Front-end code quality detection method and system

Abstract

The invention relates to the technical field of code quality management. The invention provides a front-end code quality detection method. The front-end code quality detection method comprises the following steps: converting a front-end code into an abstract syntax tree through a syntax analyzer; performing static analysis on the abstract syntax tree, and identifying error points of front-end codes; performing performance analysis on the abstract syntax tree after static analysis, and identifying potential performance bottlenecks and optimization points of front-end codes; performing security detection on the abstract syntax tree after the performance analysis, and identifying security risks existing in front-end codes; according to the results of the static analysis, the performance analysis and the security detection, generating an automatic repair suggestion, and applying the automatic repair suggestion through a code reconstruction and optimization tool; and generating a corresponding detection report based on the results of the static analysis, the performance analysis and the safety detection and the repair suggestion. Therefore, the problems that an existing front-end code detection method is insufficient in detection comprehensiveness, low in automation degree, poor in customization ability and lack of systematic reports are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of code quality management, and in particular to a front-end code quality detection method and system. Background Art

[0002] Front-end code detection From the earliest manual code review to modern automated static analysis tools, this process demonstrates the continuous progress of software engineering quality assurance. In the early stages of the development of computer science, programmers mainly relied on manual code inspection to find errors and potential problems. This method is very time-consuming and prone to missing details, but it laid the foundation for the development of later automated tools. In the 1990s, with the development of programming languages ​​and development tools, some early static code analysis tools emerged. These tools can detect some basic syntax errors, naming convention problems and some common code defects, helping developers improve code quality and maintainability. With the rapid development of Web development technology, the quality and security of front-end code have become particularly important. The front-end code is directly facing users, and its quality not only affects the user experience, but is also directly related to the security and performance of the application. However, in complex Web projects, the amount of front-end code is huge, and manual code quality and security inspection becomes extremely time-consuming and error-prone.

[0003] Most of the existing front-end code quality detection tools and methods focus on a single aspect of detection, such as syntax error detection, code style checking or performance analysis, and lack a comprehensive and systematic solution. Summary of the invention

[0004] The purpose of the present invention is to provide a front-end code quality detection method and system, aiming to solve the problems of insufficient comprehensiveness, low degree of automation, poor customization capability and lack of systematic reporting in existing front-end code detection methods.

[0005] The present invention is achieved through the following technical solutions:

[0006] A front-end code quality detection method comprises the following steps:

[0007] Convert the front-end code into an abstract syntax tree through a syntax parser;

[0008] Perform static analysis on the abstract syntax tree to identify errors in the front-end code;

[0009] Perform performance analysis on the abstract syntax tree after static analysis to identify potential performance bottlenecks and optimization points of the front-end code;

[0010] Perform security checks on the abstract syntax tree after performance analysis to identify security risks in the front-end code;

[0011] Generate automatic repair suggestions based on the results of static analysis, performance analysis, and security detection, and apply automatic repair suggestions through code refactoring and optimization tools;

[0012] Generate corresponding test reports based on the results of static analysis, performance analysis, and security testing, as well as repair suggestions.

[0013] Optionally, the static analysis includes: syntax error detection, code specification checking and logic error analysis; wherein, syntax error detection is used to identify syntax errors or spelling errors in the code; code specification checking is used to check whether the code complies with corresponding coding specifications and actual requirements; logic error analysis is used to detect potential logic errors.

[0014] Optionally, the performance analysis includes: resource loading analysis and execution time analysis; wherein, resource loading analysis is used to analyze the order, size and optimization points of resource loading; execution time analysis is used to analyze the time complexity of front-end code execution and identify potential performance issues.

[0015] Optionally, the security detection includes: cross-site scripting attack detection and cross-site request forgery detection; wherein, cross-site scripting attack detection is used to detect whether there are potential vulnerabilities in the front-end code that lead to cross-site scripting attacks; cross-site request forgery detection is used to check whether there are front-end code fragments in the front-end code that lead to cross-site request forgery attacks.

