Delivery quality alarm processing method and device for informatization research and development project

Abstract

The invention provides a delivery quality alarm processing method and device for an informatization research and development project, and belongs to the technical field of research and development management. The method comprises the following steps: extracting research and development process data of a research and development team of an informatization research and development project from a data acquisition end; performing classification processing on the research and development process data according to multiple types of preset delivery quality indexes to obtain a group of to-be-processed data corresponding to each type of preset delivery quality index, and selecting a preset operation model corresponding to the to-be-processed data from multiple preset operation models to determine as a target operation model; inputting the to-be-processed data into the target operation model to generate a first delivery quality value; accumulating all the first delivery quality values to obtain a second delivery quality value; and if yes, generating an alarm prompt and sending the alarm prompt to a project management end, so that the project management end completes reminding of a user related to the informatization research and development project. According to the invention, the efficiency and accuracy of delivery quality alarm processing can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of R&D management, and in particular to a method and device for processing delivery quality alarms of information-based R&D projects. Background Art

[0002] Delivery quality is one of the performance evaluation indicators for R&D teams of information technology R&D projects. The work quality of all R&D teams can be evaluated through delivery quality to motivate R&D teams to improve the delivery quality of information technology R&D projects.

[0003] At present, in the existing technology, the scoring personnel designated by the management mainly score the project delivery quality of the R&D team according to their own scoring experience or subjective judgment criteria, and then determine the delivery quality warning information according to the scoring results and send it to the project management end, so that the management personnel and the R&D team can form a work process or method to better improve the delivery quality according to the scoring results.

[0004] However, the inventors have found that the prior art has at least the following technical problems: the process of delivery quality scoring is time-consuming and labor-intensive, and its accuracy needs to be improved urgently to improve the efficiency and accuracy of delivery quality alarm processing. Summary of the invention

[0005] The present application provides a delivery quality alarm processing method and device for an information-based R&D project, which are used to improve the scoring efficiency and accuracy of delivery quality scoring, thereby improving the efficiency and accuracy of delivery quality alarm processing.

[0006] In a first aspect, an embodiment of the present application provides a delivery quality alarm processing method for an information-based R&D project, which is applied to a server, and the method includes:

[0007] Extracting the R&D process data of the R&D team of the information technology R&D project from the data collection end;

[0008] Classify and process the R&D process data according to multiple types of preset delivery quality indicators to obtain a group of to-be-processed data corresponding to each type of preset delivery quality indicator, and select a preset operation model corresponding to the to-be-processed data from multiple preset operation models according to each type of preset delivery quality indicator to determine as a target operation model;

[0009] Inputting the data to be processed into a target operation model to generate a first delivery quality value;

[0010] All first delivery quality values ​​are accumulated to obtain a second delivery quality value;

[0011] When it is detected that the second delivery quality value meets the preset alarm threshold, an alarm prompt is generated and sent to the project management end, so that the project management end can complete the reminder of users related to the information technology research and development project.

[0012] In a possible implementation, the extracting of the R&D process data of the R&D team of the information-based R&D project from the data collection terminal includes: extracting R&D demand information from the data collection terminal; responding to the R&D operations performed by the R&D team related to the information-based R&D project based on the R&D demand information, and recording the completed R&D projects generated during the R&D operations as the first data and the unfinished R&D projects as the second data.

[0013] In a possible implementation, the preset delivery quality indicators include interval ratio smoothing indicators, benchmark value dynamic change indicators and benchmark value selection indicators, wherein the interval ratio smoothing indicators include weighted production problem rate, the benchmark value dynamic change indicators include weighted verification defect rate and internal test case coverage, and the benchmark value selection indicators include internal test thoroughness; accordingly, the R&D process data is classified and processed according to multiple types of preset delivery quality indicators to obtain a group of to-be-processed data corresponding to each type of preset delivery quality indicators, and a preset operation model corresponding to the to-be-processed data is selected from multiple preset operation models according to each type of preset delivery quality indicators to be determined as a target operation model, including: determining the number of published requirements in the R&D process data as a group of first to-be-processed data corresponding to the weighted production problem rate according to the weighted production problem rate, and selecting a weighted production problem rate operation model corresponding to the first to-be-processed data from multiple preset operation models according to the weighted production problem rate to be determined as a target operation model; calculation model; or according to the weighted verification defect rate, determine the number of published requirements in the R&D process data as a group of second data to be processed corresponding to the weighted verification defect rate, and select a weighted verification defect rate calculation model corresponding to the second data to be processed from multiple preset calculation models according to the weighted verification defect rate to be determined as the target calculation model; or according to the internal test case coverage, determine the number of published requirements of the mounted test cases and the total number of published requirements in the R&D process data as a group of third data to be processed corresponding to the internal test case coverage, and select a coverage calculation model corresponding to the third data to be processed from multiple preset calculation models according to the internal test case coverage to be determined as the target calculation model; or according to the internal test exhaustiveness, determine the R&D team integration test task hours and R&D team coding hours in the R&D process data as a group of fourth data to be processed corresponding to the internal test exhaustiveness, and select an internal test exhaustiveness calculation model corresponding to the third data to be processed from multiple preset calculation models according to the internal test exhaustiveness to be determined as the target calculation model.

[0014] In a possible implementation, the benchmark value dynamic change indicator also includes a defect clearance rate, and the target operation model corresponding to the defect clearance rate is a clearance rate operation model.

