CN111837109A - Code quality and defect analysis method, server and storage medium - Google Patents
Code quality and defect analysis method, server and storage medium Download PDFInfo
- Publication number
- CN111837109A CN111837109A CN201980010312.1A CN201980010312A CN111837109A CN 111837109 A CN111837109 A CN 111837109A CN 201980010312 A CN201980010312 A CN 201980010312A CN 111837109 A CN111837109 A CN 111837109A
- Authority
- CN
- China
- Prior art keywords
- defect
- code
- quality
- detection
- data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/36—Preventing errors by testing or debugging software
- G06F11/3668—Software testing
- G06F11/3672—Test management
- G06F11/3688—Test management for test execution, e.g. scheduling of test suites
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/36—Preventing errors by testing or debugging software
- G06F11/3668—Software testing
- G06F11/3672—Test management
- G06F11/3692—Test management for test results analysis
Abstract
A method, a server and a storage medium for analyzing code quality and defects include: acquiring a code to be detected according to a preset trigger condition (S21); presetting detection dimensions, detecting the codes to be detected, and obtaining detection data corresponding to the detection dimensions (S22); generating a quality data report based on the detected dimensions and the detected data (S23); and drawing a dimension data chart (S24) according to the quality data report and the defect management report, thereby combining the code quality report with the defect type, showing the quality of the code and the defect problem in the form of the dimension data chart and improving the accuracy of code quality detection.
Description
Technical Field
The present invention relates to the field of computer technologies, and in particular, to a method, a server, and a storage medium for analyzing code quality and defects.
Background
With the rapid development of internet technology and intelligent devices, more and more functions can be realized in a software mode, so that the quality of code writing of developers can directly influence the progress of software development and even influence the experience of users.
At present, a plurality of types of code quality detection tools exist in the market, and the tools are mainly used for helping developers to quickly and effectively locate and find code defects through a means of analyzing and scanning source programs by static codes, evaluating the code quality and further providing a reference standard for improving the code quality, so that the software reliability is greatly improved.
The code quality detection tool mainly detects and manages the quality of static codes, detects the correctness of programs by analyzing and/or checking grammars, structures, processes, interfaces and the like of source programs, and further positions hidden errors and defects of the codes. However, static code scanning requires less heavy patterns and formats, which affects the accuracy of detection.
Disclosure of Invention
The embodiment of the invention provides a code quality and defect analysis method, a server and a storage medium, which improve the accuracy of code quality detection.
In order to solve the above technical problem, an embodiment of the present invention provides a method for analyzing code quality and defects, including:
acquiring a code to be detected according to a preset trigger condition;
presetting detection dimensions, and detecting the codes to be detected to obtain detection data corresponding to the detection dimensions;
generating a quality data report according to the detection dimension and the detection data;
and drawing a dimension data chart according to the quality data report and in combination with the defect management report.
Optionally, the acquiring the code to be detected includes:
compiling the code to be detected;
or combining the codes to be detected.
Optionally, the drawing a dimension data chart according to the quality data report in combination with a defect management report includes:
generating the defect management report;
and extracting the detection dimension and the detection data in the quality data report, associating the detection dimension with the defect type in the defect management report, and drawing the dimension data chart.
Optionally, the generating the defect management report includes:
presetting a plurality of defect types, wherein the defect types comprise a function defect, a UI defect, a demand defect, a design defect, a security vulnerability and a performance problem;
performing label classification on the defect types, wherein the label classification comprises human and item classes;
and generating the defect management report by combining the label classification according to the defect type.
Optionally, after the drawing the dimension data table according to the quality data report and in combination with the defect management report, the method further includes:
and predicting the Bug condition in the code to be detected according to the dimension data chart.
Optionally, the dimension data chart is any one of a pie chart, a line chart and a radar chart.
Optionally, the triggering condition includes that the system reaches a preset triggering time or the system state meets a preset triggering event state.
Optionally, the detection dimensions include programming specifications, potential defects, annotations, repetition code, complexity, test coverage, and architectural design.
In a second aspect, an embodiment of the present invention provides a server, where the server includes:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the memory stores a program of instructions executable by the at least one processor to cause the at least one processor to perform any one of the methods of analyzing code quality and defects.