[0016] Optionally, the automatic repair suggestions include: code optimization suggestions, performance optimization suggestions and security repair suggestions; among which, the code optimization suggestions are used to guide the optimization process of the front-end code and give corresponding front-end code refactoring solutions; the performance optimization suggestions are used to give specific measures for performance optimization; and the security repair suggestions are used to provide specific measures for security repairs and corresponding front-end codes.

[0017] Optionally, the test report is output in HTML or PDF format.

[0018] Optionally, perform front-end code validation on the generated automatic fix suggestions.

[0019] Optionally, the static analysis, performance analysis and security detection processes support custom rules and configurations.

[0020] The present invention also provides a front-end code quality detection system, which is used to implement the front-end code quality detection method, including:

[0021] Code parsing module, used to convert front-end code into an abstract syntax tree;

[0022] Static analysis module, used to detect syntax errors, check code specifications, and analyze logical errors on the abstract syntax tree;

[0023] Performance analysis module, used to analyze resource loading and execution time of front-end code;

[0024] Security detection module, used to detect security risks of front-end code;

[0025] The automatic repair module is used to generate automatic repair suggestions based on the results of static analysis, performance analysis, and security detection, and apply the automatic repair suggestions through code refactoring and optimization tools;

[0026] The report generation module is used to generate detailed reports containing detection results, repair suggestions, and code optimization suggestions.

[0027] Optionally, it also includes:

[0028] The custom configuration module is used for users to customize the rules of static analysis, performance analysis, and security detection according to project requirements.

[0029] The technical solution of the present invention has at least the following advantages and beneficial effects:

[0030] Improve code quality: Through comprehensive front-end code quality testing, including syntax error detection, code standard checking, logic error analysis, performance analysis, and security testing, various problems in the code can be discovered and fixed in a timely manner, significantly improving the quality and stability of the code.

[0031] Improve development efficiency: The automatic repair module of the present invention can provide automatic repair suggestions for problems that can be automatically repaired, and automatically apply these repairs, which greatly reduces the burden on developers and improves development efficiency.

[0032] Optimize front-end performance: The performance analysis module can accurately identify performance bottlenecks in the front-end code through resource loading analysis and execution time analysis, and provide targeted optimization suggestions, which helps to reduce resource loading time, improve page rendering speed, and optimize user experience.

[0033] Enhanced security: The security detection module effectively identifies security risks in the front-end code through cross-site scripting attack detection and cross-site request forgery detection, and provides corresponding repair suggestions, which helps to protect user data and system security and prevent potential security vulnerabilities from being maliciously exploited.

[0034] Support custom configuration: The present invention provides a custom configuration module, allowing users to customize the rules of static analysis, performance analysis and security detection according to project requirements, so that the system can adapt to different development environments and coding specifications to meet diverse needs.

[0035] Improve code maintainability: Through detailed detection reports, including detection results, repair suggestions and code optimization suggestions, the present invention provides developers with a comprehensive code quality assessment, which helps developers to quickly understand and improve the code, and improve the maintainability and scalability of the code.

[0036] Promote team collaboration: Standardized code quality detection processes and detailed detection reports help team members share code quality information, promote team collaboration and code sharing; at the same time, automated detection tools reduce the impact of human factors on code quality and improve the overall development level of the team. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 This is a flow chart of the front-end code quality detection method in Embodiment 1 of the present invention;

[0038] Figure 2 This is a schematic diagram of the structure of the front-end code quality detection system in Example 2 of the present invention. DETAILED DESCRIPTION

[0039] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0040] Example 1

[0041] Reference Figure 1 , a front-end code quality detection method, characterized in that it includes the steps of:

[0042] Step 1: Convert the front-end code into an abstract syntax tree through a syntax parser;

[0043] In this embodiment, for HTML code, the html parser2 parser can be used for parsing, and the HTML code can be converted into an abstract syntax tree; for CSS code, postcss can be used with a corresponding parser, such as sugarss, scss, etc., and the parser is selected according to the CSS preprocessor to convert the CSS code into an abstract syntax tree; for JavaScript code, acorn, espr ima or babel-parser can be used for parsing, and the JavaScript code can be converted into an abstract syntax tree.

