Toxic Exposure Purge Follow-up: Data Integrity Check

Ensuring the accuracy and integrity of veterans' data is paramount, especially when dealing with features like the Toxic Exposure purge onSubmit. This article delves into the follow-up actions taken after an initial release revealed unexpected issues, highlighting the importance of meticulous code review, comprehensive logging, and rigorous testing in safeguarding sensitive information.

Understanding the User Need

At the heart of any software development lies a deep understanding of user needs. In this case, the primary user is the engineer tasked with implementing and maintaining the Toxic Exposure purge onSubmit feature. The core need is to guarantee that the feature operates without inadvertently altering the shape of the data, thus preserving the integrity of veterans' information and ensuring accurate submissions. This requirement stems from the critical nature of veteran data and the potential consequences of errors or corruption.

When developing features that handle sensitive data, such as veterans' information, it's crucial to prioritize data integrity above all else. Any modifications to the data structure, even seemingly minor ones, can have cascading effects on downstream processes and systems. For instance, changes in data format or field types can lead to compatibility issues, data loss, or incorrect calculations. Therefore, engineers must exercise extreme caution and implement robust validation mechanisms to prevent unintended data transformations.

Furthermore, maintaining data integrity is not just a technical concern; it's also an ethical imperative. Veterans entrust their personal information to the Department of Veterans Affairs (VA), and it's the responsibility of the VA to protect that trust by ensuring the data is handled with the utmost care and accuracy. Any breach of data integrity can erode trust and undermine the VA's mission to serve veterans effectively. To address this user need, it's essential to adopt a proactive approach that encompasses thorough code reviews, comprehensive testing, and detailed logging. By carefully examining the code for potential data shape alterations, conducting rigorous tests to identify any unexpected behavior, and implementing detailed logging to track data flow and transformations, engineers can significantly reduce the risk of data integrity issues. This proactive approach not only safeguards veterans' data but also enhances the reliability and trustworthiness of the entire system.

The Initial Release and Subsequent Rollback

The initial 25% release of the Toxic Exposure purge onSubmit feature served as a crucial learning experience. The unexpected outcome – a higher-than-anticipated number of records with the "total removed" flag set to true – prompted immediate action. This triggered a rollback of the release, demonstrating the importance of having robust monitoring and rollback mechanisms in place. This proactive step prevented further potential data discrepancies and allowed the team to investigate the root cause of the issue. The decision to roll back the release underscores the commitment to data integrity and the willingness to prioritize accuracy over speed.

The higher-than-expected number of records with the "total removed" flag raised concerns about the feature's behavior and its impact on veteran data. This unexpected result highlighted the need for a more granular understanding of the user journey and the reasons why the purge monitors were being triggered. The rollback provided a necessary pause to allow for a thorough investigation and prevent any further unintended consequences.

The recommendation in https://github.com/department-of-veterans-affairs/va.gov-team/issues/118990#issuecomment-3563777131 emphasized the need to review the code meticulously. The primary goal was to ensure that the feature wasn't inadvertently altering the data structure during the submission process. This step was crucial in identifying and rectifying any potential data integrity issues. In addition to code review, the team also recognized the importance of enhancing logging capabilities. By incorporating more granular details into the logs, the team aimed to gain a clearer picture of the user journey and the specific triggers for the purge monitors. This enhanced visibility would facilitate faster troubleshooting and prevent similar issues in the future. The rollback experience served as a valuable reminder of the importance of continuous monitoring, proactive intervention, and a commitment to data quality in software development. It also highlighted the need for a collaborative approach, involving engineers, product managers, and designers, to ensure the successful implementation of critical features.

Key Tasks and Objectives

The primary objective is to thoroughly review the code to identify and rectify any instances where the data shape might be unnecessarily altered during the onSubmit process. This involves a detailed examination of the codebase, paying close attention to data transformations, validations, and persistence mechanisms. The goal is to ensure that the feature operates as intended, without compromising the integrity of the data.

Code Review and Data Shape Preservation

A meticulous code review is crucial for identifying potential issues related to data shape alterations. This process involves examining the code line by line, looking for instances where data structures might be modified, fields might be renamed, or data types might be changed. The review should also focus on data validation logic to ensure that it's correctly handling different data scenarios and preventing invalid data from being persisted.

