A digital supervision system and method for software development based on cloud platform

Abstract

The present invention relates to the field of software R&D supervision, and specifically to a digital software R&D supervision system and method based on a cloud platform, comprising: a cloud communication module, a R&D container module, a test estimation module, a quantitative recording module and a resource management module. The cloud communication module is used to manage R&D data, the R&D container module is used to calculate one-dimensional test parameters of a process, the test estimation module is used to test delivered software and estimate the test parameters of each process, the quantitative recording module is used to calculate the comprehensive quality parameters of the process, and the resource management module is used to plan and adjust the resource allocation of the R&D process. The present invention can save the number of test items, reduce the overall test time, improve the test efficiency, help to achieve quantitative supervision of the development process, improve R&D efficiency, reduce R&D costs, maximize the input-output ratio, improve the software R&D return rate, and improve the efficiency and quality of software development.

Description

Technical Field

[0001] The present invention relates to the field of software development supervision, and specifically to a cloud platform-based digital software development supervision system and method. Background Art

[0002] Software development refers to the process of designing, developing, testing, deploying and maintaining software applications through systematic methods and processes. For larger software, each part of the software needs to be independently developed and finally the delivered software needs to be merged. Each process will have an impact on the actual function of the software, so each development process needs to be supervised to ensure the quality of software delivery.

[0003] The commonly used development supervision process requires data sampling of the development products of each process, but the software development process involves many parameters, and the real-time parameters of the software are constantly changing during the development process. The sampling method not only consumes a lot of resources, but also cannot accurately reflect the software development quality of each development process, resulting in the problem that the supervision process is difficult to update in real time.

[0004] In addition, the software development process requires resources such as manpower, capital, data and equipment. Different development processes require different resources, and experienced R&D managers are needed to allocate resources, making the resource allocation process difficult to quantify. In order to achieve the highest software delivery quality with limited resources, the software development process requires a digital allocation system. Summary of the invention

[0005] The purpose of the present invention is to provide a cloud platform-based digital software development supervision system and method to solve the problems raised in the above-mentioned background technology.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a digital software development supervision system based on a cloud platform, comprising: a cloud communication module, a development container module, a test estimation module, a quantitative recording module and a resource management module;

[0007] The cloud communication module is used to build a cloud test supervision platform, uniformly manage the data required in each R&D process, and recover the output data from the R&D process. At the same time, it builds a communication channel between the software R&D equipment and the cloud platform, and sets a data unpacking program at the end of the communication channel to capture the output data packets of the R&D equipment;

[0008] The R&D container module is used to add identification placeholders in the test data provided to the R&D process in the cloud according to a specific rule, capture identification placeholders in the output data of the R&D process, calculate the one-dimensional test parameters of the software in the development process according to the changes in the position and proportion of the placeholders in the output data, store the test results in the cloud database, and keep them updated in real time;

[0009] The test estimation module is used to generate test cases to perform concurrent testing on the R&D software, analyze the test feedback data in the cloud platform, obtain the two-dimensional test results of the software, the two-dimensional test results include all the test items in the one-dimensional test parameters, calculate the correlation coefficient between the two-dimensional test results of the software and the one-dimensional test parameters in each process, and estimate all the test item parameters of the process according to the correlation coefficient;

[0010] The quantitative recording module is used to obtain the comprehensive quality parameters of the process according to the preset weighted calculation method based on all the test item parameters of each R&D process, draw the relationship function between the comprehensive quality parameters and the data resource consumption, and remind the person in charge of the corresponding R&D process through the cloud platform when the decline rate of the relationship function is higher than the threshold;

[0011] The resource management module is used to adjust the resource allocation of the R&D process, recalculate the comprehensive quality weight of the process according to the adjusted test data, fit the joint influence function of the comprehensive quality weight and various types of resources, and use the actual usage requirements of the software as constraints to obtain the resource allocation conditions when the influence function takes the minimum value, and allocate R&D resources according to the calculation results of the resource allocation conditions.

[0012] Further, the cloud communication module includes: a platform storage unit and a data communication unit;

[0013] The platform storage unit is used to divide space in the cloud to build a data processing platform and database to uniformly manage test data;

[0014] The data communication unit is composed of communication management software installed in the R&D equipment and the cloud server, and is used to manage the communication process between the equipment and the server.