[0015] In a possible implementation, the preset operation model includes preset weights, the preset weights are associated with the R&D requirement information and / or the preset delivery quality indicators, and the preset weights include internal test defect weights, production defect weights, and acceptance defect weights;

[0016] Accordingly, the weighted production problem rate calculation model is:

[0017] If P>a, then x=x1 points; if P∈(b,a], then x takes a value in the interval from x1 to x2; if P∈(c,b], then x takes a value in the interval from x2 to x3; if P≤c, then x=x3 points, where P is the weighted production problem rate value determined according to the first data to be processed and the preset weighting number, and x is the result output by the weighted production problem rate calculation model, in which 0<c<b<a≤1, 0≤x1<x2<x3; the weighted production problem rate value determined according to the first data to be processed and the preset weighting number is calculated as follows: weighted production problem rate value=weighted number of production defects÷number of published requirements;

[0018] The weighted inspection defect rate calculation model is:

[0019] R = first reference value + first standard value × n × (first reference value - r) ÷ (first reference value - min)

[0020] Wherein, R is the result output by the weighted verification defect rate calculation model, r is the weighted verification defect rate value determined according to the second data to be processed and the preset weighted number, the reference value is m times the first standard value, the first standard value is the preset acceptance defect value, the reference value is the maximum value of the weighted verification defect rate values ​​of all R&D teams, min is the minimum value of the weighted verification defect rate values ​​of all R&D teams, m and n are preset coefficients less than 1, and the calculation method of the weighted verification defect rate value determined according to the second data to be processed and the preset weighted number is: weighted verification defect rate value = acceptance test defect weighted number ÷ number of released requirements;

[0021] The coverage calculation model is: C=second standard value×z; wherein, C is the result output by the coverage calculation model, the second standard value is the preset coverage score, z is the coefficient determined by the interval proportional smoothing score, C∈[e%,f%], z∈[zo,z1], wherein 1≤e<f≤100, 1≤z0<z1≤1, and the calculation method of the internal test case coverage value determined according to the number of release requirements of the mounted test cases in the R&D process data is: internal test case coverage value=number of release requirements of the mounted test cases÷total number of release requirements;

[0022] The internal test degree calculation model is:

[0023] If I>d, then y=y1 points, if I≤d, then y=y0 points, where I is the internal test exhaustiveness value determined according to the R&D team's integrated test task hours and the R&D team's coding hours, d is the internal test exhaustiveness threshold pre-selected from (0,1), y is the result output by the internal test degree calculation model, y1 is a natural number greater than y0, y0∈[0,y1], where the internal test exhaustiveness value determined according to the R&D team's integrated test task hours and the R&D team's coding hours is calculated as follows:

[0024] Internal test detail value = R&D team integration test task hours ÷ R&D team coding hours;

[0025] The clearance calculation model is:

[0026] DER=second reference value+third standard value×q×(second reference value-actual defect removal value)÷(second reference value-minimum defect removal value), where DER is the result output by the removal rate calculation model, second reference value=third standard value×defect removal coefficient, minimum defect removal value is the smallest value among all defect removal rate values, actual defect removal value is the real-time value among all defect removal rate values, q is the preset removal rate calculation coefficient, and the third standard value is the preset defect removal rate value, wherein the defect removal rate value is calculated as follows:

[0027] Defect clearance rate value = weighted number of internal test defects ÷ (weighted number of internal test defects + weighted number of production defects + weighted number of acceptance defects).

[0028] In a possible implementation, the method further includes: when it is detected that the information technology R&D project is a test project and the test project is a self-test project, adding the weighted verification defect rate value to the weighted production problem rate value.

[0029] In one possible implementation, the R&D team of the information technology R&D project includes a testing team and other R&D teams, the second delivery quality value includes the testing team score and the other R&D team scores, and the ratio of the testing team score to the second delivery quality value is a preset responsibility value.

[0030] In a second aspect, the embodiment of the present application further provides a delivery quality alarm processing device for an information-based R&D project, which is applied to a server and includes:

[0031] An extraction module, used to extract the R&D process data of the R&D team of the information-based R&D project from the data collection terminal;

[0032] a processing module, configured to classify and process the R&D process data according to multiple types of preset delivery quality indicators, obtain a group of to-be-processed data corresponding to each type of preset delivery quality indicators, and select a preset operation model corresponding to the to-be-processed data from multiple preset operation models according to each type of preset delivery quality indicators to determine as a target operation model;

[0033] A computing module, configured to input the data to be processed into a target computing model to generate a first delivery quality value;

[0034] The operation module is further used to accumulate all the first delivery quality values ​​to obtain a second delivery quality value;

[0035] The alarm module is used to generate an alarm prompt and send it to the project management end when it is detected that the second delivery quality value meets the preset alarm threshold, so that the project management end can complete the reminder of users related to the information technology R&D project.

[0036] In a third aspect, the present application provides an electronic device, comprising: at least one processor and a memory;

[0037] The memory stores computer-executable instructions;

[0038] The at least one processor executes the computer-executable instructions stored in the memory, so that the at least one processor executes the delivery quality alarm processing method of the information-based R&D project as described in the first aspect above.

[0039] In a fourth aspect, the present application provides a computer-readable storage medium, in which a computer-executable instruction is stored. When a processor executes the computer-executable instruction, the delivery quality alarm processing method of the information technology R&D project described in the first aspect above is implemented.

[0040] The present application provides a delivery quality alarm processing method and device for an information-based R&D project. The method first extracts the R&D process data of the R&D team of the information-based R&D project from the data acquisition end, and then obtains a group of to-be-processed data corresponding to each type of preset delivery quality indicator, and selects a preset operation model corresponding to the to-be-processed data from multiple preset operation models according to each type of preset delivery quality indicator to be determined as the target operation model. The to-be-processed data is then input into the target operation model to generate a first delivery quality value. All first delivery quality values ​​are accumulated to obtain a second delivery quality value. Finally, when it is detected that the second delivery quality value meets the preset alarm threshold, an alarm prompt is generated and sent to the project management end, so that the project management end can complete the reminder to users related to the information-based R&D project. The delivery quality alarm results of the information-based R&D project are more accurate due to the reduction of human factors, and the efficiency of alarm processing can also be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0042] Figure 1 A schematic diagram of an application scenario of a method for processing delivery quality alarms for an information-based R&D project provided in an embodiment of the present application;

[0043] Figure 2 A flowchart of a method for processing delivery quality alarms of an information-based R&D project provided in an embodiment of the present application;

[0044] Figure 3 A schematic diagram of the structure of a delivery quality alarm processing device for an information-based R&D project provided in an embodiment of the present application;

[0045] Figure 4 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application.

