Distributed cloud-native computing architecture supports flexible scaling, with faster and more stable execution.
Distributed cloud-native computing architecture supports flexible scaling, with faster and more stable execution.
Many self-developed and well-known open-source tools have been integrated, adopting a hierarchical and separated architecture to meet the needs of teams for rapid self-service tool management.
Many self-developed and well-known open-source tools have been integrated, adopting a hierarchical and separated architecture to meet the needs of teams for rapid self-service tool management.
Supports dozens of languages such as Java, C++, Objective-C, C#, JavaScript, Python, Go, and PHP, covering commonly used programming languages.
Supports dozens of languages such as Java, C++, Objective-C, C#, JavaScript, Python, Go, and PHP, covering commonly used programming languages.
Incremental analysis quickly identifies issues, while full analysis ensures full coverage of problems.
Incremental analysis quickly identifies issues, while full analysis ensures full coverage of problems.
Supports rapid access to distributed client nodes, including Mac, Linux, Windows, and Docker, improves the efficiency of concurrent analysis, and supports high-frequency analysis scenarios for users locally.
Supports rapid access to distributed client nodes, including Mac, Linux, Windows, and Docker, improves the efficiency of concurrent analysis, and supports high-frequency analysis scenarios for users locally.
Supports configuring and using Tencent Cloud Code Assistant, Hunyuan, DeepSeek, etc., to provide AI-powered repair suggestions for analysis issues.
Supports configuring and using Tencent Cloud Code Assistant, Hunyuan, DeepSeek, etc., to provide AI-powered repair suggestions for analysis issues.
Supports custom configuration of rules, adjustment of metrics, setting of quality gates, new issue baselines, etc., helping businesses drive issue resolution and gradually optimize code.
Supports custom configuration of rules, adjustment of metrics, setting of quality gates, new issue baselines, etc., helping businesses drive issue resolution and gradually optimize code.
Graphical visualization report, easily monitor related trends.
Graphical visualization report, easily monitor related trends.
Provide standardized API interfaces, supporting rapid integration with DevOps platforms.
Provide standardized API interfaces, supporting rapid integration with DevOps platforms.
Distributed cloud-native computing architecture supports flexible scaling, with faster and more stable execution.
Many self-developed and well-known open-source tools have been integrated, adopting a hierarchical and separated architecture to meet the needs of teams for rapid self-service tool management.
Supports dozens of languages such as Java, C++, Objective-C, C#, JavaScript, Python, Go, and PHP, covering commonly used programming languages.
Incremental analysis quickly identifies issues, while full analysis ensures full coverage of problems.
Supports rapid access to distributed client nodes, including Mac, Linux, Windows, and Docker, improves the efficiency of concurrent analysis, and supports high-frequency analysis scenarios for users locally.
Supports configuring and using Tencent Cloud Code Assistant, Hunyuan, DeepSeek, etc., to provide AI-powered repair suggestions for analysis issues.
Supports custom configuration of rules, adjustment of metrics, setting of quality gates, new issue baselines, etc., helping businesses drive issue resolution and gradually optimize code.
Graphical visualization report, easily monitor related trends.
Provide standardized API interfaces, supporting rapid integration with DevOps platforms.
Upon completion of code analysis, only issue location data (such as line number, cause, and responsible person) is reported. The TCA platform does not upload or store user source code, so you can use it with confidence.
Supports dozens of languages such as Java/C++/Objective-C/C#/JavaScript/Python/Go/PHP, covering commonly used programming languages. For specific details, please refer to the platform.
In scenarios where code is not executed, there are speculative algorithms. The tool will prioritize exposing code issues, and then the R&D personnel corresponding to the business will confirm whether it needs to be marked as ignored. If the business R&D personnel assess that there are no issues or it does not actually affect functionality, the issue can be marked as ignored, or ignored using code comments.
TCA adopts a distributed client architecture, supporting one-click access to dedicated machine resources for teams and team projects, making it convenient for customers to independently manage and control machine resources and compilation environments. It provides various forms of access to dedicated machine resources, such as binary packages and Docker images, which are simple and efficient.
- Eliminates the need to rely on CI resources, reducing the startup execution call chain of code analysis.
- Can manage and control the quantity of dedicated machine resources based on business scale, execute code analysis in a distributed manner, increase analysis concurrency, reduce execution queuing, and accelerate analysis speed.
- Can access Mac dedicated machine resources to conduct code analysis for Object-C projects.
- Development machines of various businesses can also be added to the list of dedicated machine resources, which can effectively utilize development resources and reduce machine costs.
- Business parties can independently manage and control the compilation environment of dedicated machines, designate the corresponding dedicated machines to execute analysis tasks, and avoid compilation failure exceptions when analysis projects that require compilation execute code analysis.
Upon completion of code analysis, only issue location data (such as line number, cause, and responsible person) is reported. The TCA platform does not upload or store user source code, so you can use it with confidence.
Supports dozens of languages such as Java/C++/Objective-C/C#/JavaScript/Python/Go/PHP, covering commonly used programming languages. For specific details, please refer to the platform.
In scenarios where code is not executed, there are speculative algorithms. The tool will prioritize exposing code issues, and then the R&D personnel corresponding to the business will confirm whether it needs to be marked as ignored. If the business R&D personnel assess that there are no issues or it does not actually affect functionality, the issue can be marked as ignored, or ignored using code comments.
TCA adopts a distributed client architecture, supporting one-click access to dedicated machine resources for teams and team projects, making it convenient for customers to independently manage and control machine resources and compilation environments. It provides various forms of access to dedicated machine resources, such as binary packages and Docker images, which are simple and efficient.
- Eliminates the need to rely on CI resources, reducing the startup execution call chain of code analysis.
- Can manage and control the quantity of dedicated machine resources based on business scale, execute code analysis in a distributed manner, increase analysis concurrency, reduce execution queuing, and accelerate analysis speed.
- Can access Mac dedicated machine resources to conduct code analysis for Object-C projects.
- Development machines of various businesses can also be added to the list of dedicated machine resources, which can effectively utilize development resources and reduce machine costs.
- Business parties can independently manage and control the compilation environment of dedicated machines, designate the corresponding dedicated machines to execute analysis tasks, and avoid compilation failure exceptions when analysis projects that require compilation execute code analysis.