CN113177746A - Software quality comprehensive evaluation method aiming at life cycle evaluation field - Google Patents
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Abstract
The invention discloses a software quality comprehensive evaluation method aiming at the field of life cycle evaluation, which comprises the following steps: and (3) cutting the functional attributes of the software: cutting a software quality model by combining the life cycle evaluation field and the ISO/IEC25010 software quality characteristics and considering the characteristics of the software, user requirements and quality requirements; determining an evaluation index system of software in the life cycle evaluation field: based on the life cycle evaluation process, comparing the novel life cycle evaluation software to be evaluated with the developed and mature life cycle evaluation software, and constructing a software evaluation index system; comprehensively evaluating the software quality: determining the evaluation index weight of life cycle evaluation software based on an analytic hierarchy process and information entropy; after the weight is determined, performing relevance analysis on different evaluation indexes, determining the relevance between the different evaluation indexes, and constructing a maximum spanning tree of the evaluation indexes; and scoring each index, and performing comprehensive evaluation by using a maximum spanning tree.
Description
Technical Field
The invention relates to a software quality comprehensive evaluation method aiming at the life cycle evaluation field, belonging to the software quality evaluation field.
Background
In order to solve the problem that the whole process of life cycle evaluation usually takes a lot of time to collect and process data, colleges and enterprises are more and more interested in developing LCA tools, which are increasing, such as GaBi in germany, Simapro in netherlands, ebance in china, bes in the usa, Ecopro in switzerland and the like, and part of software can be integrated with other software, so that LCA is more widely applied. At present, GaBi software and SimaPro software are used frequently, and the calculation results of the GaBi software and the SimaPro software are very close to each other in most cases, but the difference of the two cases is large. The two types of software databases have wide range, can calculate the product life cycle environmental influences of multiple fields and regions, but for a specific product, the actual state of the product can be influenced by the region where the product is located, the field where the product is located, the change of the product surrounding environment, the product iteration and the like, in the field of electromechanical products, the product deviation can be influenced by universality software such as GaBi, SimaPro and the like on the product life cycle evaluation environment, and partial software has no evaluation method required by a researcher, so that the actual requirements of the researcher cannot be met.
Disclosure of Invention
The invention aims to provide guidance for improvement of a life cycle evaluation tool of an electromechanical product and provides a software quality comprehensive evaluation method aiming at the life cycle evaluation field.
The invention is realized by the following technical scheme:
a software quality comprehensive evaluation method aiming at the life cycle evaluation field is characterized by comprising the following steps:
(1) tailoring software functional attributes
Cutting a software quality model by combining the life cycle evaluation field and the ISO/IEC25010 software quality characteristics and considering the characteristics of the software, user requirements and quality requirements;
(2) evaluation index system for determining software in life cycle evaluation field
Based on the life cycle evaluation process, comparing the novel life cycle evaluation software to be evaluated with the developed and mature life cycle evaluation software, and constructing a software evaluation index system;
(3) comprehensive evaluation of software quality
Firstly, determining the evaluation index weight of life cycle evaluation software based on an analytic hierarchy process and information entropy; after the weight is determined, performing relevance analysis on different evaluation indexes, determining the relevance between the different evaluation indexes, and constructing a maximum spanning tree of the evaluation indexes; the expert scores each index through the use of the tool, and uses the maximum spanning tree to carry out comprehensive evaluation, thereby providing guidance for the improvement of the life cycle evaluation tool of the electromechanical product.
Further, the method for comprehensively evaluating software quality in the life cycle evaluation field is characterized in that the software quality model is cut in the step (1), and the method specifically comprises the following steps:
combining a software function attribute model, considering the actual characteristics and attributes of life cycle evaluation software, establishing a life cycle evaluation software function attribute cutting model, and cutting the software function attributes, specifically comprising the following steps:
selecting quality characteristics meeting the requirements of the software life cycle evaluation field through functional attribute cutting, and extracting essential and same attributes, namely standard extraction characteristics, common to software in the life cycle evaluation field; for abstract extraction characteristics, firstly learning and integrating life cycle field knowledge, analyzing by combining expert opinions and user survey condition information and a plurality of software in the field, transversely comparing the same points and different points of life cycle evaluation software, upgrading the essential and same characteristics into a uniform concept, and determining the relationship between attributes.