[0015] Further, the R&D container module includes: a process closure unit, a data flow unit and a one-dimensional test unit;

[0016] The process closing unit is used to close the software functions involved in each R&D process to form a separate test unit;

[0017] The data circulation unit is used to insert placeholders according to preset rules in the input data provided to the R&D process in the cloud, and to capture the identification placeholders in the output data uploaded to the cloud;

[0018] The one-dimensional test unit is used to calculate the one-dimensional test parameters of the software, including software test delay and R&D data redundancy, according to the position and proportion of the placeholders in the output data.

[0019] Further, the test estimation module includes: a concurrent testing unit and a related estimation unit;

[0020] The concurrent testing unit is used to perform concurrent testing on the final software to obtain all test items of the software, which are recorded as two-dimensional test results;

[0021] The correlation estimation unit is used to fit the correlation between the one-dimensional test parameters and the two-dimensional test results, and to construct the correlation to estimate the test item parameters in the process.

[0022] Further, the quantitative recording module includes: a quality assessment unit and a supervision alarm unit;

[0023] The quality assessment unit is used to obtain the comprehensive quality parameters of the process according to a preset weighted calculation method;

[0024] The supervision alarm unit is used to alarm the corresponding process when the function decline rate between the comprehensive quality parameter and the data resource consumption is higher than a threshold.

[0025] Further, the resource management module includes: an impact fitting unit, a constraint planning unit and a resource allocation unit;

[0026] The impact fitting unit is used to adjust the resource allocation amount of each R&D process during the R&D process, obtain the comprehensive quality parameters of the process, and calculate the impact function of the comprehensive quality parameters of each R&D process with the resource allocation amount;

[0027] The constraint programming unit is used to calculate the maximum value of the influence function under the constraint condition by taking the actual use requirements of the software as the constraint condition;

[0028] The resource allocation unit is used to adjust the resource allocation of each R&D process according to the linear programming result and send the allocation plan to the management department.

[0029] A digital supervision method for software development based on a cloud platform includes the following steps:

[0030] Step S1. Build a test supervision platform in the cloud to uniformly manage the data required in each R&D process, and set up communication management software in the R&D equipment and the cloud server, which can capture input and output data packets;

[0031] Step S2. Each software function involved in the R&D process is closed as an independent test unit. The platform inserts an identifier into the data provided to each test unit according to the first rule, and captures the identifier in the output data at the end of the channel, analyzes the distribution of the identifier in the input and output data, and obtains a one-dimensional test parameter;

[0032] Step S3. Perform concurrency testing on all test items of the delivered software, record the test results as two-dimensional test parameters, calculate the correlation between the one-dimensional test parameters and the two-dimensional test parameters, and estimate the parameters of the untested items in each test unit based on the correlation;

[0033] Step S4. Preset the weight of each test item, perform weighted calculation according to the test item parameters corresponding to each R&D process, obtain the comprehensive quality parameter corresponding to the R&D process, and issue an alarm when the comprehensive quality parameter is lower than the threshold;

[0034] Step S5. Adjust the resource allocation of each R&D process, use the function fitting tool to output the function of the comprehensive quality parameters of the project as a function of various resources, perform linear programming on the change function with the total resource quantity as the constraint condition, and use the planning results to allocate resources for the next cycle.

[0035] Further, step S1 includes:

[0036] Step S11. Divide the space in the cloud to build a test supervision platform, which consists of a user interface, a database, a data packet capture software and a data analysis software, and is used to uniformly manage the input and output data of each R&D process;

[0037] Step S12. Install communication management software in each R&D device to receive test data sent from the cloud, and upload the data output by the R&D device to the test supervision platform. At the same time, set up receiving software in the cloud to capture the output data packets sent by the R&D device at the end of the channel.

[0038] Further, step S2 includes:

[0039] Step S21. Close the software functions developed by each R&D process to form a separate test unit, which is numbered as {W1, W2, ..., Wn}, where n is the number of R&D processes and Wn is the test unit corresponding to the nth R&D process;

[0040] Step S22. The platform provides test data to each test unit, and inserts an identifier into the test data according to a first rule, wherein the first rule is as follows:

[0041] Preset position function F(x), x is the identifier number, let G(x)=TD[F(x)·c x ], where G(x) is the insertion interval function, c is the preset amplification coefficient, c>0 and c≠1, TD is the rounding function, parse the test data packet, and insert the identifier after the G(1), G(2), ..., G(x)th data in the data packet, respectively, until there is not enough data in the data packet;

[0042] Step S23. Send the test data to the test unit, collect the output data in the test unit, capture the output identifier in the output data, record the location of each identifier, and generate the function g(x) with the identifier sequence number as the independent variable and the identifier location as the dependent variable;

[0043] The test delay and data redundancy are calculated according to the distribution calculation method. The distribution calculation method is as follows:

[0044]

[0045] Where tc represents the test delay, Q represents the data redundancy, x0 represents the number of identifiers inserted in the test data, v represents the cloud communication speed, G(i) and g(i) represent the function values ​​of G(x) and g(x) respectively when the independent variable is i;

[0046] Step S24. Record one-dimensional test parameters of each test unit, wherein the one-dimensional test parameters include test delay and data redundancy.

