CN112363911A - Software test defect analysis method and device - Google Patents
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- 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
- G06F11/3604—Software analysis for verifying properties of programs
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- 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
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- 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
The embodiment of the invention provides a software test defect analysis method and a device, wherein the method comprises the following steps: taking the target attribute as a unit, carrying out classification statistics on the structured defect list data to obtain a classification statistical result; and performing correlation analysis on the structured defect list data and the classification statistical result to obtain a correlation analysis result. The software test defect analysis method and device provided by the embodiment of the invention can obtain the defect distribution and defect state results, comprehensively analyze the defects from more dimensions, discover potential correlation among the defects, discover problems in software test, improve the test working efficiency and improve the working efficiency of a software development process.
Description
Technical Field
The invention relates to the technical field of computer software, in particular to a software test defect analysis method and device.
Background
With the continuous acceleration of software development, version iteration and function updating are more and more frequent, and the rhythm is accelerated by software testing work. The software test mainly finds whether the software has defects or not according to a certain method and logic, and obtains the related information of the existing defects so as to realize the evaluation of the software quality and the development process from the test angle.
For software testing work, in addition to the task of executing test items, analysis of test defects is also very important. At present, analysis according to software test results is basically simple statistical analysis aiming at one or more indexes, and problems existing in software tests are difficult to find, so that the test working efficiency and the software development process working efficiency are low.
Disclosure of Invention
The embodiment of the invention provides a software test defect analysis method and device, which are used for solving the defect that the problems of software test are difficult to find in the prior art and improving the working efficiency of the software test.
The embodiment of the invention provides a software test defect analysis method, which comprises the following steps:
taking the target attribute as a unit, carrying out classification statistics on the structured defect list data to obtain a classification statistical result;
and performing correlation analysis on the structured defect list data and the classification statistical result to obtain a correlation analysis result.
According to the software test defect analysis method provided by the embodiment of the invention, the correlation analysis result comprises a defect state and recurrence analysis result and/or a tester defect record and follow-up analysis result.
According to the software test defect analysis method of one embodiment of the invention, the target attribute comprises at least one of an item, a presenter, a responsible person and a defect grade.
According to an embodiment of the present invention, before performing classification statistics on structured defect list data by using a target attribute as a unit and obtaining a classification statistical result, the method further includes:
and preprocessing a defect collection table to obtain the structured defect list data.
According to the software test defect analysis method of one embodiment of the invention, the defect state and recurrence analysis result comprises:
an overall repair rate, recurrence rate and overdue rate of the defect, at least one of a failure rate and a defect recurrence rate of each item and modules included in the item.
According to the software test defect analysis method provided by the embodiment of the invention, the tester defect record and follow-up analysis result comprises at least one of the total defect level, recurrence rate and overdue rate corresponding to each responsible person.
According to the software test defect analysis method of an embodiment of the present invention, before preprocessing the defect collection table and acquiring the structured defect list data, the method further includes:
and acquiring and verifying original software test data to generate the defect collection table.
The embodiment of the invention also provides a software test defect analysis device, which comprises: the classification analysis module is used for performing classification statistics on the structured defect list data by taking the target attribute as a unit to obtain a classification statistical result;
and the collision analysis module is used for performing correlation analysis on the structured defect list data and the classification statistical result to obtain a correlation analysis result.
The embodiment of the present invention further provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the processor implements any of the steps of the software test defect analysis method described above when executing the program.
Embodiments of the present invention further provide a non-transitory computer readable storage medium, on which a computer program is stored, and the computer program, when executed by a processor, implements the steps of the software test defect analysis method according to any one of the above.
According to the software test defect analysis method and device provided by the embodiment of the invention, the specific value of each target attribute is taken as a root node, the number of the defects with different attributes in the structured defect list data is obtained through classification statistics, the classification statistical result is obtained, the structured defect category data and the classification statistical result are subjected to correlation analysis, the correlation analysis result is obtained, the correlation existing among the defects can be obtained, the defect distribution and defect state results can be obtained, the defects can be comprehensively analyzed from more dimensions, the potential correlation among the defects can be found, the problems existing in the software test can be found, the test working efficiency can be improved, and the working efficiency of the software development process can be improved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart of a software test defect analysis method according to an embodiment of the present invention;
FIG. 2 is a schematic structural diagram of a software testing defect analysis apparatus according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, 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 with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the embodiments of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience in describing the embodiments of the present invention and simplifying the description, but do not indicate or imply that the referred devices or elements must have specific orientations, be configured in specific orientations, and operate, and thus, should not be construed as limiting the embodiments of the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the embodiments of the present invention, it should be noted that, unless explicitly stated or limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. Specific meanings of the above terms in the embodiments of the present invention can be understood in specific cases by those of ordinary skill in the art.
