Airborne software configuration item division scheme evaluation method

Abstract

The invention belongs to the field of airborne computer software engineering, and provides an airborne software configuration item division scheme evaluation method, which comprises the following steps of: constructing a hierarchical evaluation index system comprising a first-level index and a second-level index; obtaining initial scores given by the plurality of to-be-evaluated division schemes; calculating the score value of each scheme after cleaning by adopting a quartile method; determining the comprehensive weight of each secondary index by adopting an analytic hierarchy process based on the index system; constructing an evaluation matrix based on the cleaned score values, and calculating the close degree of each division scheme and the positive / negative ideal solution by combining the comprehensive weight and adopting a TOPSIS method; and selecting the scheme with the highest close degree as an optimal division scheme. According to the method, scientization, quantification and reproducible evaluation of airborne software configuration item division are realized, the full-life-cycle engineering cost is remarkably reduced, and the technical problems of frequent maintenance, high change cost and long development cycle caused by the fact that traditional airborne software configuration item division depends on artificial experience can be solved.

Description

Technical Field

[0001] This invention belongs to the field of airborne computer software engineering and relates to an evaluation method for airborne software configuration item partitioning schemes. This method is applicable to the scientific, objective, and reproducible comprehensive evaluation and selection of multiple candidate configuration item partitioning schemes in the absence of quantitative decision-making basis. Background Technology

[0002] With the development of artificial intelligence, cloud computing, and big data technologies, modern airborne software systems are becoming increasingly complex and their scale continues to expand. Software Configuration Items (SCIs), as the smallest logical unit requiring configuration management throughout the software lifecycle, directly impact software development efficiency, integration difficulty, test coverage, and subsequent maintenance costs through their proper allocation.

[0003] Currently, the classification of airborne software configuration items mainly relies on the personal experience or past project experience of project leaders or senior engineers, lacking a unified, quantifiable, and verifiable evaluation mechanism. This subjective classification method often results in configuration items being too coarse or too fine-grained, leading to numerous software maintenance, upgrade, and frequent changes due to evaluation deviations in the configuration item classification scheme. This results in persistently high costs in terms of human resources, time, financial resources, and quality for software development.

[0004] Although multi-attribute decision-making methods such as the Analytic Hierarchy Process (AHP) and Top-Level Structural Analysis (TOPSIS) have been applied in other engineering fields, AHP is susceptible to the influence of expert subjective judgment, while TOPSIS itself does not include a weight generation mechanism. Furthermore, existing methods do not construct dedicated indicator systems for the development constraints specific to airborne software (such as software size, software functionality, developer, support requirements, reusability, and criticality level), making them difficult to apply directly. Therefore, there is an urgent need to provide an evaluation method to address these issues. Summary of the Invention

[0005] To address the technical problems caused by frequent software maintenance, difficult upgrades, high change costs, and extended development cycles resulting from manually dividing airborne software configuration items based on historical project experience, this invention discloses an evaluation method for airborne software configuration item division schemes. The method includes the following steps: S1. Construct a hierarchical evaluation index system for evaluating the airborne software configuration item division scheme. The evaluation index system includes primary indicators and secondary indicators belonging to each primary indicator. S2. Obtain initial scores from multiple domain experts based on the secondary indicators in the evaluation index system for several classification schemes to be evaluated. S3. For the initial scores of each division scheme to be evaluated under each secondary indicator, outliers are removed using the quartile method, and the arithmetic mean of the remaining initial scores is used as the cleaned score of the division scheme to be evaluated under the secondary indicator. S4. Based on the hierarchical evaluation index system, the comprehensive weight of each secondary index is determined by the analytic hierarchy process. S5. Construct an evaluation matrix based on the cleaned score, perform weighted standardization on the evaluation matrix according to the comprehensive weight, and calculate the closeness of each partition scheme to be evaluated to the positive ideal solution and the negative ideal solution based on the processing result using the TOPSIS method. S6. Sort the division schemes to be evaluated according to the proximity, and select the division scheme with the highest proximity as the optimal division scheme.

