. Raychev, V. et al. (2016). \"DeepTyper: Predicting Type Annotations with Deep Learning.\"\n4. Hellendoorn, V. J., et al. (2018), \"Deep Learning for Source Code Modeling: A Comprehensive Survey.\"\n5. Corpus of Python Programs (CoPP). https://github.com/CoPP-Datasets\n\nThese references help ensure that type inference systems evaluated have a robust foundation and are up to real-world coding standards and complexities.",
	"Claude": "To answer this query about publicly available datasets for evaluating type inference systems in Python, I'll provide a comprehensive overview divided into relevant sections. \n\n1. Introduction to Type Inference Evaluation Datasets\n\nType inference is a crucial aspect of programming language analysis, particularly for dynamically typed languages like Python. Evaluating type inference systems requires diverse and representative datasets. While there isn't a single standardized dataset universally used across all type inference research, several datasets have gained prominence in the field.\n\n2. Popular Datasets for Python Type Inference Evaluation\n\n2.1 Python Corpus from TypeScript\n\nOne of the most widely used datasets is derived from the TypeScript type inference project. This dataset includes a large corpus of Python code extracted from popular open-source projects.\n\nCitation: [1] Allamanis, M., et al. (2020). A Type-Based Dataset for Python. In Proceedings of the ACM on Programming Languages, 4(POPL), 1-29.\n\n2.2 ManyTypes4Py\n\nManyTypes4Py is a large-scale benchmark dataset specifically designed for machine learning-based type inference in Python.\n\nCitation: [2] Mir, A. M., et al. (2021). A Large-Scale Dataset for Type Inference in Python. In Proceedings of the 18th International Conference on Mining Software Repositories (MSR '21), 399\u2013410.\n\n2.3 PyTyper Dataset\n\nThe PyTyper dataset is another significant collection used for evaluating type inference systems, containing a diverse set of Python projects with type annotations.\n\nCitation: [3] Hassan, M., et al. (2018). MaxSMT-Based Type Inference for Python 3. In International Conference on Computer Aided Verification, 12\u201319.\n\n3. GitHub-based Datasets\n\nMany researchers create custom datasets by mining GitHub repositories. These datasets often include popular, well-maintained Python projects with type annotations.\n\n3.1 Typeshed\n\nWhile not strictly a dataset, Typeshed is a collection of type stubs for Python's standard library and third-party packages. It's often used as a reference or supplement in type inference evaluations.\n\nCitation: [4] The Typeshed Project. (n.d.). GitHub repository. https://github.com/python/typeshed\n\n4. Synthetic Datasets\n\nSome researchers create synthetic datasets to test specific aspects of type inference systems. These datasets are generated to cover edge cases or specific language features that might be underrepresented in real-world code.\n\n5. Benchmarks from Type Checking Tools\n\nDatasets and benchmarks from popular type checking tools like mypy, pytype, and pyre are sometimes used for evaluating type inference systems.\n\nCitation: [5] The mypy Project. (n.d.). GitHub repository. https://github.com/python/mypy\n\n6. Considerations for Dataset Selection\n\nWhen evaluating type inference systems, researchers often consider the following factors in dataset selection:\n\n- Size and diversity of the codebase\n- Presence of type annotations (for ground truth)\n- Coverage of Python language features\n- Representation of different programming paradigms and styles\n- Inclusion of both standard library and third-party package usage\n\n7. Conclusion\n\nWhile there isn't a single, universally accepted dataset for evaluating Python type inference systems, researchers typically use a combination of the aforementioned datasets. The choice often depends on the specific aspects of type inference being studied and the evaluation metrics being used. It's common for researchers to use multiple datasets or create custom datasets to ensure comprehensive evaluation of their type inference systems."
	}
	}