In order to overcome the above problems in the prior art, embodiments of the present invention provide a method and an apparatus for analyzing software test defects, and the inventive concept is to count the number of defects in defect indexes by classification from different dimensions, find correlations existing among the defects, find problems existing in software tests according to the correlations among the defects, comprehensively analyze the defects from more dimensions, obtain potential correlations among the defects, find problems existing in software tests according to the correlations among the defects, and improve the working efficiency of the tests and the working efficiency of software development processes.
Fig. 1 is a schematic flow chart of a software test defect analysis method according to an embodiment of the present invention. The software test defect analysis method according to the embodiment of the present invention is described below with reference to fig. 1. As shown in fig. 1, the method includes: and S101, carrying out classification statistics on the structured defect list data by taking the target attribute as a unit to obtain a classification statistical result.
Specifically, the structured defect list logically expresses the defect data by a two-dimensional table structure. The structured defect list data contains one or more attributes.
The attributes may include attributes describing software and attributes describing software defects.
The attributes describing the software are used to describe the items of software that are tested for defects, such as: item names, module names, function names, and the like.
Attributes describing software defects are used to describe the nature of defects discovered by software tests, such as: defect type, defect name, defect grade, presentation time, number of recurrences, recurrence status, and presenter, responsible person, etc.
Each piece of data in the defect list describes a defect by the attributes described above.
One or more of the above attributes may be selected as target attributes, each of which is taken as a dimension or unit of analyzing the defect.
And for any target attribute, taking the target attribute as a unit, and acquiring all values of the target attribute. Each value can be respectively used as a root node, and the structured defect list data is classified and counted according to the root node, so that the number of defects corresponding to each value of each other attribute with the target attribute as the value is obtained.
For example: the number of defects in the module A contained in a certain item is M; the number of defects in which the defect state is Open in a certain entry is N.
And classifying and counting the structured defect list data by taking each target attribute as a unit, so as to obtain the number of the defects corresponding to each value of each other attribute of each value of each target attribute as a classified and counted result.
After the classification statistical result is obtained, comprehensive analysis of the defects can be performed according to the classification statistical result, and defect distribution and defect state results corresponding to the root node are established.
After the classification statistical result is obtained, different data query interfaces can be created according to the root node, and data query and call are provided.
And S102, performing correlation analysis on the structured defect list data and the classification statistical result to obtain a correlation analysis result.
Specifically, the structured defect list data and the classification statistical result may be subjected to association analysis in a manner of colliding the structured defect list data with the classification statistical result, so as to obtain an association relationship between the defects as an association analysis result.
The incidence relation among a plurality of defects comprises that the defects have the same attributes.
The correlation information obtained by the correlation analysis may include, but is not limited to, the following:
1. according to the defect analysis requirement, the defect numbers of the same child nodes in the plurality of root nodes can be respectively obtained, for example: the number of defects with Open states in item a is X, and the number of defects with Open states in item b is Y, where: x is more than Y;
2. according to the defect analysis requirement, the defect number corresponding to some sub-nodes in a certain node can be obtained. For example: the defect number of the item A with the defect state of Open and the defect type of C-Checking is Q;
3. according to the defect analysis requirements, the defect number corresponding to some sub-nodes in different root nodes can be obtained. For example: in the item A and the item B, the total defect number is R, wherein the defect state is Open and the defect type is C-Checking.
And obtaining a correlation analysis result according to the correlation information between the target attribute and the defect obtained by the correlation analysis. After the correlation analysis result is obtained, the defect generation reason, the defect searching method, the defect repairing method, the problem finding method in the software test and the like can be comprehensively analyzed from more dimensions according to the defect analysis requirement.
The software test defect analysis method provided by the embodiment of the invention can realize real-time query and display of structured defect list data, root node classification statistical results and correlation analysis results through real-time data query.
The software test defect analysis method of the embodiment of the invention can also perform correlation analysis on the structured defect list data and the classification statistical result at a fixed time through a timing task to obtain the condition that the software tests the defects in a fixed period. Further, the variation trend of the condition of the defect of the software test along with the period variation can be obtained.