[0046] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0047] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0048] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0049] At present, the R&D team of an information technology R&D project is scored only based on manual experience and subjective judgment. When the amount of data in the R&D process of an information technology R&D project increases, the process of evaluating the delivery quality of the information technology R&D project requires a lot of time and labor costs. In addition, with the continuous work of manual labor, human errors such as increased scoring errors occur, resulting in a decrease in the accuracy of the delivery quality scoring results.

[0050] To solve the above technical problems, the embodiment of the present application provides the following technical ideas for solving the problems: first, collect the R&D process data of the R&D team, then model and generate a scoring model based on a variety of preset delivery quality indicators, and then automatically score the R&D process data corresponding to each preset quality indicator using the R&D process data and the scoring model to obtain a scoring result, and finally automatically perform a comprehensive score based on the scoring results corresponding to all preset delivery quality indicators, and execute an alarm processing flow based on the result of the comprehensive score, so that the project management end can complete the reminder of users related to the information technology R&D project. The entire process automatically scores the delivery quality and handles the alarm, which improves efficiency while also improving accuracy.

[0051] Figure 1 A schematic diagram of an application scenario of the method for processing delivery quality alarms of an information-based R&D project provided in an embodiment of the present application, such as Figure 1 As shown, it includes: a data collection terminal 101, a server 102 and a project management terminal 103.

[0052] The data collection terminal 101 is used to transmit the R&D process data from the cloud, other servers or mobile terminals. The server 102 is used to extract the R&D process data from the data collection terminal 101, and perform the delivery quality alarm processing operation of the informationized R&D project to generate the scoring result. The project management terminal 103 is used for users to view the scoring results and related data of the informationized R&D project and for managing the team.

[0053] Figure 2 This is a flow chart of a method for processing a delivery quality alarm of an information-based R&D project provided in an embodiment of the present application. The execution subject of this embodiment may be Figure 1The server 102 in the illustrated embodiment may also be other computer-related devices, and there is no particular limitation on this embodiment.

[0054] like Figure 2 As shown in the figure, the delivery quality alarm processing method of the information technology R&D project includes:

[0055] S201: Extract the R&D process data of the R&D team of the information technology R&D project from the data collection end.

[0056] In this embodiment, the R&D process data of the information-based R&D project refers to the data related to all the R&D operation processes of the R&D team recorded on the online platform when the R&D team carries out R&D work according to the R&D demand information after the demand side provides the R&D demand information of the information-based project. For example: the R&D time data of the R&D team and the project testing workload data.

[0057] Specifically, in an optional embodiment of the present application, step S201 includes:

[0058] S201a: extracting R&D demand information from the data collection terminal;

[0059] S201b: In response to the R&D operation performed by the R&D team related to the information-based R&D project based on the R&D demand information, the completed R&D projects generated during the R&D operation are recorded as first data and the unfinished R&D projects are recorded as second data.

[0060] In this embodiment, the data acquisition terminal is configured with an online platform, which can be a management system, software or web page. The data acquisition terminal generates R&D demand information in response to relevant operations of the user. The data acquisition terminal and the server can be connected by communication so that the server can extract the R&D demand information from the data terminal.

[0061] In an optional embodiment of the present application, the R&D demand information includes business requirements, public requirements and version requirements. Among them, business requirements refer to the needs of business development or business management. Business requirements can be requirements for information technology R&D projects (such as building an information system) to achieve a specific function. Public requirements refer to requirements created to meet the daily management requirements of information technology R&D projects. After the public requirements are realized, the information system can be used to connect daily project tasks such as technical research, training and leave approval. Version requirements refer to requirements created to meet the requirements of software version management and development process management. Version requirements are used to connect tasks related to the version work but not belonging to any business requirements, such as: development and testing plan formulation, demand evaluation, various review meetings and system testing.

[0062] S202: Classify and process the R&D process data according to multiple types of preset delivery quality indicators to obtain a group of data to be processed corresponding to each type of preset delivery quality indicators, and select a preset operation model corresponding to the data to be processed from multiple preset operation models according to each type of preset delivery quality indicators to determine it as the target operation model.

[0063] In this embodiment, there are multiple types of preset delivery quality indicators, and the delivery quality of an information technology R&D project is determined by the values ​​corresponding to all preset delivery quality indicators. Therefore, it is necessary to select a group of to-be-processed data corresponding to each type of preset delivery quality indicator from the R&D process data. Each preset delivery quality indicator and the corresponding to-be-processed data can be calculated using only one specific preset calculation model to obtain a delivery quality score.

[0064] Specifically, in an optional embodiment of the present application, the preset delivery quality indicators include interval ratio smoothing indicators, benchmark value dynamic change indicators and benchmark value selected indicators, wherein the interval ratio smoothing indicators include weighted production problem rate, benchmark value dynamic change indicators include weighted verification defect rate and internal test case coverage, and benchmark value selected indicators include internal test exhaustiveness. Accordingly, step S202 includes:

[0065] S202a: According to the weighted production problem rate, the number of published requirements in the R&D process data is determined as a group of first data to be processed corresponding to the weighted production problem rate, and according to the weighted production problem rate, a weighted production problem rate operation model corresponding to the first data to be processed is selected from multiple preset operation models to be determined as the target operation model.

[0066] In this embodiment, the weighted production problem rate can be determined based on the preset value of production problems and the number of released demands within a specific period. The preset operation model can be a formula, a scoring rule or a scoring method. Among them, the weighted production problem rate operation model can be an interval proportional smoothing scoring model.