[0047] Further, step S3 includes:

[0048] Step S31. Combine all test units to obtain the functionally complete delivery software, use the concurrency testing technology to test all test items of the delivery software, and obtain the two-dimensional test parameters of the delivery software, the two-dimensional test parameters include: data throughput, data utilization, maximum concurrency, test delay and data redundancy;

[0049] Step S32. According to the common items in the two-dimensional test parameters and the one-dimensional test parameters, the correlation between the delivered software and each test unit is established, and the equation is listed: A1·r1+A2·r2+…+An·rn=A0;

[0050] Among them, A1, A2, ...An represent the one-dimensional test parameters in the 1st, 2nd, ...nth test units respectively, A0 represents the corresponding two-dimensional test parameters, r1, r2, ...rn represent the correlation coefficients of the 1st, 2nd, ...nth test units respectively, substitute the test results of n consecutive tests, and solve the value of the correlation coefficient;

[0051] Step S33. After obtaining the correlation coefficient between each test unit and the delivered software, for each untested item in the one-dimensional test parameter, list the equation E1·r1+E2·r2+…+En·rn=E0, where E1, E2, …En represent the test items not involved in the 1st, 2nd, …nth test units respectively, and E0 represents the test parameters for the test items in the two-dimensional test parameters. Substitute the two-dimensional test parameters in the n tests and output the calculation results as the parameters of the untested items in each test unit.

[0052] Further, step S4 includes:

[0053] Step S41. Calculate the comprehensive quality parameter P of each R&D process, where P=a1·h1+a2·h2+…+am·hm, where m represents the number of test items, a1, a2, …am represent the preset weights of the 1st, 2nd, …mth test items respectively, and h1, h2, …hm represent the test parameters of the test unit of the R&D process in the 1st, 2nd, …mth test items respectively;

[0054] Step S42: For each R&D process, when the quality parameter is lower than a preset threshold, an alarm is sent to the control center of the corresponding R&D process.

[0055] Further, step S5 includes:

[0056] Step S51. Adjust the various types of R&D resources allocated to the R&D process, record the comprehensive quality parameters of the R&D process before and after the adjustment, and use the function fitting tool to generate the influence function H(g1, g2, ..., gt) with the number of each R&D resource as the independent variable and the comprehensive quality parameter as the dependent variable, where t represents the number of types of R&D resources, and g1, g2, ..., gt represent the number of the 1st, 2nd, ...tth types of R&D resources respectively;

[0057] Step S52. Perform linear programming on the sum of the influence functions of each R&D process and output the maximum value of the total influence function:

[0058]

[0059] Among them, Max represents the maximum value of the objective function, St represents the constraint condition, Y(g1,g2,…,gt) represents the total impact function of the delivered software, H1, H2,…Hn represent the impact functions corresponding to the 1st, 2nd,…nth R&D processes respectively, g1 max 、g2 max ,…gt max They represent the maximum investment amount of R&D resources of categories 1, 2, …t respectively;

[0060] Step S53. Allocate resources to each R&D process according to the allocation quantity of each R&D resource when the total impact function takes the maximum value in step S52.

[0061] Compared with the prior art, the beneficial effects achieved by the present invention are:

[0062] The present invention closes each R&D process, inserts placeholder data into the R&D test data according to supplementary rules, uploads output data in the R&D process, calculates one-dimensional test parameters of the software according to changes in the placeholders in the output data, and can timely discover problems and bottlenecks in the process through the cloud, thereby optimizing the process and improving development efficiency and quality.

[0063] The present invention can generate test cases to perform concurrency testing on software, obtain two-dimensional test results of the software, calculate the correlation coefficient between the two-dimensional test results of the software and each one-dimensional test parameter, and estimate the two-dimensional test data of the research and development process, thereby realizing supervision and early warning of the research and development process, saving the number of test items, reducing the overall test time, and improving test efficiency, which is helpful to realize quantitative supervision of the development process and improve research and development efficiency.