The correlation analysis result, the classification statistical result and the structured defect list data can be structurally stored. The defect data or the defect distribution condition in the whole project line can provide multi-dimensional display in a list or an image mode.
The software test defect analysis method of the embodiment of the invention can also provide an index function, realize the real-time query of the related data and support the editing of the data entry format.
The embodiment of the invention takes the specific value of each target attribute as a root node, obtains the number of the defects with different attributes in the classification statistical structured defect list data, obtains the classification statistical result, performs the correlation analysis on the structured defect category data and the classification statistical result, obtains the correlation analysis result, can obtain the correlation between the defects, can obtain the defect distribution and defect state result, can comprehensively analyze the defects from more dimensions, can discover the potential correlation between the defects, can discover the problems existing in the software test, can improve the working efficiency of the test, and can improve the working efficiency of the software development process.
Based on the above embodiments, the correlation analysis result includes the defect status and the recurrence analysis result and/or the defect record and follow-up analysis result of the tester.
In particular, the defect status and the recurrence analysis results may be used to describe the defect's own attributes.
The defect state and recurrence analysis results may include defect state analysis results and defect recurrence analysis results.
The defect status may be used to describe the status of the defect in the item of software.
And by combining the defect analysis requirements, the regularity characteristics of each defect attribute can be obtained according to the defect state analysis result. According to the regularity characteristics of each defect attribute, corresponding processing can be carried out, and further software development processes can be carried out.
Distribution and occurrence frequency of different types of defects can be obtained through defect recurrence analysis results, and regularity characteristics of defect release and occurrence can be found out. According to the defect release and occurrence regularity characteristics, the defects with the same attribute can be found more quickly and solved in batches, and therefore the software testing efficiency is improved.
The tester defect record and follow-up analysis results can be used for describing the relationship between each responsible person and/or the proposing person and the defect and the state of defect processing.
In particular, the number of defects that each responsible person appears and/or that a person is proposed to find contains a certain characteristic can be obtained from the test person defect record. Further, the regularity characteristics between the responsible person or the proposed person and the defect can be obtained according to the number of defects of certain attributes which appear and/or are discovered by each responsible person. According to the regularity characteristics between the responsible person or the proposed person and the defect, corresponding treatment can be carried out.
Through analyzing the relationship between each responsible person and the defect processing state through the defect follow-up analysis result of the tester, the regularity characteristics of each responsible person in the defect processing can be obtained, corresponding processing is carried out, and the defect processing efficiency is improved.
The defect state and the reappearance analysis result are combined with the defect record and follow-up analysis result of the tester, and the analysis results such as the relationship between each responsible person and different defect attributes or the processing efficiency of the defects with different attributes can be comprehensively analyzed and obtained.
The correlation analysis result obtained by the embodiment of the invention comprises the defect state and the recurrence analysis result and/or the defect record and follow-up analysis result of the tester, and the potential correlation between the defects can be found through the defect state and the recurrence analysis result and/or the defect record and follow-up analysis result of the tester, so that the tester can be helped to better test and repair the defects, the test working efficiency can be improved, and the working efficiency of the software development process can be improved.
Based on the contents of the above embodiments, the target attribute includes at least one of an item, a presenter, a responsible person, and a defect level.
The item, the presenter, the responsible person, or the defect level may be used as a unit for performing classification statistics on the structured defect list data.
Specific values exist for the project, the presenter, the responsible person and the defect level, for example, the project A, the project B, the responsible person A and the defect level are Minor.
For example: selecting a project as a target attribute, carrying out classification statistics on defects in the same project according to modules, functions, defect types, defect grades and defect states, respectively obtaining the defect number of the indexes, establishing a data query interface of the project, and providing data query and call;
selecting a presenter as a target attribute, classifying and counting defects presented by the same presenter according to items, modules, functions, presentation time, defect types, defect levels and defect states, respectively acquiring the defect number of the indexes, creating a presenter data query interface, and providing data query and call;
selecting responsible persons as target attributes, classifying and counting the defects corresponding to the same responsible person according to items, modules, functions, presentation time, solution time, defect types, defect grades, defect states, recurrence states and recurrence times, respectively obtaining the defect number of the indexes, establishing a responsible person data query interface, and providing data query and call;
selecting a defect grade as a target attribute, classifying and counting the defects of the same defect grade according to items, modules, functions, a presenter, a responsible person, presentation time, solution time, defect types, defect states, recurrence states and recurrence times, respectively obtaining the defect number of the indexes, establishing a defect grade data query interface, and providing data query and call.