[0044] Step 2: Perform static analysis on the abstract syntax tree to identify errors in the front-end code.

[0045] In this embodiment, static analysis includes: syntax error detection, code standard checking and logic error analysis; among them, syntax error detection is used to identify syntax errors or spelling errors in the code; code standard checking is used to check whether the code complies with the corresponding coding standards and actual requirements; logic error analysis is used to detect potential logic errors.

[0046] In this embodiment, the corresponding code standard tool is selected for static analysis according to the type of the front-end code; for HTML code, HTMLH i nt can be configured; for CSS code, Styleline can be configured; for JavaScript code, ESL i nt can be configured. By configuring these static analysis tools, the coding standards and best practices of the project or team can be met. After the static analysis tool is configured, the abstract syntax tree corresponding to the front-end code to be detected is read.

[0047] Syntax Error Detection: Uses the abstract syntax tree structure and the built-in rules of static analysis tools to check whether there are syntax errors or spelling errors in the code; reports all identified syntax problems and provides the error location and corresponding description.

[0048] Coding standard check: Apply predefined coding standard rule sets, such as ESLind rule set, Stylellint rule set, etc.; check whether the code complies with the standards, including but not limited to indentation, spaces, semicolon usage, naming conventions, etc.; report all code snippets that do not comply with the standards and provide corresponding optimization suggestions.

[0049] Logical Error Analysis: Analyzes the abstract syntax tree to identify potential logical errors, such as undefined variables, invalid function calls, misuse of loops or conditional statements, etc.; pays special attention to scope issues, variable overwriting, and potential uncaught exceptions; reports detailed information about potential logical errors and provides corresponding solutions.

[0050] Step 3: Perform performance analysis on the abstract syntax tree after static analysis to identify potential performance bottlenecks and optimization points of the front-end code;

[0051] In this embodiment, performance analysis includes: resource loading analysis and execution time analysis; wherein, resource loading analysis is used to analyze the order, size and optimization points of resource loading; execution time analysis is used to analyze the time complexity of front-end code execution and identify potential performance issues. Identify specific performance bottlenecks and optimization points, such as slow resource loading, long JavaScript execution time, etc.; generate performance optimization suggestions, such as compressing resources, optimizing caching strategies, reducing DOM operations, etc. In the front-end code quality detection process, performance analysis can be performed as an independent step, and the performance analysis results can be integrated into the final detection report together with static analysis and security detection results.

[0052] Resource loading analysis: Use performance analysis tools to analyze the loading of front-end page resources, such as Lighthouse, WebPageTest, etc. Install and run Lighthouse, and use commands to perform performance analysis on the pages generated by the front-end code. Lighthouse will generate a detailed performance report, including evaluations of loading time, interaction time, SEO, best practices, and other aspects; analyze the order of resource loading and check whether there is the possibility of parallel loading to reduce the total loading time; check the size of resources, identify files that can be compressed, such as CSS, JavaScript, images, etc., and make compression suggestions; analyze and optimize caching strategies to ensure that static resources are effectively cached and reduce repeated loading; evaluate the priority of resource loading to ensure that key resources are loaded first.

[0053] Execution time analysis: Use the browser's developer tools to analyze JavaScript execution time, such as Chrome DevTools. Open the developer tools in the Chrome browser, switch to the "Performance" tab, record the loading and execution process of the page, analyze the timeline view, identify time-consuming functions or events, and use the "Bottom-Up" or "Call Tree" view to deeply analyze the time and source of function calls; perform performance analysis on key functions or code blocks to identify parts with long execution time; evaluate the time complexity of the code, and consider optimizing algorithms or logic for parts with higher complexity; check whether there are unnecessary DOM operations or redrawing / reflowing, which will increase the rendering time of the page; analyze and optimize asynchronous loading strategies, such as using asynchronous loading of JavaScript and CSS to reduce page parsing blocking.

[0054] Step 4: Perform security checks on the abstract syntax tree after performance analysis to identify security risks in the front-end code.