Enhanced Logging for Granular Insights

To gain a deeper understanding of the user journey and the reasons behind purge monitor triggers, the team is implementing more granular logging. This involves adding detailed log messages at various points in the code, capturing relevant information such as user actions, data inputs, and system responses. The enhanced logging will provide valuable insights into the feature's behavior and help identify the root cause of any issues.

User Journey Analysis

Analyzing the user journey is essential for understanding how users interact with the feature and identifying potential pain points or areas for improvement. By tracking user actions and system responses, the team can gain valuable insights into the user experience and identify opportunities to optimize the feature's design and functionality. This analysis will also help in identifying patterns and trends that might indicate underlying issues or areas of concern.

Purge Monitor Trigger Analysis

Understanding why the purge monitors are being triggered is critical for ensuring the feature's effectiveness and preventing unintended data purges. By analyzing the log data and user journey information, the team can identify the specific conditions that lead to purge monitor triggers. This analysis will help in refining the purge logic and ensuring that it's accurately identifying and removing toxic data without affecting legitimate information.

By focusing on these key tasks and objectives, the team aims to improve the Toxic Exposure purge onSubmit feature, ensuring that it operates reliably and preserves the integrity of veteran data. The comprehensive approach, encompassing code review, enhanced logging, user journey analysis, and purge monitor trigger analysis, will provide a solid foundation for future development and maintenance efforts.

Acceptance Criteria and Definition of Done

The success of this follow-up effort hinges on clearly defined acceptance criteria and a comprehensive definition of done. While the original document lacks specific acceptance criteria, the overarching goal is to ensure that the Toxic Exposure purge onSubmit feature functions as intended, without compromising the integrity of veterans' data. This implies that the feature should accurately identify and remove toxic data while preserving legitimate information and avoiding unintended data modifications.

To meet this goal, several key acceptance criteria should be considered. First and foremost, the feature must not alter the data shape unnecessarily. This means that the data structure, field names, and data types should remain consistent throughout the process, from input to storage. Any data transformations should be intentional and well-documented, with clear justifications for the changes. Second, the feature should accurately identify and remove toxic data based on predefined criteria. This requires a thorough understanding of the criteria and the ability to translate them into effective code logic. The feature should also be able to handle edge cases and exceptions gracefully, without causing errors or data loss. Third, the enhanced logging should provide sufficient detail to track user journeys and understand the reasons for purge monitor triggers. This logging should include relevant information such as user actions, data inputs, system responses, and error messages. The logs should be easily accessible and searchable, allowing for efficient troubleshooting and analysis.

The definition of done encompasses a broader set of requirements that must be met before the feature can be considered complete. In addition to meeting the acceptance criteria, the definition of done includes code review and approval by product and/or design stakeholders. This ensures that the feature aligns with the overall product vision and user experience goals. From an engineering perspective, the definition of done includes passing all tests, including unit tests that cover new functionality. This ensures that the code is robust and reliable. It also includes implementing logging and monitoring, which are crucial for ongoing maintenance and troubleshooting. The engineering definition of done also includes conducting performance testing to ensure that the feature can handle the expected load without performance degradation.

Engineering Considerations: Testing, Logging, and Monitoring

From an engineering standpoint, several critical aspects contribute to the success of the Toxic Exposure purge onSubmit feature follow-up. These include comprehensive testing, robust logging and monitoring, and adherence to coding best practices. All tests must pass, ensuring that the feature functions as expected under various conditions. New functionality should be covered by unit tests, providing a safety net against regressions and unintended side effects. Logging and monitoring are essential for tracking the feature's performance, identifying potential issues, and understanding user behavior. Effective logging should capture relevant information about user actions, data inputs, system responses, and error messages.

Test-Driven Development (TDD)

Employing a test-driven development (TDD) approach can significantly improve the quality and reliability of the code. TDD involves writing tests before writing the actual code, forcing developers to think about the desired behavior and edge cases upfront. This approach leads to more robust and well-tested code, reducing the risk of bugs and errors.

Unit Testing

Unit tests are crucial for verifying the correctness of individual components and functions within the feature. These tests should cover a wide range of scenarios, including normal cases, edge cases, and error conditions. Well-written unit tests provide confidence in the code's functionality and make it easier to refactor and maintain.

Logging and Monitoring

Logging and monitoring are essential for gaining insights into the feature's behavior and performance in a production environment. Logs should capture relevant information about user actions, data inputs, system responses, and error messages. Monitoring tools can be used to track key metrics such as response times, error rates, and resource utilization. Effective logging and monitoring enable engineers to identify and resolve issues quickly, ensuring the feature's stability and reliability.