In the embodiment of the invention, at least one of the project, the presenter, the responsible person and the defect grade is selected as the target attribute, so that the structured defect list data can be classified and counted by taking the target attribute as a unit, the structured defect list data and the classification and counting result are subjected to correlation analysis to obtain a correlation analysis result, further the defects can be comprehensively analyzed from different dimensions, the defect distribution and defect state results in the dimension can be constructed, and the test working efficiency and the software development flow working efficiency can be improved.
Based on the content of the foregoing embodiments, before performing classification statistics on the structured defect list data by using the target attribute as a unit and obtaining a classification statistical result, the method further includes: and preprocessing the defect collection table to obtain structured defect list data.
The fields in the defect collection table may include a plurality of item names, module names, function names, defect types, defect levels, defect descriptions, presenters, stakeholders, presentation times, defect states, recurrence states, number of recurrence times, and the like.
And preprocessing the defect collection table, including structuring all fields to obtain structured defect list data.
According to the embodiment of the invention, all fields in the defect collection table are subjected to structuring processing to obtain the structured defect list data, so that data support can be provided for defect analysis work.
Based on the above embodiments, the defect status and recurrence analysis results include: an overall repair rate, recurrence rate and overdue rate of the defect, at least one of a failure rate and a defect recurrence rate of each item and each module included in the item.
And the total defect repair rate is used for describing the whole defect repair condition of each software project.
The total defect repair rate may be obtained according to the number of defects accumulated and closed and the number of defects accumulated and found, and specifically may be calculated by dividing the number of defects accumulated and closed by the number of defects accumulated and found.
Recurrence rate, which is used to describe the frequency of repeated occurrences of the same defect. For defects with high recurrence rate, the process of discovering the defects can be analyzed, the reasons of the defects can be further analyzed, and the defects can be rapidly checked by using the same method.
And the total defect recurrence rate is used for describing the defect recurrence condition of the whole software project.
The overdue rate, which is used to describe the progress of repairing the defect, represents the proportion of defects that are not closed within a specified time. A targeted solution can be provided by analyzing the reason of high overdue rate.
And the failure rate of each item is used for describing the condition that the item is tested to be defective in the whole process.
The entry includes the failure rate of each module to describe the condition that the module has tested a defect.
According to the failure rate of each project and the failure rate of each module included in the project, a targeted solution can be provided for the projects and/or modules with higher failure rates.
For example: if the failure rate of the item a is 18%, the failure rate of the item B is 10%, the failure rate of the module a in the item a is 20%, and the failure rate of the module B in the item a is 8%, the problem of defects should be focused on the item a and the module a in the item a as compared with the item B.
The defect recurrence rate of each item is used to describe the frequency of the defect recurrence of the item as a whole.
The entry includes a defect recurrence rate for each module, which describes the frequency of repeated occurrences of defects in the modules.
According to the recurrence rate of each project and the recurrence rates of the modules included in the project, the defect of high recurrence rate can be obtained. By analyzing the method for finding the defects, the reasons of the defects appearing in the projects and/or modules can be further analyzed, the defects can be checked in the projects and/or modules with high recurrence rate according to the method for finding the defects, and the responsible persons of the defects can be reminded.
For example: if the recurrence rate of the defect with the defect type of C-Checking in the item A is 50%, and the recurrence rate of the defect with the defect type of C-Checking in the module A contained in the item A is 80%, the defect with the defect type of C-Checking can be checked again in the item A and/or the module A contained in the item A according to the process of finding the defect.
After the defect state and the recurrence analysis result are obtained, a data query interface can be established to provide query and call of related data.
The embodiment of the invention can describe the attribute of the defect by taking the total repair rate, recurrence rate and overdue rate of the defect and at least one of the fault rate and the defect recurrence rate of each module included in each project and each project as the defect state and recurrence analysis result, can comprehensively analyze the reasons of the defect generation and specifically eliminate and avoid the defect, can obtain projects and/or modules with more defects, can obtain the progress of defect repair, can make corresponding treatment according to the defect analysis result, can improve the working efficiency of the test, and can improve the working efficiency of the software development process.
Based on the above embodiments, the defect record and follow-up analysis result of the tester includes at least one of the total defect level, recurrence rate and overdue rate corresponding to each responsible person.