[0067] In an optional embodiment of the present application, the weighted production problem rate calculation model is:

[0068] If P>a, then x=x1 points; if P∈(b,a], then x takes values ​​in the interval from x1 to x2; if P∈(c,b], then x takes values ​​in the interval from x2 to x3; if P≤c, then x=x3 points, where P is the weighted production problem rate value determined according to the first data to be processed and the preset weighting number, and x is the result output by the weighted production problem rate calculation model. In the weighted production problem rate calculation model, 0<c<b<a≤1, 0≤x1<x2<x3. The weighted production problem rate value determined according to the first data to be processed and the preset weighting number is calculated as follows:

[0069] Weighted production problem rate value = weighted number of production defects ÷ number of released requirements.

[0070] Based on the above-mentioned embodiment, in an optional embodiment of the present application, the weighted number of production defects can be set to different values ​​according to the different levels of R&D requirements, and the weighted production problem rate values ​​of R&D requirements of different levels can also use different levels of published requirements, such as the secondary weighted production problem rate value = secondary production defect weighted number ÷ published secondary demand number. For example: the weighted number of production defects for the first-level R&D demand is 8, the weighted number of production defects for the second-level R&D demand is 5, the weighted number of production defects for the third-level R&D demand is 3, and the weighted number of production defects for the fourth-level R&D demand is 0.5. It can be seen that the higher the level of the R&D demand, the greater its impact on the delivery quality, so the greater the weighted number of production defects, and the greater the final weighted production problem rate value. Of course, for different types of R&D requirements, the weighted numbers of production defects can also be set to different values. The evaluation of delivery quality is more accurate.

[0071] For example, assuming a=1, b=0.26, c=0.23, x1=0 points, x2=8 points, x3=10 points. When P=1.5, x=0 points. When P=0.5, x can take a value of 4 points before (0,8]. When P=0.24, x can take a value of 8.5 points in the interval (8,10). When P=0.22, x can take a value of 10 points.

[0072] S202b: Or according to the weighted verification defect rate, determine the number of published requirements in the R&D process data as a group of second data to be processed corresponding to the weighted verification defect rate, and select a weighted verification defect rate operation model corresponding to the second data to be processed from multiple preset operation models according to the weighted verification defect rate and determine it as the target operation model.

[0073] In this embodiment, the weighted inspection defect rate can be determined according to the weighted number of acceptance defects and the number of published requirements. The weighted inspection defect rate calculation model can be a calculation formula.

[0074] Specifically, in an optional embodiment of the present application, the weighted inspection defect rate calculation model is:

[0075] R = first reference value + first standard value × n × (first reference value - r) ÷ (first reference value - min)

[0076] Wherein, R is the result output by the weighted inspection defect rate calculation model, r is the weighted inspection defect rate value determined according to the second data to be processed and the preset weight, the reference value is m times the first standard value, the first standard value is the preset acceptance defect value, the reference value is the maximum value of the weighted inspection defect rate values ​​of all R&D teams, min is the minimum value of the weighted inspection defect rate values ​​of all R&D teams, m and n are preset coefficients less than 1, and the weighted inspection defect rate value determined according to the second data to be processed and the preset weight is calculated as follows:

[0077] Weighted verification defect rate value = weighted number of acceptance test defects ÷ number of released requirements.

[0078] In this embodiment, the preset weighted inspection defect rate value may be a pre-set and stored score, for example, the preset weighted inspection defect rate value is 10 points.

[0079] In an optional embodiment of the present application, in the calculation method of the weighted verification defect rate value, the acceptance test weight and the number of published requirements can vary with the type of R&D requirements and the R&D level. For details, please refer to step S202b, so this embodiment will not be repeated here. It should be noted that the responsibilities of the department where the R&D team is located are different, and the calculation method of the weighted verification defect rate value can also be: weighted verification defect rate value = R&D-acceptance test defect weight ÷ number of published requirements.

[0080] S202c: Or according to the internal test case coverage, determine the number of release requirements and the total number of release requirements of the attached test cases in the R&D process data as a group of third data to be processed corresponding to the internal test case coverage, and select a coverage operation model corresponding to the third data to be processed from multiple preset operation models according to the internal test case coverage and determine it as the target operation model.

[0081] In this embodiment, the internal test case coverage rate can be determined according to the number of release requirements of the attached test cases and the total number of release requirements. The coverage rate calculation model can be a calculation formula.

[0082] Specifically, in an optional embodiment of the present application, the coverage calculation model is: C = second standard value × z; wherein, C is the result output by the coverage calculation model, the second standard value is the preset coverage score, z is the coefficient determined by the interval proportional smoothing score, C∈[e%,f%], z∈[zo,z1], wherein 1≤e<f≤100, 1≤z0<z1≤1, and the calculation method of the internal test case coverage value determined according to the number of published requirements of the attached test cases in the R&D process data is: internal test case coverage value = number of published requirements of the attached test cases ÷ total number of published requirements.

[0083] In this embodiment, the second standard value may be a preset fixed score, for example, the second standard value may be 5 points. z is a coefficient determined by the interval proportional smoothing score, and the coefficient is in the range of z0 to z1, where z0 may be 0 and z1 may be 1. At this time, e and f may be natural numbers greater than or equal to 0, for example, e=0 and f is 100.

[0084] S202d: Or according to the internal test exhaustiveness, determine the R&D team's integrated test task hours and the R&D team's coding hours in the R&D process data as a group of fourth data to be processed corresponding to the internal test exhaustiveness, and select an internal test exhaustiveness operation model corresponding to the third data to be processed from multiple preset operation models according to the internal test exhaustiveness and determine it as the target operation model.