[0055] In this embodiment, security detection includes: cross-site scripting attack detection and cross-site request forgery detection; among them, cross-site scripting attack detection is used to detect whether there are potential vulnerabilities in the front-end code that lead to cross-site scripting attacks; cross-site request forgery detection is used to check whether there are front-end code fragments in the front-end code that lead to cross-site request forgery attacks.

[0056] Cross-site scripting attack detection: You can use automated tools such as OWASP ZAP (Zed Attack Proxy) and Snyk, which can automatically scan XSS vulnerabilities in the code; combine security plug-ins (such as eslink-plugin-security) in JavaScript static analysis tools such as ESLin to identify potential XSS risks, such as unvalidated user input directly inserted into the DOM; use automated testing tools (such as Selenium) to simulate user input and check whether the page has properly escaped special characters (such as <,>, &, etc.). Install and configure tools such as OWASP ZAP or Snyk, set scanning rules and parameters; run security detection tools to scan front-end code, especially those involving user input and output; check the scan report to identify potential XSS vulnerabilities; generate repair suggestions based on the scan results, such as using appropriate escape functions (such as JavaScript's textContent instead of innerHTML), or implementing content security policy (CSP).

[0057] Cross-site request forgery detection: Check if HTML forms and AJAX requests contain appropriate CSRF protection measures, such as CSRF tokens; simulate CSRF attack scenarios to check if the backend verifies the validity of the CSRF token. Check all form submissions and AJAX requests to confirm if they contain CSRF tokens; confirm that the backend service verifies the validity of the CSRF token when receiving requests; ensure that session management, Cookies and other security configurations are correct to reduce the risk of CSRF; verify the effectiveness of defense measures by simulating CSRF attacks.

[0058] Step 5: Generate automatic repair suggestions based on the results of static analysis, performance analysis, and security testing, and apply the automatic repair suggestions through code refactoring and optimization tools;

[0059] In this embodiment, the automatic repair suggestions include: code optimization suggestions, performance optimization suggestions and security repair suggestions; among them, the code optimization suggestions are used to guide the optimization process of the front-end code and give corresponding front-end code refactoring solutions; the performance optimization suggestions are used to give specific measures for performance optimization; the security repair suggestions are used to provide specific measures for security repairs and corresponding front-end codes.

[0060] In this embodiment, the results of static analysis, performance analysis, and security testing are integrated into a unified data structure. This data structure can clearly represent the detailed information of each problem point, such as syntax errors, code standard issues, performance bottlenecks, security vulnerabilities, including the problem type, location, description, and possible impact. Based on the integrated analysis results, repair suggestions are automatically generated. This can be achieved by writing corresponding logic code or using existing tool libraries. For example:

[0061] For grammatical errors and spelling errors: they can be automatically fixed directly by the syntax parser or static analysis tools (such as ESLint);

[0062] For coding standards issues, you can use code formatting tools (such as Prettier) and coding standards checking tools (such as ESLinnt) to provide automatic fix suggestions and automatically apply these fixes;

[0063] For simple logic errors (such as undefined variables), static analysis tools can provide repair suggestions;

[0064] For complex logic problems, the problems can be fed back to the corresponding technical personnel through the exchange interface for processing, and corresponding auxiliary prompts will be given;

[0065] For performance optimization suggestions: based on the performance analysis results, you can generate optimization suggestions such as resource compression, code segmentation, asynchronous loading, etc., which can be assisted by tools (such as Webpack plug-ins);

[0066] For security repair suggestions: Specific repair suggestions can be provided for detected security vulnerabilities (such as XSS, CSRF), such as input validation, output encoding, etc., and implemented according to specific application scenarios.

[0067] In this embodiment, for problems that can be automatically fixed directly (such as syntax errors and simple code standard issues), the corresponding tools automatically apply the fixes. For example, the --fix option of ESLint is used to automatically fix some problems in JavaScript code; for repair suggestions that require manual intervention (such as complex logical errors, performance optimization, and security repairs), the repair suggestions are clearly displayed in the generated report, and sufficient context information is provided so that developers can quickly understand and implement these suggestions.