In a third aspect, embodiments of the invention provide a non-transitory computer storage medium having stored thereon computer-executable instructions that, when executed by one or more processors, cause the one or more processors to perform any of the methods for analyzing code quality and defects.
The beneficial effects of the embodiment of the application are that: in contrast to the prior art, an analysis method, a server, and a storage medium for code quality and defect provided in an embodiment of the present application include: acquiring a code to be detected according to a preset trigger condition; presetting detection dimensions, and detecting the codes to be detected to obtain detection data corresponding to the detection dimensions; generating a quality data report according to the detection dimension and the detection data; and drawing a dimension data chart by combining the defect management report according to the quality data report, thereby combining the code quality report with the defect type, showing the quality and defect problems of the code in the form of the dimension data chart, improving the accuracy of code quality detection and realizing the imaging of the detection result.
Drawings
One or more embodiments are illustrated by way of example in the accompanying drawings, which correspond to the figures in which like reference numerals refer to similar elements and which are not to scale unless otherwise specified.
FIG. 1 is a schematic diagram of an architecture of a system for analyzing code quality and defects according to an embodiment of the present invention;
FIG. 2a is a schematic flow chart of a method for analyzing code quality and defects according to an embodiment of the present invention;
FIG. 2b is a schematic flow chart of a method for analyzing code quality and defects according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a server according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1, an embodiment of the present invention provides a system for analyzing code quality and defect, where the system 10 includes a client 11, a temporary database 12, a Jenkins service 13, a SonarQube14, a Tapd defect management 15, and a GitHub code library 16.
The client 11 is used for developing project-related codes, and developers edit the related codes at the client 11.
And the temporary database 12 is used for temporarily storing the codes to be detected uploaded by the client 11.
Jenkins service 13, Jenkins is a continuous integration tool for monitoring continuous and repetitive work, and can automate various tasks, such as building, testing, or deploying software. When the system meets the triggering condition, Jenkins is triggered to automatically construct a project, and the codes to be detected temporarily stored in the temporary database are read into the project space Workspace.
In the application, Jenkins is used for triggering the automatic test task, specifically, the Jenkins timing task is set, and the SonarQube test tool is triggered to execute the automatic test.
SonarQube14, an open source code quality inspection tool, supports code quality management and detection of multiple languages, such as Java, Python, Groovy, C + + and other dozens of programming languages, provides visual reports, can quickly locate problems, and is used for continuously analyzing and evaluating the quality of project source codes. The problems of repeated codes, potential bugs, code specifications, security vulnerabilities and the like in the codes to be detected can be detected through a SonarQube tool, and particularly, the main core value of the method is embodied in the following aspects:
check whether the code complies with the programming standard: such as a naming convention, a written convention, etc.
Checking the design for potential defects: SonarQube detects the defects of the code by a tool such as plug-in Findbugs, Checkstyle and the like.
Detecting a repeated code amount of the code: SonarQube can show that there is a large amount of copy-and-paste code in the project.
Degree of annotation in the detection code: too many or too few source code annotations are not good, which affects the readable intelligibility of the program.
Detecting the relation between packages and classes in the code: and analyzing whether the relation between the classes is reasonable or not and analyzing the complexity condition.
From the above, the detection of the code quality can be summarized in the following seven dimensions.
1. The programming specification, Sonar, can be written by code rule detection tool specification codes such as PMD, CheckStyle, FindBugs and the like.
2. Potential defects, Sonar, can be detected by code rule detection tools such as PMD, CheckStyle, FindBugs, etc.
3. Complexity distributions, for files, classes, methods, etc., are difficult to change if the complexity is too high, making them difficult for developers to understand, and, without automated testing, changes to any component in the program may result in comprehensive regression testing.
4. Repeatability, obviously, the code containing a large number of copy-paste in the program is of low quality, and Sonar can show places with serious repetition in the source code.
5. Insufficient or excessive annotations, the absence of which would deteriorate the readability of the code, especially when variations of personnel inevitably occur, would considerably decrease the readability of the program, while excessive annotations would make the developer excessively spend on reading the annotations, contrary to the original intention.
6. And the unit test, Sonar, can conveniently count and display the unit test coverage rate.
7. And (4) architectural design, circulation can be found out through Sonar, the interdependence relation between the package and the package, between the classes and between the classes can be displayed, and the self-defined architectural rule can be detected. Third party Jar packages can be managed through Sonar, and LCOM4 can be used for detecting application of single task rules and detecting coupling.