[0085] Specifically, in an optional embodiment of the present application, the internal measurement exhaustiveness calculation model is:

[0086] If I>d, then y=y1 points, if I≤d, then y=y0 points, where I is the internal test detail value determined based on the R&D team's integration test task hours and the R&D team's coding hours, d is the internal test detail threshold pre-selected from (0,1), y is the result output by the internal test degree calculation model, y1 is a natural number greater than y0, y0∈[0,y1], where the calculation method for the internal test detail value determined based on the R&D team's integration test task hours and the R&D team's coding hours is:

[0087] Internal test thoroughness value = R&D team’s integration test task hours ÷ R&D team’s coding hours.

[0088] For example, d is 0.5, y1=5 points, y0=0 points, when I is greater than 0.5, y=5 points. When I is less than or equal to 0.5, y=0 points.

[0089] In an optional embodiment of the present application, the benchmark value dynamic change indicator also includes a defect clearance rate, and the target operation model corresponding to the defect clearance rate is a clearance rate operation model.

[0090] Specifically, the clearance calculation model is:

[0091] DER=second reference value+third standard value×q×(second reference value-actual defect removal value)÷(second reference value-minimum defect removal value), where DER is the result output by the removal rate calculation model, second reference value=third standard value×defect removal coefficient, minimum defect removal value is the smallest value among all defect removal rate values, actual defect removal value is the real-time value among all defect removal rate values, and third standard value is the preset defect removal rate value. The defect removal rate value is calculated as follows:

[0092] Defect clearance rate value = weighted number of internal test defects ÷ (weighted number of internal test defects + weighted number of production defects + weighted number of acceptance defects).

[0093] In this embodiment, the third standard value may be a pre-set score, for example, the third standard value may be 5 points. The second reference value may be the maximum value of the defect removal rate values ​​of all R&D teams in the R&D center. The defect removal rate coefficient may be a pre-set fixed value, for example, the defect removal rate coefficient is 0.6. q may be a pre-set value of the operation coefficient, for example, q is 0.4.

[0094] S203: Input the data to be processed into the target operation model to generate a first delivery quality value.

[0095] In this embodiment, after the data to be processed is input into the target operation model, it is operated by a calculation formula or a scoring method to output a score as the first delivery quality value.

[0096] S204: All first delivery quality values ​​are accumulated to obtain a second delivery quality value.

[0097] In this embodiment, the first delivery quality values ​​corresponding to all preset delivery quality indicators are processed comprehensively, such as cumulatively, to obtain the sum of all first delivery quality values, the second delivery quality value. The second delivery quality value can be used as the final evaluation score of the delivery quality of the information technology R&D project.

[0098] S205: When it is detected that the second delivery quality value meets the preset alarm threshold, an alarm prompt is generated and sent to the project management end, so that the project management end can complete the reminder of users related to the information technology research and development project.

[0099] In this embodiment, the preset alarm threshold may be a preset score dividing line, for example, the preset alarm threshold may be 80 points. When the second delivery quality value is less than 80 points, it means that the second delivery quality value meets the preset alarm threshold, and an alarm prompt needs to be generated at this time.

[0100] In this embodiment, the warning prompt can be a text prompt, a voice prompt or an icon prompt, etc., which can remind the R&D team, the demander or the management of the R&D team that the delivery quality of the information R&D project is low, so as to facilitate further R&D work of the information R&D project.

[0101] Based on the above embodiment, in an optional embodiment of the present application, when it is detected that the information technology research and development project is a test project and the test project is a self-test project, the weighted verification defect rate value is added to the weighted production problem rate value.

[0102] In this embodiment, the self-test project can be a built-in self-test project that actively tests to ensure code quality and functional correctness. Only information technology R&D projects that require third-party acceptance testing have the problem of weighted verification defect rate. Therefore, the weighted verification defect rate value is added to the weighted production problem rate value to obtain the first delivery quality value corresponding to the weighted production problem rate value.

[0103] In an optional embodiment of the present application, the R&D team of the information technology R&D project includes a testing team and other R&D teams, the second delivery quality value includes the testing team score and other R&D team scores, and the ratio of the testing team score to the second delivery quality value is a preset responsibility value.

[0104] In this embodiment, the preset responsibility value can be a preset responsibility ratio, for example, the preset responsibility value can be 0.4. When the defects of the information technology R&D project to be released are not detected by the testing team, the testing team should bear 60% of the responsibility for the quality problems of the information technology R&D project delivery, so that the management can better manage the R&D team.

[0105] In summary, the delivery quality alarm processing method of an information-based R&D project provided in an embodiment of the present application first extracts the R&D process data of the R&D team of the information-based R&D project from the data acquisition end, and then obtains a set of data to be processed corresponding to each type of preset delivery quality indicator, and selects a preset operation model corresponding to the data to be processed from multiple preset operation models according to each type of preset delivery quality indicator to be determined as the target operation model. Then the data to be processed is input into the target operation model to generate a first delivery quality value. All first delivery quality values ​​are accumulated to obtain a second delivery quality value. Finally, when it is detected that the second delivery quality value meets the preset alarm threshold, an alarm prompt is generated and sent to the project management end, so that the project management end can complete the reminder to users related to the information-based R&D project. The delivery quality alarm results of the information-based R&D project are more accurate due to the reduction of human factors, and the efficiency of alarm processing can also be improved.

[0106] At the same time, multiple types of preset delivery quality indicators are set for the R&D team, and the preset weighted values ​​in the corresponding preset operation model are different depending on the level and type of R&D needs. This makes the first delivery quality value of the data to be processed corresponding to the delivery quality indicator of each R&D need more accurate, further improving the accuracy of the delivery quality alarm results.

[0107] At the same time, some first delivery quality values ​​are generated using an interval proportional smoothing scoring method. Compared with the manual numerical value division interval method, this application can better balance the differences in delivery quality alarm results, making the delivery quality alarm results between R&D teams closer, and will not cause excessive gaps in alarm results, affecting the work enthusiasm of the R&D team.

[0108] Figure 3 The structure diagram of the delivery quality alarm processing device of the information technology R&D project provided in the embodiment of the present application is applied to the delivery quality alarm processing device of the information technology R&D project. Figure 1 The server 102 shown includes: an extraction module 31, a processing module 32, a calculation module 33 and an alarm module 34.