[0064] The present invention can use the actual usage requirements of the software as constraints to obtain the resource allocation conditions when the software quality takes the maximum value, and allocate resources according to the calculation results, which can reduce the cost of the research and development stage, avoid additional expenditure due to waste or shortage of resources, maximize the input-output ratio, and improve the return on software research and development. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0066] Figure 1 It is a structural schematic diagram of a digital supervision system for software development based on a cloud platform of the present invention;

[0067] Figure 2 It is a schematic diagram of the steps of a digital supervision method for software development based on a cloud platform of the present invention. DETAILED DESCRIPTION

[0068] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0069] See also Figure 1 ,The present invention provides a technical solution: a digital supervision system for software development based on a cloud platform, comprising: a cloud communication module, a research and development container module, a test estimation module, a quantitative recording module and a resource management module;

[0070] The cloud communication module is used to build a cloud test supervision platform, uniformly manage the data required in each R&D process, and recover the output data from the R&D process. At the same time, it builds a communication channel between the software R&D equipment and the cloud platform, and sets a data unpacking program at the end of the communication channel to capture the output data packets of the R&D equipment;

[0071] The cloud communication module includes: a platform storage unit and a data communication unit;

[0072] The platform storage unit is used to divide space in the cloud to build a data processing platform and database to uniformly manage test data;

[0073] The data communication unit is composed of communication management software installed in the R&D equipment and the cloud server, and is used to manage the communication process between the equipment and the server.

[0074] The R&D container module is used to add identification placeholders in the test data provided to the R&D process in the cloud according to a specific rule, capture identification placeholders in the output data of the R&D process, calculate the one-dimensional test parameters of the software in the development process according to the changes in the position and proportion of the placeholders in the output data, store the test results in the cloud database, and keep them updated in real time;

[0075] The R&D container module includes: a process closure unit, a data circulation unit and a one-dimensional test unit;

[0076] The process closing unit is used to close the software functions involved in each R&D process to form a separate test unit;

[0077] The data circulation unit is used to insert placeholders according to preset rules in the input data provided to the R&D process in the cloud, and to capture the identification placeholders in the output data uploaded to the cloud;

[0078] The one-dimensional test unit is used to calculate the one-dimensional test parameters of the software, including software test delay and R&D data redundancy, according to the position and proportion of the placeholders in the output data.

[0079] The test estimation module is used to generate test cases to perform concurrent testing on the R&D software, analyze the test feedback data in the cloud platform, obtain the two-dimensional test results of the software, the two-dimensional test results include all the test items in the one-dimensional test parameters, calculate the correlation coefficient between the two-dimensional test results of the software and the one-dimensional test parameters in each process, and estimate all the test item parameters of the process according to the correlation coefficient;

[0080] The test estimation module includes: a concurrent test unit and a related estimation unit;

[0081] The concurrent testing unit is used to perform concurrent testing on the final software to obtain all test items of the software, which are recorded as two-dimensional test results;

[0082] The correlation estimation unit is used to fit the correlation between the one-dimensional test parameters and the two-dimensional test results, and to construct the correlation to estimate the test item parameters in the process.

[0083] The quantitative recording module is used to obtain the comprehensive quality parameters of the process according to the preset weighted calculation method based on all the test item parameters of each R&D process, draw the relationship function between the comprehensive quality parameters and the data resource consumption, and remind the person in charge of the corresponding R&D process through the cloud platform when the decline rate of the relationship function is higher than the threshold;

[0084] The quantitative recording module includes: a quality assessment unit and a supervision alarm unit;

[0085] The quality assessment unit is used to obtain the comprehensive quality parameters of the process according to a preset weighted calculation method;

[0086] The supervision alarm unit is used to alarm the corresponding process when the function decline rate between the comprehensive quality parameter and the data resource consumption is higher than a threshold.

[0087] The resource management module is used to adjust the resource allocation of the R&D process, recalculate the comprehensive quality weight of the process according to the adjusted test data, fit the joint influence function of the comprehensive quality weight and various types of resources, and use the actual usage requirements of the software as constraints to obtain the resource allocation conditions when the influence function takes the minimum value, and allocate R&D resources according to the calculation results of the resource allocation conditions.

[0088] The resource management module includes: an impact fitting unit, a constraint planning unit and a resource allocation unit;

[0089] The impact fitting unit is used to adjust the resource allocation amount of each R&D process during the R&D process, obtain the comprehensive quality parameters of the process, and calculate the impact function of the comprehensive quality parameters of each R&D process with the resource allocation amount;

[0090] The constraint programming unit is used to calculate the maximum value of the influence function under the constraint condition by taking the actual use requirements of the software as the constraint condition;

[0091] The resource allocation unit is used to adjust the resource allocation of each R&D process according to the linear programming result and send the allocation plan to the management department.