The overall defect level corresponding to each responsible person can be used to describe the severity of the defect occurring in the responsible person.
By analyzing the severity of the defects found by each responsible person, an improvement opinion can be made for responsible persons with higher defect levels.
The recurrence rate corresponding to each responsible person can be used to describe the recurrence frequency of the defect occurring in the responsible person.
The reason why the responsible person has the defect can be analyzed and corresponding improvements can be made according to the process of finding the defect.
The overdue rate corresponding to each responsible person can be used for describing the progress of the responsible person in repairing the defect. By analyzing the overdue rate corresponding to each responsible person, an improvement suggestion can be made for the responsible person with higher overdue rate.
And after the defect records of the testers are obtained and the analysis results are obtained, a data query interface can be established to provide query and call of related data.
The embodiment of the invention takes at least one of the total defect level, recurrence rate and overdue rate corresponding to each responsible person as the defect record and follow-up analysis result of the tester, can acquire the severity of the defect of each responsible person according to the defect record and follow-up analysis result of the tester, can acquire the defect frequently occurring in each responsible person, and can acquire the progress of repairing the defect of each responsible person, thereby helping each responsible person to specifically eliminate and avoid the defect, helping the tester to better analyze and repair the defect, improving the working efficiency of the test, and improving the working efficiency of the software development process.
Based on the content of the foregoing embodiments, before preprocessing the defect collection table and acquiring the structured defect list data, the method further includes: and acquiring and verifying original software test data to generate a defect collection table.
Original software test data is collected and summarized, the original software test data is verified, bad data is removed, and a defect collection table can be generated.
Raw software test data, including various attributes of each defect discovered by the software test data.
The verification can ensure the integrity and accuracy of the data in the generated defect collection table.
The specific method of the verification may be determined according to actual conditions, and is not particularly limited in the embodiment of the present invention.
According to the embodiment of the invention, the original software test data is collected and summarized, the original software test data is verified, the defect collection table is generated, bad data in the original software test data can be eliminated, the integrity and the accuracy of the data in the generated defect collection table can be ensured, and data support can be provided for defect analysis work.
The software testing defect analysis apparatus provided by the embodiment of the invention is described below, and the software testing defect analysis apparatus described below and the software testing defect analysis method described above can be referred to correspondingly.
Fig. 2 is a schematic structural diagram of a software test defect analysis apparatus according to an embodiment of the present invention. Based on the content of the above embodiments, as shown in fig. 2, the apparatus includes a classification analysis module 201 and a collision analysis module 202, wherein:
and the classification analysis module 201 is configured to perform classification statistics on the structured defect list data by taking the target attribute as a unit, and obtain a classification statistical result.
And the collision analysis module 202 is configured to perform correlation analysis on the structured defect list data and the classification statistical result to obtain a correlation analysis result.
Specifically, the classification analysis module 201 is electrically connected to the collision analysis module 202.
The classification analysis module 201 may obtain all values of any target attribute by taking the target attribute as a unit. Each value can be respectively used as a root node; classifying and counting the structured defect list data according to the root node to obtain the number of the defects corresponding to each value of each other attribute of which the target attribute is the value; and acquiring the number of the defects corresponding to each value of each other attribute of each value of each target attribute as a classification statistical result.
The collision analysis module 202 may perform association analysis on the structured defect list data and the classification statistical result by means of collision between the structured defect list data and the classification statistical result, and obtain an association relationship between the defects as an association analysis result.
The software test defect analysis device may further include: and the data preprocessing module is used for preprocessing the defect collection table to obtain structured defect list data.
The software test defect analysis apparatus may further include: and the data acquisition module is used for acquiring the original software test data, verifying the original software test data and generating a defect collection table.
The software test defect analysis apparatus provided in the embodiments of the present invention is configured to execute the software test defect analysis method provided in each of the embodiments of the present invention, and specific methods and processes for implementing corresponding functions by each module included in the software test defect analysis apparatus are described in the embodiments of the software test defect analysis method, and are not described herein again.
The software test defect analysis device is used for the software test defect analysis method of each embodiment. Therefore, the description and definition in the software test defect analysis method in the foregoing embodiments can be used for understanding the execution modules in the embodiments of the present invention.