[0109] The extraction module 31 is used to extract the R&D process data of the R&D team of the information-based R&D project from the data collection end.

[0110] The processing module 32 is used to classify and process the R&D process data according to multiple types of preset delivery quality indicators, obtain a group of data to be processed corresponding to each type of preset delivery quality indicators, and select a preset operation model corresponding to the data to be processed from multiple preset operation models according to each type of preset delivery quality indicators to determine it as the target operation model.

[0111] The operation module 33 is used to input the data to be processed into the target operation model to generate a first delivery quality value.

[0112] The calculation module 33 is further used to accumulate all the first delivery quality values ​​to obtain a second delivery quality value.

[0113] The alarm module 34 is used to generate an alarm prompt and send it to the project management end when it is detected that the second delivery quality value meets the preset alarm threshold, so that the project management end can complete the reminder of users related to the information technology research and development project.

[0114] In an optional embodiment of the present application, the extraction module 31 is specifically used to: extract R&D demand information from a data acquisition terminal; respond to R&D operations performed by an R&D team related to an information-based R&D project on the R&D demand information, and record completed R&D projects generated during the R&D operations as first data and unfinished R&D projects as second data.

[0115] In an optional embodiment of the present application, the preset delivery quality indicators include interval ratio smoothing indicators, benchmark value dynamic change indicators and benchmark value selection indicators, wherein the interval ratio smoothing indicators include weighted production problem rate, the benchmark value dynamic change indicators include weighted verification defect rate and internal test case coverage, and the benchmark value selection indicators include internal test thoroughness; accordingly, the processing module 32 is specifically used to: determine the number of published requirements in the R&D process data as a group of first data to be processed corresponding to the weighted production problem rate according to the weighted production problem rate, and select a weighted production problem rate operation model corresponding to the first data to be processed from multiple preset operation models according to the weighted production problem rate as the target operation model; or determine the number of published requirements in the R&D process data as a group of second data to be processed corresponding to the weighted verification defect rate according to the weighted verification defect rate According to the weighted verification defect rate, a weighted verification defect rate operation model corresponding to the second data to be processed is selected from multiple preset operation models to be determined as the target operation model; or according to the internal test case coverage, the number of release requirements and the total number of release requirements of the attached test cases in the R&D process data are determined as a group of third data to be processed corresponding to the internal test case coverage, and according to the internal test case coverage, a coverage operation model corresponding to the third data to be processed is selected from multiple preset operation models to be determined as the target operation model; or according to the internal test exhaustiveness, the R&D team integration test task hours and the R&D team coding hours in the R&D process data are determined as a group of fourth data to be processed corresponding to the internal test exhaustiveness, and according to the internal test exhaustiveness, an internal test exhaustiveness operation model corresponding to the third data to be processed is selected from multiple preset operation models to be determined as the target operation model.

[0116] In an optional embodiment of the present application, the processing module 32 is used to classify and process the R&D process data according to multiple types of preset delivery quality indicators, obtain a group of to-be-processed data corresponding to each type of preset delivery quality indicators, and select a preset operation model corresponding to the to-be-processed data from multiple preset operation models according to each type of preset delivery quality indicators to determine as a preset weighted number in the target operation model, the preset weighted number is associated with the R&D demand information and / or the preset delivery quality indicators, and the preset weighted number includes the weighted number of internal test defects, the weighted number of production defects, and the weighted number of acceptance defects. Accordingly, the weighted production problem rate operation model is:

[0117] If P>a, then x=x1 points; if P∈(b,a], then x takes a value in the interval from x1 to x2; if P∈(c,b], then x takes a value in the interval from x2 to x3; if P≤c, then x=x3 points, where P is the weighted production problem rate value determined according to the first data to be processed and the preset weighted number, and x is the result output by the weighted production problem rate calculation model, and in the weighted production problem rate calculation model, 0<c<b<a≤1, 0≤x1<x2<x3; the weighted production problem rate value determined according to the first data to be processed and the preset weighted number is calculated as follows: weighted production problem rate value = weighted number of production defects ÷ number of released requirements;

[0118] The weighted inspection defect rate calculation model is:

[0119] R = first reference value + first standard value × n × (first reference value - r) ÷ (first reference value - min)

[0120] Wherein, R is the result output by the weighted inspection defect rate calculation model, r is the weighted inspection defect rate value determined according to the second data to be processed and the preset weighted number, the reference value is m times the first standard value, the first standard value is the preset acceptance defect value, the first reference value is the maximum value of the weighted inspection defect rate values ​​of all R&D teams and can be dynamically changed, min is the minimum value of the weighted inspection defect rate values ​​of all R&D teams, m and n are preset coefficients less than 1, and the weighted inspection defect rate value determined according to the second data to be processed and the preset weighted number is calculated as follows: weighted inspection defect rate value = acceptance test defect weighted number ÷ number of released requirements;

[0121] The coverage calculation model is: C = second standard value × z; where C is the result output by the coverage calculation model, the second standard value is the preset coverage score, and z is the coefficient determined by the interval proportional smoothing score, C∈[e%,f%], z∈[zo,z1], where 1≤e<f≤100, 1≤z0<z1≤1, and the calculation method of the internal test case coverage value determined according to the number of release requirements of the attached test cases in the R&D process data is: internal test case coverage value = number of release requirements of the attached test cases ÷ total number of release requirements;

[0122] The internal test degree calculation model is:

[0123] If I>d, then y=y1 points, if I≤d, then y=y0 points, where I is the internal test detail value determined based on the R&D team's integration test task hours and the R&D team's coding hours, d is the internal test detail threshold pre-selected from (0,1), y is the result output by the internal test degree calculation model, y1 is a natural number greater than y0, y0∈[0,y1], where the calculation method for the internal test detail value determined based on the R&D team's integration test task hours and the R&D team's coding hours is:

[0124] Internal test detail value = R&D team integration test task hours ÷ R&D team coding hours;

[0125] The clearance calculation model is:

[0126] DER=second reference value+third standard value×q×(second reference value-actual defect removal value)÷(second reference value-minimum defect removal value), where DER is the result output by the removal rate calculation model, second reference value=third standard value×defect removal coefficient, minimum defect removal value is the smallest value among all defect removal rate values, actual defect removal value is the real-time value among all defect removal rate values, q is the preset removal rate calculation coefficient, and the third standard value is the preset defect removal rate value. The defect removal rate value is calculated as follows:

[0127] Defect clearance rate value = weighted number of internal test defects ÷ (weighted number of internal test defects + weighted number of production defects + weighted number of acceptance defects).