[0092] like Figure 2 As shown, a digital supervision method for software development based on a cloud platform includes the following steps:

[0093] Step S1. Build a test supervision platform in the cloud to uniformly manage the data required in each R&D process, and set up communication management software in the R&D equipment and the cloud server, which can capture input and output data packets;

[0094] Step S1 includes:

[0095] Step S11. Divide the space in the cloud to build a test supervision platform, which consists of a user interface, a database, a data packet capture software and a data analysis software, and is used to uniformly manage the input and output data of each R&D process;

[0096] Step S12. Install communication management software in each R&D device to receive test data sent from the cloud, and upload the data output by the R&D device to the test supervision platform. At the same time, set up receiving software in the cloud to capture the output data packets sent by the R&D device at the end of the channel.

[0097] Step S2. Each software function involved in the R&D process is closed as an independent test unit. The platform inserts an identifier into the data provided to each test unit according to the first rule, and captures the identifier in the output data at the end of the channel, analyzes the distribution of the identifier in the input and output data, and obtains a one-dimensional test parameter;

[0098] Step S2 includes:

[0099] Step S21. Close the software functions developed by each R&D process to form a separate test unit, which is numbered as {W1, W2, ..., Wn}, where n is the number of R&D processes and Wn is the test unit corresponding to the nth R&D process;

[0100] Step S22. The platform provides test data to each test unit, and inserts an identifier into the test data according to a first rule, wherein the first rule is as follows:

[0101] Preset position function F(x), x is the identifier number, let G(x)=TD[F(x)·c x ], where G(x) is the insertion interval function, c is the preset amplification coefficient, c>0 and c≠1, TD is the rounding function, parse the test data packet, and insert the identifier after the G(1), G(2), ..., G(x)th data in the data packet, respectively, until there is not enough data in the data packet;

[0102] Step S23. Send the test data to the test unit, collect the output data in the test unit, capture the output identifier in the output data, record the location of each identifier, and generate the function g(x) with the identifier sequence number as the independent variable and the identifier location as the dependent variable;

[0103] The test delay and data redundancy are calculated according to the distribution calculation method. The distribution calculation method is as follows:

[0104]

[0105] Where tc represents the test delay, Q represents the data redundancy, x0 represents the number of identifiers inserted in the test data, v represents the cloud communication speed, G(i) and g(i) represent the function values ​​of G(x) and g(x) respectively when the independent variable is i;

[0106] Step S24. Record one-dimensional test parameters of each test unit, wherein the one-dimensional test parameters include test delay and data redundancy.

[0107] Step S3. Perform concurrency testing on all test items of the delivered software, record the test results as two-dimensional test parameters, calculate the correlation between the one-dimensional test parameters and the two-dimensional test parameters, and estimate the parameters of the untested items in each test unit based on the correlation;

[0108] Step S3 includes:

[0109] Step S31. Combine all test units to obtain the functionally complete delivery software, use the concurrency testing technology to test all test items of the delivery software, and obtain the two-dimensional test parameters of the delivery software, the two-dimensional test parameters include: data throughput, data utilization, maximum concurrency, test delay and data redundancy;

[0110] Step S32. According to the common items in the two-dimensional test parameters and the one-dimensional test parameters, the correlation between the delivered software and each test unit is established, and the equation is listed: A1·r1+A2·r2+…+An·rn=A0;

[0111] Among them, A1, A2, ...An represent the one-dimensional test parameters in the 1st, 2nd, ...nth test units respectively, A0 represents the corresponding two-dimensional test parameters, r1, r2, ...rn represent the correlation coefficients of the 1st, 2nd, ...nth test units respectively, substitute the test results of n consecutive tests, and solve the value of the correlation coefficient;

[0112] Step S33. After obtaining the correlation coefficient between each test unit and the delivered software, for each untested item in the one-dimensional test parameter, list the equation E1·r1+E2·r2+…+En·rn=E0, where E1, E2, …En represent the test items not involved in the 1st, 2nd, …nth test units respectively, and E0 represents the test parameters for the test items in the two-dimensional test parameters. Substitute the two-dimensional test parameters in the n tests and output the calculation results as the parameters of the untested items in each test unit.