The embodiment of the invention takes the specific value of each target attribute as a root node, obtains the number of the defects with different attributes in the classification statistical structured defect list data, obtains the classification statistical result, performs the correlation analysis on the structured defect category data and the classification statistical result, obtains the correlation analysis result, can obtain the correlation between the defects, can obtain the defect distribution and defect state result, can comprehensively analyze the defects from more dimensions, can discover the potential correlation between the defects, can discover the problems existing in the software test, can improve the working efficiency of the test, and can improve the working efficiency of the software development process.
Fig. 3 illustrates a physical structure diagram of an electronic device, which may include, as shown in fig. 3: a processor (processor)301, a memory (memory)302, and a bus 303; wherein, the processor 301 and the memory 302 complete the communication with each other through the bus 303; the processor 301 is configured to call computer program instructions stored in the memory 302 and executable on the processor 301 to perform the software test defect analysis method provided by the above-mentioned embodiments of the methods, the method includes: taking the target attribute as a unit, carrying out classification statistics on the structured defect list data to obtain a classification statistical result; and performing correlation analysis on the structured defect list data and the classification statistical result to obtain a correlation analysis result.
Furthermore, the logic instructions in the memory 302 may be implemented in software functional units and stored in a computer readable storage medium when sold or used as a stand-alone product. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
In another aspect, an embodiment of the present invention further provides a computer program product, where the computer program product includes a computer program stored on a non-transitory computer-readable storage medium, the computer program includes program instructions, and when the program instructions are executed by a computer, the computer can execute the software test defect analysis method provided by the above-mentioned method embodiments, where the method includes: taking the target attribute as a unit, carrying out classification statistics on the structured defect list data to obtain a classification statistical result; and performing correlation analysis on the structured defect list data and the classification statistical result to obtain a correlation analysis result.
In another aspect, an embodiment of the present invention further provides a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program is implemented by a processor to perform the software test defect analysis method provided in the foregoing embodiments, and the method includes: taking the target attribute as a unit, carrying out classification statistics on the structured defect list data to obtain a classification statistical result; and performing correlation analysis on the structured defect list data and the classification statistical result to obtain a correlation analysis result.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network 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. One of ordinary skill in the art can understand and implement it without inventive effort.
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 necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can 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 methods described in 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; 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 such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A software test defect analysis method is characterized by comprising the following steps:
taking the target attribute as a unit, carrying out classification statistics on the structured defect list data to obtain a classification statistical result;
and performing correlation analysis on the structured defect list data and the classification statistical result to obtain a correlation analysis result.
2. The software test defect analysis method of claim 1, wherein the correlation analysis result comprises a defect status and recurrence analysis result and/or a tester defect record and follow-up analysis result.
3. The software test defect analysis method of claim 1, wherein the target attributes comprise at least one of an item, a presenter, a responsible person, and a defect level.
4. The software testing defect analysis method of any one of claims 1 to 3, wherein before performing classification statistics on the structured defect list data by taking the target attribute as a unit and obtaining a classification statistical result, the method further comprises:
and preprocessing a defect collection table to obtain the structured defect list data.
5. The software test defect analysis method of claim 2, wherein the defect status and recurrence analysis results comprise:
an overall repair rate, recurrence rate and overdue rate of the defect, at least one of a failure rate and a defect recurrence rate of each item and modules included in the item.
6. The software testing defect analysis method of claim 2, wherein the testing personnel defect record and follow-up analysis result comprises at least one of an overall defect level, recurrence rate and overdue rate corresponding to each responsible person.
7. The method of claim 4, wherein before preprocessing the defect collection table and obtaining the structured defect list data, the method further comprises:
and acquiring and verifying original software test data to generate the defect collection table.
8. A software test defect analysis apparatus, comprising:
the classification analysis module is used for performing classification statistics on the structured defect list data by taking the target attribute as a unit to obtain a classification statistical result;
and the collision analysis module is used for performing correlation analysis on the structured defect list data and the classification statistical result to obtain a correlation analysis result.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the software test defect analysis method according to any of claims 1 to 7 are implemented when the processor executes the program.