[0068] In this embodiment, the generated automatic repair suggestions are verified by the front-end code. After applying the automatic repair suggestions or manual repairs, the static analysis, performance analysis, and security detection tools are rerun to ensure that the problems have been correctly resolved and no new problems have been introduced. This verification process can be automated or manual, depending on the needs and resources of the project. Based on the results of the repair, the detection report is updated to remove the resolved problems from the problem list and add any newly discovered problems. At the same time, the repair suggestion section is updated to reflect the implemented fixes and any remaining issues to be fixed.

[0069] Step 6: Generate a corresponding test report based on the results of static analysis, performance analysis, and security testing, as well as repair suggestions.

[0070] In this embodiment, before generating the detection report, it is first necessary to plan the content structure of the report to ensure that the report can comprehensively and clearly display the detection results, repair suggestions and optimization suggestions of the front-end code. Briefly introduce the purpose, scope, tools and methods used in the detection; list all detected syntax errors and their locations; point out places that do not meet the coding standards, such as irregular variable naming, lack of comments, etc.; describe potential logical problems, such as undefined variables, invalid function calls, etc.; analyze performance bottlenecks in resource loading, execution time, etc.; list detected security vulnerabilities, such as XSS, CSRF, etc. Provide specific solutions for code refactoring to improve code quality and maintainability; provide specific measures for performance optimization, such as resource compression, asynchronous loading, etc.; provide specific measures for security repairs, such as input validation, output encoding, etc. Summarize the main findings of the report and give suggestions for follow-up actions, such as which problems to fix first and how to improve code quality.

[0071] In this embodiment, after the report content is determined, a script or program can be written to automatically generate the report. Collect detection results and repair suggestions from static analysis, performance analysis and security detection tools; design an HTML or PDF template to format the report content. The template should contain the necessary HTML structure, CSS style and possible JavaScript scripts to dynamically insert detection results and repair suggestions; fill the collected data into the corresponding positions in the template, which usually involves operations such as string replacement and dynamic creation of HTML elements; save the filled template as an HTML or PDF file, and specify the file name and save path.

[0072] In this embodiment, the detection report is output in HTML or PDF format, which is convenient for developers to read, provides developers with a comprehensive and clear code quality assessment, and gives specific improvement suggestions.

[0073] In this embodiment, static analysis, performance analysis and security detection processes support custom rules and configurations. Corresponding options can be set in the configuration files of the detection tools. For example, custom code standard rules can be defined in the configuration files of tools such as ESLind and Styleline, and then corresponding code standard issues can be displayed according to these rules when generating reports. Performance analysis and security detection tools also support similar custom configurations. When generating reports, these configurations can be read and the content and format of the report can be adjusted according to the configurations.

[0074] In this embodiment, a configuration file in JSON or YAML format is provided, in which the user can define rules for static analysis, performance analysis, and security detection.

[0075] Configuration items include but are not limited to: rule set paths or custom rules for tools such as ESLind, Styleline, and HTMLHind; parameter settings for performance analysis tools, such as Lighthouse's preset options; and scanning rules and parameters for security detection tools.

[0076] For users who are not familiar with the configuration file format, a graphical configuration interface is provided; all configurable rule items are listed on the interface, and users can set rules by checking, selecting or entering values; the interface is synchronized with the configuration file, and the user's settings are automatically saved to the configuration file. When the system starts or after the user reconfigures, the configuration file or configuration interface settings are read; the read configuration is applied to the corresponding detection module to ensure that the detection process follows the user-defined rules.

[0077] You can use the front-end code quality detection system in this embodiment and set strict security detection rules and performance analysis parameters through a custom configuration module. The system will detect the front-end code according to these rules and generate a report containing detailed repair suggestions. Developers can repair and optimize the code according to the suggestions in the report, thereby improving code quality and project security.