[0006] Furthermore, in step S1, the primary indicators include development, integration, and operation and maintenance; The secondary metrics attributed to the development include software functionality, software size, developer, reuse plan, and documentation that needs to be written separately; Secondary metrics belonging to the integration include interface considerations; The secondary indicators belonging to the aforementioned operations and maintenance include the host machine and target machine, the protection plan, criticality, and the need for separate control.

[0007] Furthermore, in step S3, outliers are removed using the quartile method, including: S31. Sort the expert scores of all domains for each sub-indicator under each secondary indicator for each sub-scheme to be evaluated in ascending order, and obtain the lower quartile Q1 and upper quartile Q3 according to the sorting. S32. Remove scores for each indicator that are less than Q1 or greater than Q3; S33. Calculate the arithmetic mean of the remaining initial scores, and use it as the cleaned score of the division scheme to be evaluated under the secondary index.

[0008] Further, in step S4, based on the hierarchical evaluation index system, the comprehensive weight of each secondary index is determined using the analytic hierarchy process (AHP), including: S41. Construct a judgment matrix for the primary indicators, and have domain experts perform pairwise comparisons and assign values ​​using the 1–9 scale method, and calculate the weight vector of each primary indicator using the square root method. S42. Construct judgment matrices for each secondary indicator under each primary indicator, and have domain experts perform pairwise comparisons and assign values ​​using the 1–9 scaling method. Then, use the square root method to calculate the local weight of each secondary indicator relative to its primary indicator. S43. Multiply the local weight of each secondary indicator by the weight of its corresponding primary indicator to obtain the comprehensive weight of that secondary indicator.

[0009] Furthermore, step S4 also includes: A consistency check is performed on the weight vector and the local weights respectively. When the consistency ratio CR < 0.1, the weight vector and the local weights are taken as the final weights.

[0010] Further, in step S5, an evaluation matrix is ​​constructed based on the cleaned score, and the evaluation matrix is ​​weighted and standardized according to the comprehensive weight, including: S51. Based on the cleaned score values ​​of each division scheme to be evaluated under each secondary indicator, construct an evaluation matrix, standardize each element in the evaluation matrix, and eliminate the influence of dimensions to obtain a standardized matrix. S52. Multiply each column of the standardized matrix by the comprehensive weight of the corresponding secondary indicator to obtain the weighted standardized evaluation matrix.

[0011] Further, in step S5, based on the processing results, the TOPSIS method is used to calculate the closeness between each partitioning scheme to be evaluated and the positive and negative ideal solutions, including: S53. The TOPSIS method is used to determine the positive ideal solution and the negative ideal solution. The positive ideal solution is the maximum standardized weighted value of each secondary index among all the division schemes to be evaluated, and the negative ideal solution is the minimum standardized weighted value. S54. Calculate the Euclidean distance D from each partitioning scheme to be evaluated to the positive ideal solution. + The Euclidean distance D to the negative ideal solution - ; S55. According to the formula K=D - / (D + + D - ), calculate the proximity K value for each partition scheme to be evaluated.

[0012] Furthermore, the method can be applied to the decision-making of airborne software configuration items in avionics systems, flight control software, mission management systems, or integrated avionics platforms, supporting the scientific determination of configuration item boundaries in the early stages of software development and reducing maintenance, change, and integration costs throughout the entire lifecycle.