[0113] Step S4. Preset the weight of each test item, perform weighted calculation according to the test item parameters corresponding to each R&D process, obtain the comprehensive quality parameter corresponding to the R&D process, and issue an alarm when the comprehensive quality parameter is lower than the threshold;

[0114] Step S4 includes:

[0115] Step S41. Calculate the comprehensive quality parameter P of each R&D process, where P=a1·h1+a2·h2+…+am·hm, where m represents the number of test items, a1, a2, …am represent the preset weights of the 1st, 2nd, …mth test items respectively, and h1, h2, …hm represent the test parameters of the test unit of the R&D process in the 1st, 2nd, …mth test items respectively;

[0116] Step S42: For each R&D process, when the quality parameter is lower than a preset threshold, an alarm is sent to the control center of the corresponding R&D process.

[0117] Step S5. Adjust the resource allocation of each R&D process, use the function fitting tool to output the function of the comprehensive quality parameters of the project as a function of various resources, perform linear programming on the change function with the total resource quantity as the constraint condition, and use the planning results to allocate resources for the next cycle.

[0118] Step S5 includes:

[0119] Step S51. Adjust the various types of R&D resources allocated to the R&D process, record the comprehensive quality parameters of the R&D process before and after the adjustment, and use the function fitting tool to generate the influence function H(g1, g2, ..., gt) with the number of each R&D resource as the independent variable and the comprehensive quality parameter as the dependent variable, where t represents the number of types of R&D resources, and g1, g2, ..., gt represent the number of the 1st, 2nd, ...tth types of R&D resources respectively;

[0120] Step S52. Perform linear programming on the sum of the influence functions of each R&D process and output the maximum value of the total influence function:

[0121]

[0122] Among them, Max represents the maximum value of the objective function, St represents the constraint condition, Y(g1,g2,…,gt) represents the total impact function of the delivered software, H1, H2,…Hn represent the impact functions corresponding to the 1st, 2nd,…nth R&D processes respectively, g1 max 、g2 max ,…gt max They represent the maximum investment amount of R&D resources of categories 1, 2, …t respectively;

[0123] Step S53. Allocate resources to each R&D process according to the allocation quantity of each R&D resource when the total impact function takes the maximum value in step S52.

[0124] Embodiment: The R&D project has 3 R&D processes and 5 detection items. The detection results of the one-dimensional test parameters in the R&D processes are [0.6, 200], [0.2, 100] and [1.1, 120] respectively, and the two-dimensional test parameters of the delivered software are [2.1, 1000, 50, 10, 800]. The first two test items are the same as the one-dimensional test parameters. After three consecutive tests, the remaining three parameters of each R&D process are estimated, and the estimation results are [20, 3, 250], [30, 5, 400] and [45, 8, 500] respectively. The preset weights are used for calculation, and the comprehensive quality parameters of the three R&D processes are 17, 24 and 14 respectively. The comprehensive quality parameter of R&D process 3 is lower than the threshold value 15, and an alarm is issued for R&D process 3.

[0125] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0126] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A digital supervision method for software development based on a cloud platform, characterized in that: The method comprises the following steps: Step S1. Build a test supervision platform in the cloud to uniformly manage the data required in each R&D process, and set up communication management software in the R&D equipment and the cloud server, which can capture input and output data packets; Step S2. Each software function involved in the R&D process is closed as an independent test unit. The platform inserts an identifier into the data provided to each test unit according to the first rule, and captures the identifier in the output data at the end of the channel, analyzes the distribution of the identifier in the input and output data, and obtains a one-dimensional test parameter; Step S3. Perform concurrency testing on all test items of the delivered software, record the test results as two-dimensional test parameters, calculate the correlation between the one-dimensional test parameters and the two-dimensional test parameters, and estimate the parameters of the untested items in each test unit based on the correlation; Step S4. Preset the weight of each test item, perform weighted calculation according to the test item parameters corresponding to each R&D process, obtain the comprehensive quality parameter corresponding to the R&D process, and issue an alarm when the comprehensive quality parameter is lower than the threshold; Step S5. Adjust the resource allocation of each R&D process, use the function fitting tool to output the function of the comprehensive quality parameters of the project as a function of various resources, perform linear programming on the change function with the total resource quantity as the constraint condition, and use the planning results to allocate resources for the next cycle.