[0078] Example 2

[0079] Reference Figure 2 Based on Example 1, this embodiment provides a front-end code quality detection system for implementing a front-end code quality detection method, including:

[0080] Code parsing module, used to convert front-end code into an abstract syntax tree;

[0081] Static analysis module, used to detect syntax errors, check code specifications, and analyze logical errors on the abstract syntax tree;

[0082] Performance analysis module, used to analyze resource loading and execution time of front-end code;

[0083] Security detection module, used to detect security risks of front-end code;

[0084] The automatic repair module is used to generate automatic repair suggestions based on the results of static analysis, performance analysis, and security detection, and apply the automatic repair suggestions through code refactoring and optimization tools;

[0085] The report generation module is used to generate detailed reports containing detection results, repair suggestions, and code optimization suggestions.

[0086] The custom configuration module is used for users to customize the rules of static analysis, performance analysis, and security detection according to project requirements.

[0087] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A front-end code quality detection method, characterized in that: Includes steps: Convert the front-end code into an abstract syntax tree through a syntax parser; Perform static analysis on the abstract syntax tree to identify errors in the front-end code; Perform performance analysis on the abstract syntax tree after static analysis to identify potential performance bottlenecks and optimization points of the front-end code; Perform security checks on the abstract syntax tree after performance analysis to identify security risks in the front-end code; Generate automatic repair suggestions based on the results of static analysis, performance analysis, and security detection, and apply automatic repair suggestions through code refactoring and optimization tools; Generate corresponding test reports based on the results of static analysis, performance analysis, and security testing, as well as repair suggestions.

2. The front-end code quality detection method according to claim 1, characterized in that: The static analysis includes: syntax error detection, code specification checking and logic error analysis; among them, syntax error detection is used to identify syntax errors or spelling errors in the code; code specification checking is used to check whether the code complies with the corresponding coding specifications and actual requirements; logic error analysis is used to detect potential logic errors.

3. The front-end code quality detection method according to claim 1, characterized in that: The performance analysis includes: resource loading analysis and execution time analysis; wherein, resource loading analysis is used to analyze the order, size and optimization points of resource loading; execution time analysis is used to analyze the time complexity of front-end code execution and identify potential performance problems.

4. The front-end code quality detection method according to claim 1, characterized in that: The security detection includes: cross-site scripting attack detection and cross-site request forgery detection; among which, cross-site scripting attack detection is used to detect whether there are potential vulnerabilities in the front-end code that lead to cross-site scripting attacks; cross-site request forgery detection is used to check whether there are front-end code fragments in the front-end code that lead to cross-site request forgery attacks.

5. The front-end code quality detection method according to claim 1, characterized in that: The automatic repair suggestions include: code optimization suggestions, performance optimization suggestions and security repair suggestions; among them, the code optimization suggestions are used to guide the optimization process of the front-end code and provide corresponding front-end code reconstruction solutions; the performance optimization suggestions are used to provide specific measures for performance optimization; and the security repair suggestions are used to provide specific measures for security repairs and corresponding front-end codes.

6. The front-end code quality detection method according to claim 1, characterized in that: The test report is output in HTML or PDF format.

7. The front-end code quality detection method according to claim 1, characterized in that: Perform front-end code validation on generated automatic fix suggestions.

8. The front-end code quality detection method according to claim 1, characterized in that: The static analysis, performance analysis, and security detection processes support custom rules and configurations.

9. A front-end code quality detection system, used to implement the front-end code quality detection method according to any one of claims 1 to 9, characterized in that: include: Code parsing module, used to convert front-end code into an abstract syntax tree; Static analysis module, used to detect syntax errors, check code specifications, and analyze logical errors on the abstract syntax tree; Performance analysis module, used to analyze resource loading and execution time of front-end code; Security detection module, used to detect security risks of front-end code; The automatic repair module is used to generate automatic repair suggestions based on the results of static analysis, performance analysis, and security detection, and apply the automatic repair suggestions through code refactoring and optimization tools; The report generation module is used to generate detailed reports containing detection results, repair suggestions, and code optimization suggestions.

10. The front-end code quality detection system according to claim 9, characterized in that: Also includes: The custom configuration module is used for users to customize the rules of static analysis, performance analysis, and security detection according to project requirements.

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