[0128] In an optional embodiment of the present application, the processing module 32 is also used for: when it is detected that the information technology research and development project is a test project and the test project is a self-test project, the weighted verification defect rate value is added to the weighted production problem rate value.

[0129] The delivery quality alarm processing device for information-based R&D projects provided in this embodiment can be used to execute the technical solution of the above-mentioned method embodiment. Its implementation principle and technical effects are similar, and this embodiment will not be repeated here.

[0130] Figure 4 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application is shown in FIG. Figure 4 As shown, the electronic device includes: at least one processor 401 and a memory 402.

[0131] The memory 402 is used to store computer-executable instructions.

[0132] The processor 401 is used to execute the computer-executable instructions stored in the memory 402 to implement the various steps involved in the above method embodiment. For details, please refer to the relevant description in the above method embodiment.

[0133] Optionally, the memory 402 may be independent or integrated with the processor 401 .

[0134] When the memory 402 is independently provided, the electronic device further includes a bus 403 for connecting the memory 402 and the processor 401 .

[0135] The embodiment of the present application further provides a computer-readable storage medium, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, the above-mentioned delivery quality alarm processing method for an information-based R&D project is implemented.

[0136] An embodiment of the present application also provides a computer program product, including a computer program, which, when executed by a processor, implements the above-mentioned method for processing delivery quality alarms of information-based R&D projects.

[0137] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the above modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0138] The modules described above as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to implement the solution of this embodiment.

[0139] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, each module may exist physically separately, or two or more modules may be integrated into one unit. The unit composed of the above modules may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0140] The above-mentioned integrated module implemented in the form of a software function module can be stored in a computer-readable storage medium. The above-mentioned software function module is stored in a storage medium, including a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor to perform some steps of the method of each embodiment of the present application.

[0141] It should be understood that the above processor can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in the invention can be directly embodied as being executed by a hardware processor, or can be executed by a combination of hardware and software modules in the processor.

[0142] The memory may include a high-speed RAM memory, and may also include a non-volatile storage NVM, such as at least one disk memory, and may also be a USB flash drive, a mobile hard disk, a read-only memory, a magnetic disk or an optical disk, etc.

[0143] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the bus in the drawings of this application is not limited to only one bus or one type of bus.

[0144] The above storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The storage medium can be any available medium that can be accessed by a general or special purpose computer.

[0145] An exemplary storage medium is coupled to a processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can be located in an application specific integrated circuit (ASIC). Of course, the processor and the storage medium can also exist as discrete components in an electronic device or a main control device.

[0146] Those skilled in the art can understand that all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, disk or optical disk and other media that can store program codes.

[0147] The present invention is intended to illustrate the technical solution of the present application rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some or all of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solution from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A delivery quality alarm processing method for an information-based R&D project, characterized in that: Applied to a server, the method comprises: Extracting the R&D process data of the R&D team of the information technology R&D project from the data collection end; Classify and process the R&D process data according to multiple types of preset delivery quality indicators to obtain a group of to-be-processed data corresponding to each type of preset delivery quality indicator, and select a preset operation model corresponding to the to-be-processed data from multiple preset operation models according to each type of preset delivery quality indicator to determine as a target operation model; Inputting the data to be processed into a target operation model to generate a first delivery quality value; All first delivery quality values ​​are accumulated to obtain a second delivery quality value; When it is detected that the second delivery quality value meets the preset alarm threshold, an alarm prompt is generated and sent to the project management end, so that the project management end can complete the reminder of users related to the information technology research and development project.

2. The method according to claim 1, characterized in that: The extracting of the R&D process data of the R&D team of the information technology R&D project from the data collection end includes: Extract R&D demand information from data collection terminals; In response to the R&D operation performed by the R&D team related to the information-based R&D project on the R&D demand information, the completed R&D projects generated during the R&D operation are recorded as first data and the unfinished R&D projects are recorded as second data.

3. The method according to claim 1, characterized in that The preset delivery quality indicators include interval ratio smoothing indicators, benchmark value dynamic change indicators and benchmark value selected indicators, wherein the interval ratio smoothing indicators include weighted production problem rate, the benchmark value dynamic change indicators include weighted verification defect rate and internal test case coverage, and the benchmark value selected indicators include internal test exhaustiveness; Accordingly, the R&D process data is classified and processed according to multiple types of preset delivery quality indicators to obtain a group of to-be-processed data corresponding to each type of preset delivery quality indicators, and a preset operation model corresponding to the to-be-processed data is selected from multiple preset operation models according to each type of preset delivery quality indicators to be determined as a target operation model, including: Determining the number of published requirements in the R&D process data as a set of first data to be processed corresponding to the weighted production problem rate according to the weighted production problem rate, and selecting a weighted production problem rate operation model corresponding to the first data to be processed from a plurality of preset operation models according to the weighted production problem rate as a target operation model; or Determining the number of published requirements in the R&D process data as a set of second data to be processed corresponding to the weighted verification defect rate according to the weighted verification defect rate, and selecting a weighted verification defect rate calculation model corresponding to the second data to be processed from a plurality of preset calculation models according to the weighted verification defect rate as a target calculation model; or Determine the number of release requirements and the total number of release requirements of the attached test cases in the research and development process data as a group of third data to be processed corresponding to the internal test case coverage according to the internal test case coverage, and select a coverage calculation model corresponding to the third data to be processed from multiple preset calculation models according to the internal test case coverage to determine it as the target calculation model; or According to the internal test exhaustiveness, the R&D team's integrated test task hours and the R&D team's coding hours in the R&D process data are determined as a group of fourth data to be processed corresponding to the internal test exhaustiveness, and according to the internal test exhaustiveness, an internal test exhaustiveness operation model corresponding to the third data to be processed is selected from multiple preset operation models and determined as the target operation model.