2. According to a cloud platform-based digital software development supervision method according to claim 1, it is characterized by: Step S1 includes: Step S11. Divide the space in the cloud to build a test supervision platform, which consists of a user interface, a database, a data packet capture software and a data analysis software, and is used to uniformly manage the input and output data of each R&D process; Step S12. Install communication management software in each R&D device to receive test data sent from the cloud, upload the data output by the R&D device to the test supervision platform, and set up receiving software in the cloud to capture the output data packets sent by the R&D device at the end of the channel; Step S2 includes: Step S21. Close the software functions developed by each R&D process to form a separate test unit, which is numbered as {W1, W2, ..., Wn}, where n is the number of R&D processes and Wn is the test unit corresponding to the nth R&D process; Step S22. The platform provides test data to each test unit, and inserts an identifier into the test data according to a first rule, wherein the first rule is as follows: Preset position function F(x), x is the identifier number, let G(x)=TD[F(x)·c x ], where G(x) is the insertion interval function, c is the preset amplification coefficient, c>0 and c≠1, TD is the rounding function, parse the test data packet, and insert the identifier after the G(1), G(2), ..., G(x)th data in the data packet, respectively, until there is not enough data in the data packet; Step S23. Send the test data to the test unit, collect the output data in the test unit, capture the output identifier in the output data, record the location of each identifier, and generate the function g(x) with the identifier sequence number as the independent variable and the identifier location as the dependent variable; The test delay and data redundancy are calculated according to the distribution calculation method. The distribution calculation method is as follows: ; Where tc represents the test delay, Q represents the data redundancy, x0 represents the number of identifiers inserted in the test data, v represents the cloud communication speed, G(i) and g(i) represent the function values ​​of G(x) and g(x) respectively when the independent variable is i; Step S24. Record one-dimensional test parameters of each test unit, wherein the one-dimensional test parameters include test delay and data redundancy.

3. A digital supervision method for software development based on a cloud platform according to claim 2, characterized in that: Step S3 includes: Step S31. Combine all test units to obtain the functionally complete delivery software, use the concurrency testing technology to test all test items of the delivery software, and obtain the two-dimensional test parameters of the delivery software, the two-dimensional test parameters include: data throughput, data utilization, maximum concurrency, test delay and data redundancy; Step S32. According to the common items in the two-dimensional test parameters and the one-dimensional test parameters, the correlation between the delivered software and each test unit is established, and the equation is listed: A1·r1+A2·r2+…+An·rn=A0; Among them, A1, A2, ...An represent the one-dimensional test parameters in the 1st, 2nd, ...nth test units respectively, A0 represents the corresponding two-dimensional test parameters, r1, r2, ...rn represent the correlation coefficients of the 1st, 2nd, ...nth test units respectively, substitute the test results of n consecutive tests, and solve the value of the correlation coefficient; Step S33. After obtaining the correlation coefficient between each test unit and the delivered software, for each untested item in the one-dimensional test parameter, list the equation E1·r1+E2·r2+…+En·rn=E0, where E1, E2, …En represent the test items not involved in the 1st, 2nd, …nth test units respectively, and E0 represents the test parameters for the test items in the two-dimensional test parameters. Substitute the two-dimensional test parameters in the n tests and output the calculation results as the parameters of the untested items in each test unit.

4. A digital supervision method for software development based on a cloud platform according to claim 3, characterized in that: Step S4 includes: Step S41. Calculate the comprehensive quality parameter P of each R&D process, where P=a1·h1+a2·h2+…+am·hm, where m represents the number of test items, a1, a2, …am represent the preset weights of the 1st, 2nd, …mth test items respectively, and h1, h2, …hm represent the test parameters of the test unit corresponding to the R&D process in the 1st, 2nd, …mth test items respectively; Step S42: For each R&D process, when the quality parameter is lower than a preset threshold, an alarm is sent to the control center of the corresponding R&D process.

5. A digital supervision method for software development based on a cloud platform according to claim 4, characterized in that: Step S5 includes: Step S51. Adjust the various types of R&D resources allocated to the R&D process, record the comprehensive quality parameters of the R&D process before and after the adjustment, and use the function fitting tool to generate the influence function H(g1, g2, ..., gt) with the number of each R&D resource as the independent variable and the comprehensive quality parameter as the dependent variable, where t represents the number of types of R&D resources, and g1, g2, ..., gt represent the number of the 1st, 2nd, ...tth types of R&D resources respectively; Step S52. Perform linear programming on the sum of the influence functions of each R&D process and output the maximum value of the total influence function: ; Among them, Max represents the maximum value of the objective function, St represents the constraint condition, Y(g1,g2,…,gt) represents the total impact function of the delivered software, H1, H2,…Hn represent the impact functions corresponding to the 1st, 2nd,…nth R&D processes respectively, g1 max 、g2 max ,…gt max They represent the maximum investment amount of R&D resources of categories 1, 2, …t respectively; Step S53. Allocate resources to each R&D process according to the allocation quantity of each R&D resource when the total impact function takes the maximum value in step S52.

