Improve Doctest Extraction Quality: A Comprehensive Guide

Doctests are a fantastic way to embed tests directly within your code's documentation, ensuring that your examples stay up-to-date and functional. However, sometimes the tools we use to extract these tests can fall short, leading to lower quality scores. This article delves into the challenges of doctest extraction and provides a detailed roadmap for improving the quality score, specifically focusing on moving from a D (67.7%) to a B+ (80% or higher).

Understanding the Problem: Why Doctest Extraction Quality Matters

The quality of doctest extraction is crucial for several reasons. High-quality extraction ensures that the generated test suite accurately reflects the intended behavior of the code. This leads to more reliable tests, better code coverage, and ultimately, more robust software. Conversely, poor extraction can result in tests that are incomplete, inaccurate, or simply fail to capture the essence of the documented examples. Poor extraction can lead to a false sense of security, where tests pass but don't truly validate the code's functionality.

Currently, the doctest extract command faces several challenges, resulting in a low-quality score of D (67.7%). Let's break down the key issues:

1. The Empty signature Column: Missing Function Signatures

One of the most significant contributors to the low score is the absence of function signatures in the extracted data. The signature column should ideally contain the function's definition, including its name, parameters, and return type (e.g., def foo(a: int, b: str) -> bool). Without this information, it becomes harder to understand the context of the test and its intended usage. This lack of context makes the tests less valuable and harder to maintain.

2. Constant Columns Flagged: Unnecessary Penalties

In single-project extraction scenarios, columns like source (the file where the doctest is located) and version (the project's version) often remain constant. While this is expected behavior, the current quality scoring system penalizes these constant columns, negatively impacting the overall score. This creates an artificial barrier to achieving a higher score and doesn't accurately reflect the quality of the extracted tests themselves.

3. High Duplicate Ratios in the expected Column: Oversimplified Tests

Doctests frequently involve simple assertions, such as checking if a function returns True, False, or a specific integer. This leads to a high number of duplicate values in the expected output column. While these simple tests are valuable, a high proportion of them can indicate a lack of diversity in the test suite. This can lead to a skewed perception of code coverage and may mask potential issues.

Proposed Improvements: A Step-by-Step Approach to B+

To address these challenges and improve the doctest extraction quality score, we propose a multi-faceted approach that tackles each issue head-on. The goal is to achieve a score of B+ (80% or higher) by implementing the following improvements:

1. Extract Function Signatures: The Key to Contextual Understanding

The most critical improvement is to extract function signatures from the doctests. This involves parsing the Abstract Syntax Tree (AST) of the code to identify function definitions and extract their signatures. The AST provides a structured representation of the code, making it possible to programmatically analyze and extract information. Parsing the AST allows us to accurately capture the function's definition, including its name, parameters, and return type.

By populating the signature column with this information, we provide valuable context for each test, making it easier to understand its purpose and usage. This, in turn, improves the overall quality and maintainability of the test suite. The following steps outline the process of extracting function signatures:

1. Parse the code: Use Python's ast module to parse the source code file into an AST.
2. Identify function definitions: Traverse the AST to find function definition nodes (ast.FunctionDef).
3. Extract signature information: For each function definition, extract the name, parameters, and return type annotations.
4. Populate the signature column: Add the extracted signature information to the corresponding doctest entry.

2. Add a Quality Profile for Doctest Corpora: Tailoring the Scoring System

The current quality scoring system applies a uniform set of thresholds to all types of corpora. However, doctest corpora have unique characteristics that require a more tailored approach. Specifically, training data for machine learning models may have different requirements than corpora used for general analytics. A one-size-fits-all approach can lead to inaccurate quality assessments and hinder the development of effective testing strategies.

To address this, we propose adding a quality profile specifically designed for doctest corpora. This profile would define different thresholds for various metrics, taking into account the specific characteristics of doctests. For example, the profile might relax the penalty for constant columns in training corpora, as these columns often provide valuable context for the model. This profile option allows us to customize the scoring system based on the intended use case of the extracted doctests.

The profile option can be implemented as a command-line flag (e.g., --profile doctest-corpus) that activates the tailored scoring system. This provides flexibility and allows users to choose the appropriate profile for their needs.

3. Populate the signature Column: Enhancing Test Clarity

As mentioned earlier, populating the signature column is crucial for improving the quality and maintainability of doctests. This involves extracting the function signature, including type hints when available. Type hints provide valuable information about the expected input and output types of the function, further enhancing the clarity and context of the test. Type hints make the tests more self-documenting and easier to understand.

By including type hints in the signature column, we can provide a more complete and informative representation of the function's definition. This makes it easier for developers to understand the intended usage of the function and to identify potential issues. The following is an example of how to populate the signature column with type hints:

• Original signature: def add(a, b)
• Signature with type hints: def add(a: int, b: int) -> int

4. Consider Removing the Constant-Column Check for ML Training Corpora: A Pragmatic Approach

For corpora used to train machine learning models, the constant-column check may be overly restrictive. Columns like source and version, while constant within a single project, can provide valuable information for the model to learn from. For example, the source column can help the model identify patterns specific to certain files or modules. Removing the constant-column check for ML training corpora can improve the model's performance and provide more relevant insights.

However, it's important to note that this change should be carefully considered and applied only to corpora specifically intended for machine learning. For other use cases, the constant-column check may still be valuable in identifying potential issues. A balanced approach is key to maximizing the benefits of this change while minimizing the risks.

Acceptance Criteria: Measuring Success

To ensure that the proposed improvements are effective, we need to define clear acceptance criteria. These criteria will serve as a benchmark for measuring the success of the implementation and ensuring that the desired quality score is achieved. The following acceptance criteria are proposed:

• Doctest extraction produces a quality score >= B (80%): This is the primary goal of the project and will serve as the key indicator of success.
• signature column populated with function signatures: This ensures that the crucial function signature information is being extracted and included in the corpus.
• Quality scoring has a profile option: --profile doctest-corpus: This provides the flexibility to tailor the scoring system to the specific characteristics of doctest corpora.

References: Learning from the Data

To gain a better understanding of the current state of doctest extraction, we analyzed a sample of 155 doctests extracted from the reprorusted-python-cli project. This analysis revealed the following key findings:

• 155 doctests extracted: This provides a good sample size for assessing the effectiveness of the improvements.
• 142 unique inputs (91.6% diversity): This indicates a relatively high level of diversity in the input examples, which is a positive sign.
• 69 unique expected outputs: This suggests that there is room for improvement in the diversity of the expected outputs.

These findings provide valuable insights into the strengths and weaknesses of the current extraction process and will help guide the implementation of the proposed improvements.

Conclusion: Paving the Way for High-Quality Doctests

Improving doctest extraction quality is essential for creating reliable and maintainable test suites. By addressing the challenges outlined in this article and implementing the proposed improvements, we can significantly enhance the quality score and unlock the full potential of doctests. A higher quality score translates to better tests, more robust code, and a more confident development process.

By extracting function signatures, tailoring the quality scoring system, and carefully considering the constant-column check, we can move from a D (67.7%) to a B+ (80% or higher) and create a more effective doctest extraction process. This will ultimately lead to better code, better tests, and a more enjoyable development experience.

For more information on testing best practices, check out resources like the Python Testing with pytest tutorial on Real Python. This external link provides a great resource for further learning about testing techniques.