[0013] Compared with the prior art, the beneficial effects that at least one technical solution adopted in the embodiments of this specification can achieve include at least: 1. Traditional methods mainly rely on personal experience or analogies from historical projects, which can easily lead to problems such as strong subjectivity in configuration item allocation and lack of scientific data quantification support. This invention, by constructing a hierarchical evaluation index system, transforms the allocation decision into a multi-dimensional, quantifiable comprehensive evaluation problem, significantly improving the objectivity and standardization of the allocation process; 2. By introducing the quartile method to remove outliers from the initial scores of experts in multiple fields, and using the arithmetic mean of the remaining scores as the cleaned score, subjective bias and abnormal interference in expert scores are effectively suppressed, the distortion of evaluation results by extreme scores from individual experts is avoided, and the robustness and credibility of the data are improved. 3. The Analytic Hierarchy Process (AHP) was used to determine the weights of the primary indicators and the local weights of the secondary indicators, and the comprehensive weights were synthesized by multiplication. This not only preserved the experts' judgment on the importance of different dimensions, but also ensured that the weight system matched the actual needs of the entire life cycle of airborne software development (development-integration-operation and maintenance), thus realizing the scientific allocation and hierarchical integration of indicator weights. 4. By organically integrating data cleaning (QM), scientific weighting (AHP), and top-first-situation evaluation (TOPSIS), a complete and reproducible QM-AHP-TOPSIS closed-loop evaluation model is formed. By calculating the closeness of each partitioning scheme to the ideal solution, the models achieve quantitative ranking of the schemes, supporting optimal scheme selection and providing clear decision-making basis for engineering practice. 5. The method of this invention scientifically determines the boundaries of configuration items in the early stages of software development, effectively reducing problems such as frequent changes, integration difficulties, and insufficient test coverage caused by improper partitioning, thereby reducing manpower, time and costs, and improving the level of software engineering management; In summary, the method of this invention achieves scientific, quantitative, and reproducible evaluation of airborne software configuration item division, significantly reducing the total life cycle engineering cost. Moreover, this method is not only applicable to typical airborne software systems such as avionics, flight control, and mission management, but can also be extended to software configuration management in other fields. Attached Figure Description

[0014] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 This is a flowchart of the evaluation method for the airborne software configuration item partitioning scheme of the present invention; Figure 2 Schematic diagram of the evaluation method for partitioning airborne software configuration items; Figure 3 A diagram illustrating the hierarchical evaluation indicator system; Figure 4 This is a schematic diagram of the principle of the quartile method. Detailed Implementation

[0016] The embodiments of this application will now be described in detail with reference to the accompanying drawings.

[0017] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. This application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be noted that, in the absence of conflict, the following embodiments and features of the embodiments can be combined with each other. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0018] This invention discloses an evaluation method for airborne software configuration item partitioning schemes. This method achieves objective ranking and optimization of multiple partitioning schemes by constructing a dedicated index system, introducing the Quartile Method (QM) to clean expert scores, and integrating the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) comprehensive evaluation method. This solves the problems of strong subjectivity in traditional AHP and lack of weight allocation mechanism in TOPSIS in multi-objective decision evaluation of airborne software configuration item partitioning schemes, thus improving the scientific advantage of airborne software configuration item partitioning scheme evaluation.

[0019] Specifically, see Figure 1 and Figure 2 As shown, the method includes the following steps: S1. Construct a hierarchical evaluation index system for evaluating the airborne software configuration item division scheme. The evaluation index system includes primary indicators and secondary indicators belonging to each primary indicator. S2. Obtain initial scores from multiple domain experts based on the secondary indicators in the evaluation index system for several classification schemes to be evaluated. S3. For the initial scores of each division scheme to be evaluated under each secondary indicator, outliers are removed using the quartile method, and the arithmetic mean of the remaining initial scores is used as the cleaned score of the division scheme to be evaluated under the secondary indicator. S4. Based on the hierarchical evaluation index system, the comprehensive weight of each secondary index is determined by the analytic hierarchy process. S5. Construct an evaluation matrix based on the cleaned score, perform weighted standardization on the evaluation matrix according to the comprehensive weight, and calculate the closeness of each partition scheme to be evaluated to the positive ideal solution and the negative ideal solution based on the processing result using the TOPSIS method. S6. Sort the division schemes to be evaluated according to the proximity, and select the division scheme with the highest proximity as the optimal division scheme.