6. A digital software development supervision system based on a cloud platform, characterized in that: The system includes the following modules: cloud communication module, R&D container module, test estimation module, quantification recording module and resource management module; The cloud communication module is used to build a cloud test supervision platform, uniformly manage the data required in each R&D process, and recover the output data from the R&D process. At the same time, it builds a communication channel between the software R&D equipment and the cloud platform, and sets a data unpacking program at the end of the communication channel to capture the output data packets of the R&D equipment; The R&D container module is used to add identification placeholders according to a regular pattern in the test data provided to the R&D process in the cloud, capture identification placeholders in the output data of the R&D process, calculate the one-dimensional test parameters of the software in the development process according to the changes in the position and proportion of the placeholders in the output data, store the test results in the cloud database, and keep them updated in real time; The test estimation module is used to generate test cases to perform concurrent testing on the R&D software, analyze the test feedback data in the cloud platform, obtain the two-dimensional test results of the software, the two-dimensional test results include all the test items in the one-dimensional test parameters, calculate the correlation coefficient between the two-dimensional test results of the software and the one-dimensional test parameters in each process, and estimate all the test item parameters of the process according to the correlation coefficient; The quantitative recording module is used to obtain the comprehensive quality parameters of the process according to the preset weighted calculation method based on all the test item parameters of each R&D process, draw the relationship function between the comprehensive quality parameters and the data resource consumption, and remind the person in charge of the corresponding R&D process through the cloud platform when the decline rate of the relationship function is higher than the threshold; The resource management module is used to adjust the resource allocation of the R&D process, recalculate the comprehensive quality weight of the process according to the adjusted test data, fit the joint influence function of the comprehensive quality weight and various types of resources, and use the actual usage requirements of the software as constraints to obtain the resource allocation conditions when the influence function takes the maximum value, and allocate R&D resources according to the calculation results of the resource allocation conditions.

7. A digital software development supervision system based on a cloud platform according to claim 6, characterized in that: The cloud communication module includes: a platform storage unit and a data communication unit; The platform storage unit is used to divide space in the cloud to build a data processing platform and database to uniformly manage test data; The data communication unit is composed of communication management software installed in the R&D equipment and in the cloud server, and is used to manage the communication process between the equipment and the server.

8. A digital software development supervision system based on a cloud platform according to claim 7, characterized in that: The R&D container module includes: a process closure unit, a data circulation unit and a one-dimensional test unit; The process closing unit is used to close the software functions involved in each R&D process to form a separate test unit; The data circulation unit is used to insert placeholders according to preset rules in the input data provided to the R&D process in the cloud, and to capture the identification placeholders in the output data uploaded to the cloud; The one-dimensional test unit is used to calculate the one-dimensional test parameters of the software, including software test delay and R&D data redundancy, according to the position and proportion of the placeholders in the output data.

9. A cloud platform-based digital software development supervision system according to claim 8, characterized in that: The test estimation module includes: a concurrent test unit and a related estimation unit; The concurrent testing unit is used to perform concurrent testing on the final software to obtain all test items of the software, which are recorded as two-dimensional test results; The correlation estimation unit is used to fit the correlation between the one-dimensional test parameters and the two-dimensional test results, and to construct the correlation to estimate the test item parameters within the process; The quantitative recording module includes: a quality assessment unit and a supervision alarm unit; The quality assessment unit is used to obtain the comprehensive quality parameters of the process according to a preset weighted calculation method; The supervision alarm unit is used to alarm the corresponding process when the function decline rate between the comprehensive quality parameter and the data resource consumption is higher than a threshold.

10. A digital software development supervision system based on a cloud platform according to claim 9, characterized in that: The resource management module includes: an impact fitting unit, a constraint planning unit and a resource allocation unit; The impact fitting unit is used to adjust the resource allocation amount of each R&D process during the R&D process, obtain the comprehensive quality parameters of the process, and calculate the impact function of the comprehensive quality parameters of each R&D process with the resource allocation amount; The constraint programming unit is used to calculate the maximum value of the influence function under the constraint condition by taking the actual use requirements of the software as the constraint condition; The resource allocation unit is used to adjust the resource allocation of each R&D process according to the linear programming result and send the allocation plan to the management department.

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