Performance Testing

Performance testing is crucial for ensuring that the feature can handle the expected load without performance degradation. This testing should simulate realistic user scenarios and measure key performance indicators (KPIs) such as response times, throughput, and resource utilization. Performance testing helps identify bottlenecks and areas for optimization, ensuring that the feature can scale to meet the demands of its users.

Code Review and Pull Request Best Practices

The code review process plays a vital role in ensuring code quality, maintainability, and adherence to coding standards. Pull requests (PRs) should include clear local testing steps, allowing reviewers to easily verify the functionality of the changes. If applicable, PRs should also include details about Flipper or testing state, providing context for the reviewer. To aid in the review process and ensure that the changes meet the required standards, including Author's local proof of submission screenshot, this offer the reviewer the ability to compare the expected output with the actual result.

Local Testing Steps

Clear and concise local testing steps are essential for reviewers to effectively verify the functionality of the changes. These steps should describe how to set up the development environment, run the code, and test the key features. Detailed testing steps save reviewers time and effort, making the review process more efficient.

Flipper/Testing State Details

If the changes involve feature flags or testing states, the PR should include detailed information about these configurations. This allows reviewers to understand how the changes are affected by different feature flag settings and testing environments. Clear Flipper/testing state details help ensure that the changes are tested thoroughly and that they behave as expected in various scenarios.

Author's Local Proof of Submission Screenshot

Including a screenshot of the author's local proof of submission provides reviewers with visual confirmation that the changes are working as expected. This screenshot can help reviewers quickly identify any discrepancies between the expected output and the actual result. It also demonstrates that the author has thoroughly tested the changes before submitting them for review.

Copilot Review

A Copilot review can help identify potential issues and improve code quality. Copilot is an AI-powered code review tool that can automatically detect common coding errors, security vulnerabilities, and performance bottlenecks. By running a Copilot review before submitting a PR, developers can proactively address potential issues and ensure that their code meets the required standards.

Internal Reviewer Approval

Before a PR can be merged, it should be approved by an internal reviewer. The internal reviewer is typically a senior engineer or a subject matter expert who has a deep understanding of the codebase and the project's goals. The reviewer is responsible for ensuring that the changes are well-designed, well-tested, and aligned with the project's overall architecture.

Internal Reviewer Added Local Proof of Submission Screenshot

To provide an extra layer of verification, the internal reviewer should also add a local proof of submission screenshot. This screenshot confirms that the reviewer has independently verified the changes and that they are working as expected. The reviewer's screenshot provides additional confidence in the quality and correctness of the code.

Code Functionality Verified on Staging

After a PR is merged, the code functionality should be verified on a staging environment. The staging environment is a replica of the production environment, allowing for realistic testing without affecting live users. Verifying the code on staging helps identify any issues that might not have been caught during local testing or code review. This step is crucial for ensuring that the changes are ready for deployment to production.

Refinement Checklist: Ensuring a Comprehensive Approach

A refinement checklist ensures that all aspects of the task are thoroughly considered and addressed. This includes adding a clear description, detailed tasks, and measurable acceptance criteria. Estimating the effort required for the task helps with planning and resource allocation. Labeling the issue with the appropriate practice area (engineer, design, product, data science) ensures that the right expertise is involved. Labeling with the issue type and characteristics (bug, accessibility, request, discovery, documentation, research, content, UX testing, front-end, back-end, Datadog, etc.) helps with categorization and prioritization. Adding relevant project fields (team, OCTO priority...) provides additional context and helps with tracking progress. Finally, associating the issue with an Epic or Super Epic provides a higher-level view of the work and ensures that it aligns with the overall project goals.

By adhering to this comprehensive refinement checklist, the team can ensure that all tasks are well-defined, properly scoped, and aligned with the project's objectives. This leads to more efficient development, higher-quality code, and a smoother overall process.

In conclusion, following up on the Toxic Exposure purge onSubmit feature requires a multifaceted approach, encompassing meticulous code review, enhanced logging, rigorous testing, and adherence to best practices. By prioritizing data integrity and implementing a comprehensive refinement process, the team can ensure the feature operates reliably and protects veterans' sensitive information. For further reading on data integrity and best practices, consider exploring resources from trusted organizations like the National Institute of Standards and Technology (NIST).