[0020] In one embodiment of step S1, see Figure 3 As shown, based on the characteristics of the entire software development lifecycle, a primary indicator system is established with development (A1), integration (A2), and operation and maintenance (A3) as the main dimensions. Furthermore, based on domain requirements and engineering experience, the primary indicators of development (A1), integration (A2), and operation and maintenance (A3) are further refined to form secondary indicators under each primary indicator.

[0021] For example, secondary indicators belonging to development (A1) include software functionality (B1), software scale (B2), developer (B3), reuse plan (B4), and documentation requiring separate writing (B5); secondary indicators belonging to integration (A2) include interface considerations (B6); secondary indicators belonging to operations and maintenance (A3) include host and target machines, assurance plans, criticality, and the need for separate control of host and target machines (B7), assurance plans (B8), criticality (B9), and the need for separate control, etc. (B9). 10 ).

[0022] The indicators in the above hierarchical evaluation indicator system are shown in Table 1 below: Table 1: Hierarchical Evaluation Index System for Airborne Software Configuration Item Classification

[0023] In one embodiment of step S3, outliers are removed using the quartile method, including: S31. Sort the expert scores of all domains for each sub-indicator under each secondary indicator for each sub-scheme to be evaluated in ascending order, and obtain the lower quartile Q1 and upper quartile Q3 according to the sorting. S32. Remove scores for each indicator that are less than Q1 or greater than Q3; S33. Calculate the arithmetic mean of the remaining initial scores, and use it as the cleaned score of the division scheme to be evaluated under the secondary index.

[0024] In one embodiment of step S4, based on the hierarchical evaluation index system, the comprehensive weight of each secondary index is determined using the analytic hierarchy process (AHP), including: S41. Construct a judgment matrix for the primary indicators, and have domain experts perform pairwise comparisons and assign values ​​using the 1–9 scale method, and calculate the weight vector of each primary indicator using the square root method. S42. Construct judgment matrices for each secondary indicator under each primary indicator, and have domain experts perform pairwise comparisons and assign values ​​using the 1–9 scaling method. Then, use the square root method to calculate the local weight of each secondary indicator relative to its primary indicator. S43. Multiply the local weight of each secondary indicator by the weight of its corresponding primary indicator to obtain the comprehensive weight of that secondary indicator.

[0025] Furthermore, step S4 also includes: A consistency check is performed on the weight vector and the local weights respectively. When the consistency ratio CR < 0.1, the weight vector and the local weights are taken as the final weights.

[0026] In practical implementation, a judgment matrix is ​​constructed and the weights of each level of indicators are calculated. Based on the airborne software configuration items, the evaluation indicator system is divided, requiring the construction of weight judgment matrices for both primary and secondary indicators. This invention illustrates the method for obtaining the weight vector of the primary indicator as an example: (a) Establish the judgment matrix D for the primary indicators: , = , (1); In equation (1), n ​​is the number of indicators. This represents the scale value (values ​​1-9) of the i-th primary indicator relative to the j-th primary indicator, which is compared and assigned using the 1-9 level scaling method shown in Table 2.

[0027] Table 2: 1-9 level scaling method

[0028] The steps for determining matrix weights using the square root method and the computer-configured software weights are as follows: Step 1: Calculate the row product of the weight judgment matrix.

[0029] (2), where, Let be the row product of the i-th primary index.

[0030] Step 2: Take the nth root of the row product to obtain the eigenvector.

[0031] ( 1≤i≤n (3), Let be the feature vector of the i-th primary indicator; Step 3: Normalize the feature vectors to obtain the weight vectors.

[0032] ( 1≤i≤n (4), Let be the weight vector of the i-th primary indicator.

[0033] Step 4: Calculate the weight vector DW of the secondary indicators.

[0034] (5); Step 5: Calculate the largest eigenvalue λ based on the weight vector from Step 4. max 。

[0035] (1≤i≤n)(6); Step Six: Based on the number of indicators, the largest eigenvalue λ max Calculate the consistency index CI and consistency ratio CR, and verify each matrix: (7); For matrices of order 1 to 9, the average random consistency index RI is shown in Table 3 below: Table 3: Average Random Consistency Index of Weight Judgment Matrices of Orders 1-9

[0036] Step 7: Consistency check.

