Pricing Model
Free
Visit Trag's website for the most up-to-date pricing tiers and features.
The core function of Trag is to optimize the code review process. It pre-reviews the code, identifies issues that need to be addressed, and hence speeds up the review process, saving valuable time for senior engineers.
Trag performs pre-review of code by understanding it in-depth and analyzing it semantically. It uses AI-based methods to inspect the code and find potential issues before they are reviewed by a senior engineer. This includes detecting proactive bugs and suggesting refactoring.
Trag is capable of identifying a wide range of issues in the code review process. These include semantic issues, bugs that may arise in the future, and areas where code could be refactored for improved efficiency and quality.
The uniqueness of Trag's semantic code analysis feature lies in its ability to understand the intent behind the code, not just the syntax. It conducts a deep dive analysis of the code to ensure it aligns with specified patterns and rules, thereby ensuring it meets the required coding standards.
In its effort to assist proactive bug detection, Trag continuously monitors the code to find degradations or improvement areas. It is designed to find these bugs before the code review begins, making the process more efficient and saving engineering time.
Yes, Trag can make refactoring suggestions. It does this by understanding the overall context of the code and identifying areas where large scale changes or improvements can be made. These suggestions are then presented for team review and are not auto-implemented to maintain human control.
Trag provides users with the flexibility to create and implement their own rules. This is done using natural language, enabling users to describe what they want, the tool to look at while reviewing the code, and Trag does the remaining.
Yes, the custom rules created in Trag have a direct impact on pull request changes. Once the rules are defined, Trag matches these rules with the pull request changes and then automates the process to fix those issues.
Trag's auto-fix function is designed to correct identified issues within the code. However, it operates on the principle of not committing changes directly. Instead, it proposes the fixes via pull requests, allowing human reviewers the final say over any changes.
Trag's analytics feature allows teams to monitor pull request analytics for better decision-making. It provides useful data and insights that help teams understand their code review process and improve upon it to achieve faster, more efficient outcomes.
Trag's team collaboration feature supports teamwork within a shared workspace. Teammates can be invited to join the workspace, enabling collaborative efforts for better coding and review practices.
Yes, Trag can connect with multiple repositories for source control integration. It allows users to attach multiple repositories to their account, streamlining the process of code review across different codebases.
While Trag provides automated fixes for identified issues, it does not commit any changes directly. Humans have the final control over changes as fixes are suggested via pull requests for review and approval.
Trag ensures the quality and efficiency of the code through its unique features like in-depth code understanding, semantic code analysis, predictive bug detection, and refactoring suggestions. It also allows users to implement their own rules and aligns the pull requests with these rules before fixing issues automatically.
No, Trag does not commit changes directly after detecting issues. Instead, it suggests the detected issues via pull requests, preserving human reviewers with the final control over accepting any changes.
IDK
Yes, Trag does more than just identify issues in the code. It also suggests changes in the pull requests based on its examinations. However, it does not make direct commits, leaving the final control over changes to the human reviewers.
With Trag, teams can monitor their pull request analytics through its dedicated analytics feature. It provides valuable data and insights on pull requests which help in making faster, better decisions.
Setting up Trag consists of a few easy steps. First, users connect their GitHub account and attach multiple repositories. Then, they write patterns for code review using natural language. Once a pull request is opened, Trag matches these rules with the changes in the pull request and fixes them automatically.
Trag differentiates from other linting tools in terms of its capabilities like complex code understanding across multiple repositories, semantic code analysis that addresses the context behind the code, proactive bug detection that spots potential issues before they occur, and automated but controlled refactoring suggestions.
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