In the application, in order to realize sustainable monitoring, the support of a sustainable integration tool Jenkins is required, before the version is built, a project analysis instruction is executed through the Jenkins + Sonar plug-in, and the SonarScanner is called through the Jenkins plug-in to continuously scan the code to be detected. And the SonarQube performs code quality detection according to the seven dimensions to generate a code quality report.
And the Tapd defect management unit 15 is used for recording software defects detected by a tester in the testing process, tracking and managing the defects found in each testing stage, collecting defect data, analyzing the data and making the basis of defect measurement. Software defects are mainly divided into defects (defects), faults (faults) and failures (Failure). It will be appreciated that the tester, when submitting a defect, needs to describe the defect, the process of finding the defect, and some of the manifestations of the defect, and that the defect has certain relevant attributes.
Specifically, the software defects are further divided into a plurality of defect types according to related attributes, wherein the defect types include a functional defect, a UI defect, a demand defect, a design defect, a security vulnerability, a performance problem and the like. The Tapd defect management 15 also classifies the above defects by labels, which include personal class labels and item class labels. The Tapd defect management 15 also integrates the historical defect data to draw a defect management report.
And the GitHub code library 16 is an open-source and private software project-oriented hosting platform and is used for storing main and/or branch codes, and the codes to be detected which are temporarily stored in the temporary database 12 are compiled and combined into a directory corresponding to the GitHub code library after being check by a manager.
On the other hand, the GitHub code library 16 combines the code quality report with the defect type according to the code quality report provided by SonarQube and the defect management report generated by Tapd defect management, generates a dimension data chart, and shows the code quality and defect problems in the form of the chart, thereby improving the accuracy of code quality detection.
Referring to fig. 2a, an embodiment of the invention provides a method for analyzing code quality and defects, including:
s21, acquiring the code to be detected according to the preset trigger condition;
the triggering condition includes that the system reaches a preset triggering time or the system state meets a preset triggering event state, and it can be understood that the triggering condition may be periodic triggering or real-time triggering according to the triggering event. The periodic triggering is performed, for example, by circularly calling a timer method, that is, a trigger time is preset in the timer, so that the system periodically performs triggering according to the trigger time. The triggering is performed in real time according to a triggering event, that is, the system performs the step when the triggering event occurs. Wherein the triggering event includes, but is not limited to, pulling a new code branch or updating branch code.
Acquiring a code to be detected, comprising: compiling the code to be detected; or combining the codes to be detected. It can be understood that the above-mentioned codes to be detected are temporarily stored in the temporary database.
In the application, periodic triggering is realized by constructing a timing task of a Jenkins service of a third party, wherein Jenkins is a continuous integration tool used for monitoring continuous and repeated work, and various tasks such as constructing, testing or deploying software can be automated. And when the system meets the trigger, calling an API (application program interface) of Jenkins service, connecting Jenkins of continuous integration service, and constructing a pre-configured automatic test task. Before executing an automatic test task, the code to be detected needs to be updated to Jenkins project space Workspace.
S22, presetting detection dimensions, detecting the codes to be detected, and obtaining detection data corresponding to the detection dimensions;
s23, generating a quality data report according to the detection dimension and the detection data;
after an automatic test task is triggered, detecting the code to be detected according to preset detection dimensionality, wherein the detection dimensionality comprises programming specification, potential defects, annotations, repeated codes, complexity, test coverage and architecture design.
Specifically, the detection method and process are different according to different detection dimensions, for example, when the detection dimensions are programming specifications, the codes to be detected are scanned one by one, class names, function names, variable names and code formats in the codes to be detected are matched based on regular expression usage, so as to perform programming specification detection, and codes which do not meet the specification requirements are pointed out, and the detection results are quantized to the programming specification dimensions. For another example, when the detection dimension is a potential defect, the code to be detected is detected through static code examination, potential bugs, errors and unsafe factors of the code are detected, the bugs, the errors and the unsafe factors comprise repeated definition of variables, array boundary crossing, unreleased memory, possible null pointers or wild pointers, and file operation are not equal, the defect is pointed out, and the detection result is quantized to the potential defect dimension.