[0037] The consistency rule for the weight judgment matrix is ​​CR < 0.10, when CR When 1.0, then d in equation (1) D ij The value has a logical error and needs to be reassigned until the consistency check passes.

[0038] (II) Processing of data samples using the quartile method for scheme evaluation For each partitioning scheme to be evaluated, multiple domain experts scored each secondary indicator, and the score for each secondary indicator was obtained using the quartile method. The quartile method involves sorting a set of data from smallest to largest, then dividing the data sample into four equal parts using three points, each containing 25% of the data. The lower quartile (Q1, 25th percentile), median (Q2, 50th percentile), and upper quartile (Q3, 75th percentile) are calculated. The principle of the quartile method is as follows: Figure 4 As shown. A dataset is arranged in ascending order, denoted as X1, X2, ..., X... n Where n is the number of data points, calculated as follows: (8); (9); (10); Suppose that z experts have scored indicator B in a certain plan, forming z initial indicator sample sets, b = {b1, b2, b3, ..., b...} z The selected indicator sample set b' = { b'1, b'2, b'} is... 3. .... b' p}, z Then we have: b' y = b y Q1≤ b y ≤Q3, 1 ≤ y ≤ p (11); Using the quartile method to filter index data, the data to be evaluated for index B in a certain grading scheme is as follows: .

[0039] In one embodiment of step S5, an evaluation matrix is ​​constructed based on the post-cleaning score, and the evaluation matrix is ​​weighted and standardized according to the comprehensive weight, including: S51. Based on the cleaned score values ​​of each division scheme to be evaluated under each secondary indicator, construct an evaluation matrix, standardize each element in the evaluation matrix, and eliminate the influence of dimensions to obtain a standardized matrix. S52. Multiply each column of the standardized matrix by the comprehensive weight of the corresponding secondary indicator to obtain the weighted standardized evaluation matrix.

[0040] In another embodiment of step S5, based on the processing results, the TOPSIS method is used to calculate the proximity of each partition scheme to be evaluated to the positive and negative ideal solutions, including: S53. The TOPSIS method is used to determine the positive ideal solution and the negative ideal solution. The positive ideal solution is the maximum standardized weighted value of each secondary index among all the division schemes to be evaluated, and the negative ideal solution is the minimum standardized weighted value. S54. Calculate the Euclidean distance D from each partitioning scheme to be evaluated to the positive ideal solution. + The Euclidean distance D to the negative ideal solution - ; S55. According to the formula K=D - / (D + + D - ), calculate the proximity K value for each partition scheme to be evaluated.

[0041] In practice, the calculation process for the closeness of each partitioning scheme to be evaluated to the positive and negative ideal solutions is as follows: Step 1: Construct evaluation matrix B using the scores of the secondary indicators of all schemes (obtained by the quartile method).

[0042] (13); In equation (13), m The number of solutions x This represents the number of indicators at the lowest level in the indicator system.

[0043] To minimize differences in the dimensions and magnitudes of the evaluation indicators and ensure data comparability and calculation rationality, the evaluation matrix is ​​standardized. In this method, the larger the value of each indicator in B, the better the corresponding indicator performs. All indicators are positive indicators. The following formula (14) is used to standardize the indicators: (14); The processed standardized evaluation matrix is ​​B~: (15).

[0044] Step 2: Calculate the score for each partitioning scheme to be evaluated using the weight matrix and the evaluation matrix B~.

[0045] Based on the AHP method, the weighted evaluation matrix F is obtained by determining the indicator weights and the standardized evaluation matrix. (16); In the airborne software configuration item classification index system, based on the primary indexes of development (A1), integration (A2), and operation and maintenance (A3) and the secondary indexes (B1, B2, ..., B...),... 10 The membership relationship of ) and the index weights in equation (16) ( ) is a secondary indicator in the indicator system. ( ) and their respective primary indicators ( The product of ).