4. The method according to claim 3, characterized in that The benchmark value dynamic change indicator also includes a defect clearance rate, and the target operation model corresponding to the defect clearance rate is a clearance rate operation model.

5. The method according to claim 4, characterized in that The preset operation model includes preset weights, the preset weights are associated with the R&D demand information and / or the preset delivery quality indicators, and the preset weights include internal test defect weights, production defect weights, and acceptance defect weights; Accordingly, the weighted production problem rate calculation model is: If P>a, then x=x1 points; if P∈(b,a], then x takes a value in the interval from x1 to x2; if P∈(c,b], then x takes a value in the interval from x2 to x3; if P≤c, then x=x3 points, where P is the weighted production problem rate value determined according to the first data to be processed and the preset weighting number, and x is the result output by the weighted production problem rate calculation model, in which 0<c<b<a≤1, 0≤x1<x2<x3; the weighted production problem rate value determined according to the first data to be processed and the preset weighting number is calculated as follows: weighted production problem rate value=weighted number of production defects÷number of published requirements; The weighted inspection defect rate calculation model is: R = first reference value + first standard value × n × (first reference value - r) ÷ (first reference value - min) Wherein, R is the result output by the weighted verification defect rate calculation model, r is the weighted verification defect rate value determined according to the second data to be processed and the preset weighted number, the reference value is m times the first standard value, the first standard value is the preset acceptance defect value, the reference value is the maximum value of the weighted verification defect rate values ​​of all R&D teams, min is the minimum value of the weighted verification defect rate values ​​of all R&D teams, m and n are preset coefficients less than 1, and the calculation method of the weighted verification defect rate value determined according to the second data to be processed and the preset weighted number is: weighted verification defect rate value = acceptance test defect weighted number ÷ number of released requirements; The coverage calculation model is: C=second standard value×z; wherein, C is the result output by the coverage calculation model, the second standard value is the preset coverage score, z is the coefficient determined by the interval proportional smoothing score, C∈[e%,f%], z∈[zo,z1], wherein 1≤e<f≤100, 1≤z0<z1≤1, and the calculation method of the internal test case coverage value determined according to the number of release requirements of the mounted test cases in the R&D process data is: internal test case coverage value=number of release requirements of the mounted test cases÷total number of release requirements; The internal test degree calculation model is: If I>d, then y=y1 points, if I≤d, then y=y0 points, where I is the internal test exhaustiveness value determined according to the R&D team's integrated test task hours and the R&D team's coding hours, d is the internal test exhaustiveness threshold pre-selected from (0,1), y is the result output by the internal test degree calculation model, y1 is a natural number greater than y0, y0∈[0,y1], where the internal test exhaustiveness value determined according to the R&D team's integrated test task hours and the R&D team's coding hours is calculated as follows: Internal test detail value = R&D team integration test task hours ÷ R&D team coding hours; The clearance rate calculation model is: DER=second reference value+third standard value×q×(second reference value-actual defect removal value)÷(second reference value-minimum defect removal value), where DER is the result output by the removal rate calculation model, second reference value=third standard value×defect removal coefficient, minimum defect removal value is the smallest value among all defect removal rate values, actual defect removal value is the real-time value among all defect removal rate values, q is the preset removal rate calculation coefficient, and the third standard value is the preset defect removal rate value, wherein the defect removal rate value is calculated as follows: Defect clearance rate value = weighted number of internal test defects ÷ (weighted number of internal test defects + weighted number of production defects + weighted number of acceptance defects).

6. The method according to claim 5, characterized in that Also includes: When it is detected that the information technology research and development project is a test project and the test project is a self-test project, the weighted verification defect rate value is added to the weighted production problem rate value.

7. The method according to any one of claims 1 to 6, characterized in that The R&D team of the information technology R&D project includes the testing team and other R&D teams, the second delivery quality value includes the testing team score and other R&D team scores, and the ratio of the testing team score to the second delivery quality value is a preset responsibility value.

8. A delivery quality alarm processing device for an information-based R&D project, characterized in that: Applicable to servers, including: An extraction module, used to extract the R&D process data of the R&D team of the information-based R&D project from the data collection terminal; a processing module, configured to classify and process the R&D process data according to multiple types of preset delivery quality indicators, obtain a group of to-be-processed data corresponding to each type of preset delivery quality indicators, and select a preset operation model corresponding to the to-be-processed data from multiple preset operation models according to each type of preset delivery quality indicators to determine as a target operation model; A computing module, configured to input the data to be processed into a target computing model to generate a first delivery quality value; The operation module is further used to accumulate all the first delivery quality values ​​to obtain a second delivery quality value; The alarm module is used to generate an alarm prompt and send it to the project management end when it is detected that the second delivery quality value meets the preset alarm threshold, so that the project management end can complete the reminder of users related to the information technology R&D project.

9. An electronic device, characterized in that: include: at least one processor and memory; The memory stores computer-executable instructions; The at least one processor executes the computer-executable instructions stored in the memory, so that the at least one processor executes the delivery quality alarm processing method for an information-based R&D project according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions. When the processor executes the computer-executable instructions, the delivery quality alarm processing method for an information-based R&D project as described in any one of claims 1 to 7 is implemented.

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