In some embodiments, the automated testing task further includes unit testing and integration testing, wherein the unit testing is mainly used for verifying the interface, function, boundary, branch coverage, logic and the like of the program so as to ensure the correctness of the program and the function. The integration test is to test the code running unit after the submission of a plurality of project tasks, check whether the mutual call among the modules conflicts, whether the call relation is correct, and the like.
In the present application, code quality is analyzed and managed by a SonarQube quality management platform and quality data reports are generated. And importing the detection rules corresponding to the detection dimensions into a rule base of a code quality management platform Sonar Qube, judging the quality of the code to be detected by adopting an API (application programming interface) of Sonar, automatically creating a manual code review CodeReview if no alarm or error exists after the judgment of a quality threshold, otherwise, feeding the alarm or error back to a preset responsible person, and generating a quality data report.
Specifically, a Sonar scanning analysis tool is adopted to push code dimension data to a sonarQube quality management platform, a code high-level and problem list is generated through quality rule and quality threshold judgment of the sonarQube platform, the quality of the code is analyzed from a plurality of angles such as reliability, safety and maintainability, and a quality data report is generated.
And S24, drawing a dimension data chart according to the quality data report and in combination with the defect management report.
The defect management report is generated by a tester according to historical data of defect types, the tester detects software defects in the testing process, inputs the software defects into a defect management platform for management so as to track and manage the defects found in each testing stage, collects defect data and performs data analysis, and divides corresponding defect types according to attributes defined by the defects.
In the present application, a Tapd defect management platform is employed to manage the historical defect types and generate a defect management report based on the historical defect types.
Referring to fig. 2b, step S24 includes:
s241, generating the defect management report;
specifically, a plurality of defect types are preset, wherein the defect types comprise functional defects, UI defects, demand defects, design defects, security holes and performance problems; performing label classification on the defect types, wherein the label classification comprises human and item classes; and generating the defect management report by combining the label classification according to the defect type.
S242, extracting the detection dimension and the detection data in the quality data report, associating the detection dimension with the defect type, and drawing the dimension data chart.
Specifically, each item of detection data corresponds to one detection dimension, the quality data report is traversed, the detection dimension is extracted, each detection dimension corresponds to one detection data, and the detection dimension is associated with the defect type. For example, when the detection dimension is a programming specification, the defect type associated therewith is a functional defect and/or a UI defect, and when the detection dimension is a potential defect, the defect type associated therewith is a design defect and/or a security vulnerability. And drawing a dimension data chart according to the incidence relation between the detection dimension and the defect type.
Preferably, the dimension data chart is any one of a pie chart, a line chart and a radar chart.
And further combining the label classification of the defect types on the basis of the quality data report, wherein the label classification comprises individual human and item classes, the defect types of the individual classes are combined with the code quality data report to draw a dimension data chart of the individual, and the personal code style and the common error types can be led out through the dimension data chart. And combining the defect type of the project class with the code quality data report to draw a dimension data chart of the project, and deducing the possible defects or potential problems of the project in the future through the dimension data chart so as to further improve the quality of the project.
In the embodiment, a code to be detected is acquired according to a preset trigger condition; presetting detection dimensions, and detecting the codes to be detected to obtain detection data corresponding to the detection dimensions; generating a quality data report according to the detection dimension and the detection data; and drawing a dimension data chart by combining the defect management report according to the quality data report, thereby combining the code quality report with the defect type, showing the quality and defect problems of the code in the form of the dimension data chart, improving the accuracy of code quality detection and realizing the imaging of the detection result.
In some embodiments, the Bug condition in the code to be detected is predicted according to the dimension data chart. Specifically, the defect proportion of the individual human or project class is obtained according to the analysis of the dimension data chart.
As another aspect of the embodiments of the present invention, an embodiment of the present invention provides a server. Referring to fig. 3, the server 30 includes: one or more processors 31 and memory 32. In fig. 3, one processor 31 is taken as an example.
The processor 31 and the memory 32 may be connected by a bus or other means, such as the bus connection in fig. 3.
The memory 32, which is a non-volatile computer-readable storage medium, may be used for storing non-volatile software programs, non-volatile computer-executable programs, and modules, such as program instructions/modules corresponding to the analysis method of the code quality and the defect in the embodiment of the present invention. The processor 31 executes the analysis method of the code quality and the defect of the above-described respective embodiments by executing the nonvolatile software program, the instructions, and the modules stored in the memory 32.