[0046] Step 3: Define the positive ideal solution based on the weighted evaluation matrix of equation (16). and negative ideal solution ,include: Positive Ideal Solution The set of maximum values ​​for each indicator across all options can be represented as: (17); Negative ideal solution The set of minimum values ​​for each indicator across all options can be represented as: (18); Step 4: Determine the distance from the proposed solution to the ideal solution. No. y The first distance between the proposed solution and the ideal solution is: (1≤y≤m, 1≤z≤x)(19); No. y The distance between each solution and the negative ideal solution is: (1≤y≤m, 1≤z≤x)(20); Step 5: Sort the solutions. Calculate the first solution using two distances. y The coefficient of closeness K between the proposed solution and the ideal solution y The higher the degree of similarity, the more ideal the solution; select the solution with the highest degree of similarity as the final solution.

[0047] (1≤y≤m)(21); Furthermore, the method can be applied to the decision-making of airborne software configuration items in avionics systems, flight control software, mission management systems, or integrated avionics platforms, supporting the scientific determination of configuration item boundaries in the early stages of software development and reducing maintenance, change, and integration costs throughout the entire lifecycle.

[0048] To better illustrate the method of this invention, a detailed explanation of the evaluation method is provided using the evaluation of a software configuration item division scheme for a project in the airborne field as an example. During the project planning phase, three alternative schemes (S1, S2, S3) were provided for the division of software configuration items. Each of the three schemes was evaluated and scored by six experts, and the sample of each indicator is shown in Table 4 below.

[0049] Table 4: Sample Values ​​of Indicators for Different Schemes of Software Configuration Item Division for a Certain Airborne System

[0050] Step 1. Calculate the weights of the two levels of indicators in the evaluation indicator system. Based on the specific requirements for software configuration items in a certain airborne system project, a first-level indicator evaluation matrix D is constructed according to equation (1). ; Based on equations (2) to (5), the weight W of D is obtained. A : ; Based on equations (6) to (7), we obtain =3.0055, CR=0.0053<1, therefore D passed the matrix consistency check and meets the requirements.

[0051] Similarly, the weights of each secondary indicator in the secondary evaluation matrices A1, A2, and A3 are calculated: ; ; ; Among them, the largest eigenvalue =5.0268, =1, =4.2026, the consistency test results are CR1=0.0053, CR2=0.0053, CR3=0.0053, and A1, A2, and A3 all pass the consistency test.

[0052] The weights of the two levels of indicators for evaluating the software configuration items of a certain airborne system are shown in Table 5 below: Table 5: Weight Values ​​of the Evaluation Index System for Configuration Item Division Scheme

[0053] Step 2. Initial sample processing using the quartile method Based on equations (8) to (12), the indicators B1 to B1 in schemes 1 to 3 in Table 4 are... 10 The expert scoring data was analyzed using the quartile method. The sample values ​​of the indicator data after quartile processing are shown in Table 6 below: Table 6: Values ​​of each indicator in the configuration item partitioning scheme after quartile processing

[0054] Step 3. TOPSIS Assessment Score The data sample in Table 6 is standardized using equations (13) to (15) to obtain the standardized evaluation matrix B. ; According to equation (16), the weighted matrix for the allocation item partitioning scheme is obtained from the standardized evaluation matrix B~. : ; Based on equations (17) and (18), the ideal solution for evaluating the allocation item partitioning scheme is obtained. and : ; ; The Euclidean distances for each scheme are obtained according to equations (19) and (20). and : =0.1902, =0.3411; =0.1362 = 0.3493; = 0.4152 = 0.1302; The closeness degree K is obtained according to Equation (21).

[0055] K1 = 0.6420, K2 = 0.7194, K3 = 2387; The ranking of the closeness degrees of the evaluation of the partitioning scheme of a certain airborne system software configuration item is: K3 < K1 < K2. Therefore, Scheme 2 is the optimal scheme.