10. A non-transitory computer readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the software test defect analysis method according to any one of claims 1 to 7.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111190817A (en) * | 2019-12-23 | 2020-05-22 | 平安医疗健康管理股份有限公司 | Method and device for processing software defects |
| CN113128981A (en) * | 2021-05-18 | 2021-07-16 | 中国农业银行股份有限公司 | Project management method and system |
| CN113297092A (en) * | 2021-06-21 | 2021-08-24 | 中国农业银行股份有限公司 | Defect prediction method of software and related equipment |
| CN113742227A (en) * | 2021-09-02 | 2021-12-03 | 上海浦东发展银行股份有限公司 | Method, device, equipment and medium for controlling software testing process |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080201612A1 (en) * | 2007-02-16 | 2008-08-21 | Kathryn Allyn Bassin | Defect Resolution Methodology and Data Defects Quality/Risk Metric Model Extension |
| US20140201573A1 (en) * | 2013-01-14 | 2014-07-17 | International Business Machines Corporation | Defect analysis system for error impact reduction |
| CN104899143A (en) * | 2015-06-15 | 2015-09-09 | 中国航空无线电电子研究所 | Software peer review system realizing device for providing DM (Data Mining) |
| CN107480065A (en) * | 2017-08-15 | 2017-12-15 | 郑州云海信息技术有限公司 | A kind of defect management method and equipment |
| CN107783890A (en) * | 2016-12-28 | 2018-03-09 | 平安科技（深圳）有限公司 | Software defect data processing method and device |
| CN107908550A (en) * | 2017-10-27 | 2018-04-13 | 链家网（北京）科技有限公司 | A kind of software defect statistical processing methods and device |
| CN108241574A (en) * | 2016-12-26 | 2018-07-03 | 航天信息股份有限公司 | A kind of method and system analyzed based on test and management tool QC software test defect |
| CN110109821A (en) * | 2019-03-19 | 2019-08-09 | 深圳壹账通智能科技有限公司 | Software program quality evaluating method, device, computer equipment and storage medium |
| CN111190817A (en) * | 2019-12-23 | 2020-05-22 | 平安医疗健康管理股份有限公司 | Method and device for processing software defects |
-
2020
- 2020-09-29 CN CN202011055410.8A patent/CN112363911A/en active Pending
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080201612A1 (en) * | 2007-02-16 | 2008-08-21 | Kathryn Allyn Bassin | Defect Resolution Methodology and Data Defects Quality/Risk Metric Model Extension |
| US20140201573A1 (en) * | 2013-01-14 | 2014-07-17 | International Business Machines Corporation | Defect analysis system for error impact reduction |
| CN104899143A (en) * | 2015-06-15 | 2015-09-09 | 中国航空无线电电子研究所 | Software peer review system realizing device for providing DM (Data Mining) |
| CN108241574A (en) * | 2016-12-26 | 2018-07-03 | 航天信息股份有限公司 | A kind of method and system analyzed based on test and management tool QC software test defect |
| CN107783890A (en) * | 2016-12-28 | 2018-03-09 | 平安科技（深圳）有限公司 | Software defect data processing method and device |
| CN107480065A (en) * | 2017-08-15 | 2017-12-15 | 郑州云海信息技术有限公司 | A kind of defect management method and equipment |
| CN107908550A (en) * | 2017-10-27 | 2018-04-13 | 链家网（北京）科技有限公司 | A kind of software defect statistical processing methods and device |
| CN110109821A (en) * | 2019-03-19 | 2019-08-09 | 深圳壹账通智能科技有限公司 | Software program quality evaluating method, device, computer equipment and storage medium |
| CN111190817A (en) * | 2019-12-23 | 2020-05-22 | 平安医疗健康管理股份有限公司 | Method and device for processing software defects |
Non-Patent Citations (2)
| Title |
|---|
| 司倩然;张慧颖;闫国英;: "基于缺陷分析的软件测试有效性评估方法", 计算机工程与设计, no. 03, pages 915 - 919 * |
| 王汉雄: "软件缺陷管理系统的设计与实现", 中国优秀硕士学位论文全文数据库信息科技辑, vol. 2019, no. 01, pages 138 - 1195 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111190817A (en) * | 2019-12-23 | 2020-05-22 | 平安医疗健康管理股份有限公司 | Method and device for processing software defects |
| CN113128981A (en) * | 2021-05-18 | 2021-07-16 | 中国农业银行股份有限公司 | Project management method and system |
| CN113297092A (en) * | 2021-06-21 | 2021-08-24 | 中国农业银行股份有限公司 | Defect prediction method of software and related equipment |
| CN113742227A (en) * | 2021-09-02 | 2021-12-03 | 上海浦东发展银行股份有限公司 | Method, device, equipment and medium for controlling software testing process |
| CN113742227B (en) * | 2021-09-02 | 2024-01-23 | 上海浦东发展银行股份有限公司 | Control method, device, equipment and medium for software testing process |
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