The memory 32 may include high speed random access memory and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some embodiments, the memory 32 may optionally include memory located remotely from the processor 31, and these remote memories may be connected to the processor 31 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The program instructions/modules are stored in the memory 32 and, when executed by the one or more processors 31, perform the methods of analyzing code quality and defects of any of the method embodiments described above, e.g., to perform the methods of analyzing code quality and defects of the various embodiments described above.
The server of the embodiments of the present application exists in various forms, including but not limited to:
(1) tower server
The general tower server chassis is almost as large as the commonly used PC chassis, while the large tower chassis is much larger, and the overall dimension is not a fixed standard.
(2) Rack-mounted server
Rack-mounted servers are a type of server that has a standard width of 19 inch racks, with a height of from 1U to several U, due to the dense deployment of the enterprise. Placing servers on racks not only facilitates routine maintenance and management, but also may avoid unexpected failures. First, placing the server does not take up too much space. The rack servers are arranged in the rack in order, and no space is wasted. Secondly, the connecting wires and the like can be neatly stored in the rack. The power line, the LAN line and the like can be distributed in the cabinet, so that the connection lines accumulated on the ground can be reduced, and the accidents such as the electric wire kicking off by feet can be prevented. The specified dimensions are the width (48.26cm ═ 19 inches) and height (multiples of 4.445 cm) of the server. Because of its 19 inch width, a rack that meets this specification is sometimes referred to as a "19 inch rack".
(3) Blade server
A blade server is a HAHD (High Availability High Density) low cost server platform designed specifically for the application specific industry and High Density computer environment, where each "blade" is actually a system motherboard, similar to an individual server. In this mode, each motherboard runs its own system, serving a designated group of different users, without any relationship to each other. Although system software may be used to group these motherboards into a server cluster. In the cluster mode, all motherboards can be connected to provide a high-speed network environment, and resources can be shared to serve the same user group.
(4) Cloud server
The cloud server (ECS) is a computing Service with simplicity, high efficiency, safety, reliability, and flexible processing capability. The management mode is simpler and more efficient than that of a physical server, and a user can quickly create or release any plurality of cloud servers without purchasing hardware in advance. The distributed storage of the cloud server is used for integrating a large number of servers into a super computer, and a large number of data storage and processing services are provided. The distributed file system and the distributed database allow access to common storage resources, and IO sharing of application data files is achieved. The virtual machine can break through the limitation of a single physical machine, dynamically adjust and allocate resources to eliminate single-point faults of the server and the storage equipment, and realize high availability.
Embodiments of the present invention also provide a non-transitory computer-readable storage medium storing computer-executable instructions for causing a server to perform the method for analyzing code quality and defects as described in any one of the above.
An embodiment of the present invention provides a computer program product comprising a computer program stored on a non-volatile computer readable storage medium, the computer program comprising program instructions which, when executed by a server, cause the server to perform any one of the methods for analyzing code quality and defects.
The above-described embodiments of the apparatus or device are merely illustrative, wherein the unit modules described as separate parts may or may not be physically separate, and the parts displayed as module units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network module units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a general hardware platform, and certainly can also be implemented by hardware. Based on such understanding, the above technical solutions substantially or contributing to the related art may be embodied in the form of a software product, which may be stored in a computer-readable storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method according to the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; within the idea of the invention, also technical features in the above embodiments or in different embodiments may be combined, steps may be implemented in any order, and there are many other variations of the different aspects of the invention as described above, which are not provided in detail for the sake of brevity; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present application.
Claims (10)
1. A method for analyzing code quality and defects, comprising:
acquiring a code to be detected according to a preset trigger condition;
presetting detection dimensions, and detecting the codes to be detected to obtain detection data corresponding to the detection dimensions;
generating a quality data report according to the detection dimension and the detection data;
and drawing a dimension data chart according to the quality data report and in combination with the defect management report.
2. The method of claim 1, wherein the obtaining the code to be detected comprises:
compiling the code to be detected;
or combining the codes to be detected.
3. The method of claim 1, wherein said charting dimensional data in conjunction with a defect management report based on said quality data report comprises:
generating the defect management report;
and extracting the detection dimension and the detection data in the quality data report, associating the detection dimension with the defect type in the defect management report, and drawing the dimension data chart.