[0056] It should be noted that the above division of the first-level indicators and the second-level indicators is set according to the specific requirements of the evaluation of airborne software configuration items; if this invention is applied to other technical fields, the index system can be adjusted accordingly according to the actual engineering characteristics of this field.

[0057] The embodiments of this invention have achieved the following technical effects: 1. Traditional methods mainly rely on personal experience or analogy of historical projects, which easily lead to unreasonable configuration item granularity, high module coupling degree, and chaotic interfaces. By constructing a hierarchical evaluation index system, this invention transforms the partitioning decision into a multi-dimensional and quantifiable comprehensive evaluation problem, significantly improving the objectivity and standardization of the partitioning process; 2. By introducing the Quartile Method to eliminate outliers from the initial scores of multiple domain experts and using the arithmetic mean of the remaining scores as the cleaned score value, the subjective deviation and abnormal interference in the expert scores are effectively suppressed, avoiding the distortion of the evaluation results caused by the extreme scores of individual experts, and improving the data robustness and credibility; 3. Using the Analytic Hierarchy Process (AHP) to determine the weights of the first-level indicators and the local weights of the second-level indicators respectively, and synthesizing the comprehensive weights through multiplication, not only retains the experts' judgments on the importance of different dimensions, but also ensures that the weight system matches the actual needs of the entire life cycle of airborne software development (development - integration - operation and maintenance), achieving the scientific allocation and hierarchical integration of the index weights; 4. Organically integrating data cleaning (QM), scientific weight assignment (AHP), and multi-scheme ranking (TOPSIS) to form a complete QM-AHP-TOPSIS closed-loop evaluation model that is reproducible and verifiable. By calculating the closeness degrees of each partitioning scheme to the ideal solution, the quantitative ranking of the schemes is achieved, supporting the decision-making of scheme selection, and providing a clear decision-making basis for engineering practice; 5. The method of this invention scientifically determines the configuration item boundaries in the initial stage of software development, effectively reducing problems such as frequent later changes, integration difficulties, and insufficient test coverage caused by improper partitioning, thereby reducing labor, time, and financial costs, and improving the level of software engineering management;[[ID=二十九]] In summary, the method of this invention achieves scientific, quantitative, and reproducible evaluation of airborne software configuration item partitioning, significantly reducing the total lifecycle engineering cost. Furthermore, this method is not only applicable to typical airborne software systems such as avionics, flight control, and mission management, but can also be extended to software configuration management processes in other fields. In this embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the aforementioned evaluation method for any of the airborne software configuration item partitioning schemes.

[0058] Specifically, the computer device can be a computer terminal, a server, or a similar computing device.

[0059] In this embodiment, a computer-readable storage medium is provided, which stores a computer program that executes any of the above-described airborne software configuration item partitioning scheme evaluation methods.

[0060] Specifically, computer-readable storage media, including both permanent and non-permanent, removable and non-removable media, can store information using any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer-readable storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable storage media does not include transient media, such as modulated data signals and carrier waves.

[0061] Obviously, those skilled in the art should understand that the modules or steps of the above-described embodiments of the present invention can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Optionally, they can be implemented using computer-executable program code, thereby storing them in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those presented here, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the embodiments of the present invention are not limited to any particular hardware and software combination.

[0062] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, various modifications and variations can be made to the embodiments of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An in-flight software configuration item partitioning scheme evaluation method, characterized in that, include: A hierarchical evaluation index system is constructed to evaluate the airborne software configuration item division scheme. The evaluation index system includes primary indicators and secondary indicators belonging to each primary indicator. Obtain initial scores from multiple domain experts based on the secondary indicators in the evaluation index system for several classification schemes to be evaluated; For each classification scheme to be evaluated under each secondary indicator, the quartile method is used to remove outliers, and the arithmetic mean of the remaining initial scores is used as the cleaned score of the classification scheme to be evaluated under the secondary indicator. Based on the hierarchical evaluation index system, the comprehensive weight of each secondary index is determined by the analytic hierarchy process. An evaluation matrix is ​​constructed based on the cleaned score. The evaluation matrix is ​​then weighted and standardized according to the comprehensive weight. Based on the processing results, the TOPSIS method is used to calculate the closeness of each partition scheme to be evaluated to the positive and negative ideal solutions. The partitioning schemes to be evaluated are ranked according to their proximity, and the partitioning scheme with the highest proximity is selected as the optimal partitioning scheme.