4. The method of claim 3, wherein the generating the defect management report comprises:
presetting a plurality of defect types, wherein the defect types comprise a function defect, a UI defect, a demand defect, a design defect, a security vulnerability and a performance problem;
performing label classification on the defect types, wherein the label classification comprises human and item classes;
and generating the defect management report by combining the label classification according to the defect type.
5. The method of claim 3, wherein said plotting a dimensional data graph in conjunction with a defect management report based on said quality data report further comprises:
and predicting the Bug condition in the code to be detected according to the dimension data chart.
6. The method of claim 5, wherein the dimension data chart is any one of a pie chart, a line chart, and a radar chart.
7. The method according to claim 1, wherein the triggering condition includes that a system reaches a preset triggering time or a system state meets a preset triggering event state.
8. The method of claim 1, wherein the inspection dimensions comprise programming specifications, potential defects, annotations, repetition code, complexity, test coverage, and architectural design.
9. A server, characterized in that the server comprises:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the memory stores a program of instructions executable by the at least one processor to cause the at least one processor to perform the method of analyzing code quality and defects of any of claims 1-8.
10. A non-transitory computer storage medium storing computer-executable instructions for execution by one or more processors to cause the one or more processors to perform a method of analyzing code quality and defects according to any one of claims 1 to 8.
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| PCT/CN2019/120350 WO2021097824A1 (en) | 2019-11-22 | 2019-11-22 | Code quality and defect analysis method, server and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111837109A true CN111837109A (en) | 2020-10-27 |
Family
ID=72912313
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201980010312.1A Pending CN111837109A (en) | 2019-11-22 | 2019-11-22 | Code quality and defect analysis method, server and storage medium |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN111837109A (en) |
| WO (1) | WO2021097824A1 (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112632546A (en) * | 2020-12-31 | 2021-04-09 | 华数传媒网络有限公司 | Automatic code analysis method for broadcasting and television industry |
| CN112799935A (en) * | 2020-12-10 | 2021-05-14 | 前海飞算科技(深圳)有限公司 | Intelligent quality detection method, device and platform |
| CN113778852A (en) * | 2021-06-04 | 2021-12-10 | 南方科技大学 | Code analysis method based on regular expression |
| CN116302043A (en) * | 2023-05-25 | 2023-06-23 | 深圳市明源云科技有限公司 | Code maintenance problem detection method and device, electronic equipment and readable storage medium |
| CN116611074A (en) * | 2023-07-17 | 2023-08-18 | 北京奇虎科技有限公司 | Security information auditing method, device, storage medium and apparatus |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102231134A (en) * | 2011-07-29 | 2011-11-02 | 哈尔滨工业大学 | Method for detecting redundant code defects based on static analysis |
| CN107045477A (en) * | 2016-12-30 | 2017-08-15 | 上海富聪金融信息服务有限公司 | A kind of quality evaluation platform for carrying out various dimensions detection |
| CN107122298A (en) * | 2017-04-21 | 2017-09-01 | 千寻位置网络有限公司 | The methods of exhibiting of visual software development process quality |
| CN107341101A (en) * | 2017-06-01 | 2017-11-10 | 西南电子技术研究所（中国电子科技集团公司第十研究所） | The method for measuring FPGA software rest mass |
| CN108829576A (en) * | 2018-04-14 | 2018-11-16 | 温州职业技术学院 | A kind of software defect detection method and its detection system |
| CN109726123A (en) * | 2018-12-20 | 2019-05-07 | 北京每日优鲜电子商务有限公司 | The detection system and detection method of code quality |
| CN109815220A (en) * | 2018-12-14 | 2019-05-28 | 深圳壹账通智能科技有限公司 | A kind of defective data treating method and apparatus |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8166451B2 (en) * | 2006-12-18 | 2012-04-24 | International Business Machines Corporation | Simultaneous static analysis on disparate resource types |
-
2019
- 2019-11-22 CN CN201980010312.1A patent/CN111837109A/en active Pending
- 2019-11-22 WO PCT/CN2019/120350 patent/WO2021097824A1/en active Application Filing
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102231134A (en) * | 2011-07-29 | 2011-11-02 | 哈尔滨工业大学 | Method for detecting redundant code defects based on static analysis |
| CN107045477A (en) * | 2016-12-30 | 2017-08-15 | 上海富聪金融信息服务有限公司 | A kind of quality evaluation platform for carrying out various dimensions detection |
| CN107122298A (en) * | 2017-04-21 | 2017-09-01 | 千寻位置网络有限公司 | The methods of exhibiting of visual software development process quality |
| CN107341101A (en) * | 2017-06-01 | 2017-11-10 | 西南电子技术研究所（中国电子科技集团公司第十研究所） | The method for measuring FPGA software rest mass |
| CN108829576A (en) * | 2018-04-14 | 2018-11-16 | 温州职业技术学院 | A kind of software defect detection method and its detection system |
| CN109815220A (en) * | 2018-12-14 | 2019-05-28 | 深圳壹账通智能科技有限公司 | A kind of defective data treating method and apparatus |
| CN109726123A (en) * | 2018-12-20 | 2019-05-07 | 北京每日优鲜电子商务有限公司 | The detection system and detection method of code quality |
Non-Patent Citations (1)
| Title |
|---|
| ZZUQIANG: "SonarQube代码质量检查工具", 《HTTPS://MY.OSCHINA.NET/ZZUQIANG/BLOG/843406》 * |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112799935A (en) * | 2020-12-10 | 2021-05-14 | 前海飞算科技(深圳)有限公司 | Intelligent quality detection method, device and platform |
| CN112632546A (en) * | 2020-12-31 | 2021-04-09 | 华数传媒网络有限公司 | Automatic code analysis method for broadcasting and television industry |
| CN113778852A (en) * | 2021-06-04 | 2021-12-10 | 南方科技大学 | Code analysis method based on regular expression |
| CN113778852B (en) * | 2021-06-04 | 2023-07-28 | 南方科技大学 | Code analysis method based on regular expression |
| CN116302043A (en) * | 2023-05-25 | 2023-06-23 | 深圳市明源云科技有限公司 | Code maintenance problem detection method and device, electronic equipment and readable storage medium |
| CN116302043B (en) * | 2023-05-25 | 2023-10-10 | 深圳市明源云科技有限公司 | Code maintenance problem detection method and device, electronic equipment and readable storage medium |
| CN116611074A (en) * | 2023-07-17 | 2023-08-18 | 北京奇虎科技有限公司 | Security information auditing method, device, storage medium and apparatus |
Also Published As
| Publication number | Publication date |
|---|---|
| WO2021097824A1 (en) | 2021-05-27 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111837109A (en) | Code quality and defect analysis method, server and storage medium | |
| CN107341098B (en) | Software performance testing method, platform, equipment and storage medium | |
| US10289535B2 (en) | Software testing integration | |
| US9824002B2 (en) | Tracking of code base and defect diagnostic coupling with automated triage | |
| US7895565B1 (en) | Integrated system and method for validating the functionality and performance of software applications | |
| CN111414172B (en) | Automatic deployment and release system and method for application software | |
| US20180285247A1 (en) | Systems, methods, and apparatus for automated code testing | |
| US8954930B2 (en) | System and method for reducing test effort by object risk analysis | |
| CN105094783B (en) | method and device for testing stability of android application | |
| EP2572294B1 (en) | System and method for sql performance assurance services | |
| US7512933B1 (en) | Method and system for associating logs and traces to test cases | |
| CN108763091B (en) | Method, device and system for regression testing | |
| CN108984179B (en) | Linux compiling processing method and device | |
| US20140289697A1 (en) | Systems and Methods for Software Development | |
| CN110851471A (en) | Distributed log data processing method, device and system | |
| CN110188049A (en) | Container Management method, apparatus and electronic equipment | |
| CN112817853A (en) | Automatic test method, system and electronic equipment | |
| CN115344300A (en) | Branch pipeline construction method, device, equipment and storage medium | |
| CN116194894A (en) | Fault localization of native cloud applications | |
| CN108595323A (en) | A kind of system detection method and relevant apparatus | |
| US9354962B1 (en) | Memory dump file collection and analysis using analysis server and cloud knowledge base | |
| CN113609015A (en) | Automatic test framework based on Bash Shell | |
| CN114116471A (en) | Automatic code scanning method, system, electronic equipment and storage medium | |
| CN112416799A (en) | Code quality early warning method and device, electronic equipment and storage medium | |
| CN110908918A (en) | Unit testing method and device for multiple interdependent node |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20201027 |
|
| RJ01 | Rejection of invention patent application after publication |