2. The method of claim 1, wherein, The primary metrics include development, integration, and operation and maintenance; The secondary metrics attributed to the development include software functionality, software size, developer, reuse plan, and documentation that needs to be written separately; Secondary metrics belonging to the integration include interface considerations; The secondary indicators belonging to the aforementioned operations and maintenance include the host machine and target machine, the protection plan, criticality, and the need for separate control.

3. The method of claim 1, wherein: Outlier removal is achieved using the quartile method, including: Arrange the expert scores of all domains for each sub-indicator under each secondary indicator in ascending order for each sub-indicator to be evaluated, and obtain the lower quartile Q1 and upper quartile Q3 according to the sorting. Remove initial scores that are less than Q1 or greater than Q3; The arithmetic mean of the remaining initial scores is used as the cleaned score of the partition scheme to be evaluated under the secondary index.

4. The method of claim 1, wherein: Based on the hierarchical evaluation index system, the comprehensive weight of each secondary index is determined using the analytic hierarchy process (AHP), including: A judgment matrix is ​​constructed for the primary indicators. Domain experts perform pairwise comparisons and assign values ​​using the 1–9 scale method, and the weight vector of each primary indicator is calculated using the square root method. For each primary indicator, a judgment matrix is ​​constructed for each secondary indicator. Domain experts perform pairwise comparisons and assign values ​​using the 1–9 scaling method, and the root method is used to calculate the local weight of each secondary indicator relative to its primary indicator. The local weight of each secondary indicator is multiplied by the weight of its corresponding primary indicator to obtain the overall weight of that secondary indicator.

5. The evaluation method for airborne software configuration item partitioning scheme according to claim 4, characterized in that, Also includes: A consistency check is performed on the weight vector and the local weights respectively. When the consistency ratio CR < 0.1, the weight vector and the local weights are taken as the final weights.

6. The evaluation method for airborne software configuration item partitioning scheme according to claim 1, characterized in that, An evaluation matrix is ​​constructed based on the post-cleaning score, and the evaluation matrix is ​​then weighted and standardized according to the comprehensive weights, including: Based on the cleaned scores of each classification scheme to be evaluated under each secondary indicator, an evaluation matrix is ​​constructed. Each element in the evaluation matrix is ​​standardized to eliminate the influence of dimensions and obtain a standardized matrix. Multiply each column of the standardized matrix by the comprehensive weight of the corresponding secondary indicator to obtain the weighted standardized evaluation matrix.

7. The evaluation method for airborne software configuration item partitioning scheme according to claim 1 or 6, characterized in that, Based on the processing results, the TOPSIS method was used to calculate the closeness of each partitioning scheme to be evaluated to the positive and negative ideal solutions, including: The TOPSIS method is used to determine the positive and negative ideal solutions. The positive ideal solution is the maximum standardized weighted value of each secondary index among all the partition schemes to be evaluated, and the negative ideal solution is the minimum standardized weighted value. Euclidean distance D of each evaluated partitioning scheme to the positive ideal solution is calculated respectively + Euclidean distance D of each evaluated partitioning scheme to the negative ideal solution is calculated respectively - ; According to the formula K = D - / (D + + D - ), the closeness K value of each division scheme to be evaluated is calculated.

8. The evaluation method for airborne software configuration item partitioning scheme according to claim 1, characterized in that, The method is applied to the decision-making of airborne software configuration items in avionics systems, flight control software, mission management systems, or integrated avionics platforms, supporting the scientific determination of configuration item boundaries in the early stages of software development.

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