Further, the method for comprehensively evaluating software quality in the life cycle evaluation field is characterized in that the evaluation index system for determining software in the life cycle evaluation field in the step (2) is specifically as follows:
determining indexes in an evaluation index system of software in the life cycle evaluation field by adopting an GQM (Goal-Question-Metric) method in combination with the actual situation in the life cycle evaluation field and the experience and knowledge of researchers to determine the evaluation index system of a life cycle evaluation tool; GQM the core of the model mainly includes three layers, which are respectively the target, question and measure, the target layer designates a target for the measure object; the problem layer is a method that describes those targets to be evaluated mainly by using a set of problems; the measurement layer is mainly used for quantitatively answering questions proposed by the question layer, and the result is a combination of subjective and objective;
the life cycle evaluation process is mainly divided into four stages, including determination of targets and ranges, list analysis, influence evaluation and result interpretation; before the product is evaluated by using the life cycle evaluation tool, the related parameters of the product are determined, including the system boundary of the product and the specific inventory data of the product, and based on the determination, the environmental impact of the product is analyzed and evaluated by using the related life cycle evaluation tool; the determination of the life cycle evaluation software index can dynamically compare mature life cycle evaluation software with novel electromechanical customized life cycle evaluation software, consider the functional attribute of LCA software, cut the LCA software, and determine an evaluation index system of the novel customized LCA software by adopting an GQM method;
before evaluating and analyzing novel customized software of an electromechanical product, mature LCA software which can be clearly compared and the electromechanical product which needs to be evaluated by the two types of software are required to be determined, and the list data of the electromechanical product is determined by actual research and experimental measurement modes, so that the accuracy and the integrity of the data are ensured; after performing the manifest analysis, the determination of the evaluation index is mainly developed from:
1) list import: before modeling, system boundary and Life Cycle Inventory data (LCI) are required to be determined, a plurality of data importing methods exist, reasonable scores are given to some indexes of evaluation software by comparing Inventory data functions of the two types of software, measured answers are given according to actual evaluation of LCA researchers or related experts, and the evaluation indexes of the software are subdivided according to the measured answers of the experts and the functional attributes of the corresponding software;
2) evaluation of influence: firstly, determining a measurement target according to a method GQM, then providing a group of targeted questions for the measurement target, giving out a measurement answer through the answers of related personnel, and subdividing the evaluation index of the software according to the measurement answer of the related personnel and the functional attribute of the corresponding software;
3) result storage and interpretation: the result is interpreted as a measurement target, and from the aspects of the accuracy of the life cycle evaluation result, the uncertainty of data whether or not, and the like, a targeted problem is put forward, and related personnel are allowed to give a reasonable measurement value; and (4) aiming at result storage, and from the perspective of convenience of storage and calling, subdividing the evaluation indexes of the software according to the functional attributes of the software through the measurement answers of related personnel.
Further, the software quality comprehensive evaluation method aiming at the life cycle evaluation field is characterized in that,
the software quality comprehensive evaluation in the step (3) is as follows:
after obtaining the determined life cycle evaluation tool evaluation index, determining index weight based on an analytic hierarchy process and an entropy weight method, obtaining edge weight through correlation analysis among indexes, constructing a maximum spanning tree model among the indexes according to the edge weight, further performing scoring evaluation on the indexes of the life cycle evaluation tool, and determining a software quality evaluation flow;
firstly, the weight setting problem of software function attribute evaluation is solved by using an AHP method, and the calculation model is as follows:
1) building a hierarchical model
The construction of an evaluation index system comprises the steps of firstly determining a target layer of a decision problem, then decomposing the target layer into corresponding related standard layers, and decomposing the standard layers into corresponding index layers to establish an evaluation index system of the decision problem;
2) structural judgment matrix
aijUsed as an evaluation index, aijRepresenting important comparisons of element i and element j, comparing the indices on the same level, and constructing a comparison matrix according to equation (1):
3) calculating the weight of the index
The maximum eigenvalue and corresponding eigenvector of the matrix are obtained by the sum-product method, and each column of the matrix is normalized by equation (2):
the normalized matrix is summed row-by-row using equation (3):
normalizing vectors by equation (4)
The maximum eigenvalue is calculated by equation (5):
4) consistency check
After the weight of each index is calculated, in order to ensure the validity and scientificity of the final result, a consistency test needs to be carried out, and when a consistency test coefficient CR is less than 0.1, the consistency test coefficient CR is in accordance with the standard; the consistency test coefficient formula is shown as a formula (6); where CI is a general consistency index, found by equation (7), and RI is an average random consistency index:
5) on the basis of an analytic hierarchy process, introducing a concept of information entropy, calculating uncertainty of the index by using an entropy weight method, and improving distribution weight of the evaluation index through objective information entropy; the specific process is as follows:
a) determining a life cycle evaluation software evaluation index set U and an evaluation result set W
Setting a software evaluation index set U-U based on functional attributes as { U ═ U1,U2,…,UnIn which UiRepresenting a first-level index of the software, and satisfying that U is equal to U1∪U1∪…∪U1Each level of first-level index can correspond to specific function index of specific software, and U in each level of first-level index corresponds to specific function index of specific softwarei＝{Ui1,Ui2,…,UimAnd evaluating all aspects of the software by using the software at a uniform period to obtain an evaluation domain, namely an evaluation result set W of the software is { W ═ W }1,W2,…,WnDividing the software evaluation index evaluation into four levels A, B, C and D, wherein A represents that the software index is excellent in state, does not need to be optimized, completely meets the requirements of LCA researchers, and can provide corresponding reference and guidance for other software; the software index represented by B is good in state, can be simply optimized and adjusted, and basically meets the requirements of LCA researchers; c represents that the software index state is general, and the software is correspondingly optimized on the basis of considering the requirements of LCA researchers; d represents that the software index state is poor, and problems must be found and solved from each link of the software;
b) determining a blur matrix
And (3) establishing fuzzy mapping from W to U by knowing an evaluation index set U and a software evaluation result set W of the software, and generating a fuzzy mapping matrix T:
wherein, tijRepresents the evaluation result set WiBelongs to a U in an evaluation index set UiDegree of membership of, tij＝zijT, t represents the number of accesses to the evaluation index set U, zijRepresents the evaluation index UiIs evaluated as an evaluation grade WjThe number of times of (c); selecting a plurality of LCA research experts to form a software evaluation group by adopting a Delphi method and a data statistical method, and evaluating indexes;
c) index weighting based on information entropy
Through the above description of the information entropy, the determination process of each index weight considering the information entropy is as follows:
calculating the proportion of the ith evaluation index in the jth evaluation grade according to the constructed fuzzy matrix, wherein a calculation formula is as follows:
calculating the system entropy e of the ith index in the systemi：
Wherein n is the number of evaluation results, k is 1/lnn, and pijSatisfy the requirement of
And when p isijWhen 1, ei＝0；
Computing entropy weight v of ith index in systemi：
Wherein m represents the number of evaluation indexes to be evaluated, and an index weight vector in the weight coefficient method is obtained through the formula: v ═ V (V)1,V2,…,Vm) In the same way, the weight direction of other level indexes can be solved;
fourthly, calculating the comprehensive weight
Obtaining the entropy weight V (V) of all indexes according to the step III1,V2,...,Vm) The index weight determined according to AHP is a ═ a1,a1,…am) (ii) a Giving the comprehensive weight B of the indexiThe following were used:
finally, relevance analysis is carried out, wherein the relevance analysis is the dependence relationship among the research objects, and further exploration and research are carried out on the dependence relationship, so that a statistical analysis method for researching the relationship among the variables is adopted; the correlation coefficient is used for clarifying the strength of the correlation among variables, and the strength of the correlation among life cycle evaluation software indexes can be measured; for the index X and the index Y, the value range of the correlation coefficient is between [ -1,1] and the closer the value of the correlation coefficient is to 0, the weaker the correlation of the X and the Y is, otherwise, the stronger the correlation is; when the correlation coefficient is 1, the X and the Y are completely positive linear correlation, when the correlation coefficient is-1, the X and the Y are completely negative linear correlation, and when the correlation coefficient is 0, the X and the Y are completely independent and have no correlation;
the correlation coefficient calculation formula of the variable X and the variable Y is as follows:
wherein S isxyRepresents the covariance between variable X and variable Y, SxRepresents the standard deviation, S, of the variable XyRepresents the standard deviation of the variable Y;
the covariance between variable X and variable Y is calculated as follows:
the standard deviation calculation for variable X is as follows:
the standard deviation calculation formula for the variable Y is as follows:
the maximum spanning tree algorithm is almost the same as the minimum spanning tree algorithm, and the edge with the maximum edge weight is selected according to the basic algorithm; the Prim algorithm of the maximum spanning tree is adopted by the maximum spanning tree algorithm;
the Prim algorithm of the maximum spanning tree has the basic idea that the maximum spanning tree is constructed by progressive layer by layer according to the one-by-one communication of vertexes; set G ═<V,E,W>Is connected and has n vertexes, then a certain vertex u according to the specified condition from G0Starting from this, the side (u) with the highest degree of association is selected0V), then the secondary edge and the two vertices form part of the tree; TE is a set of the maximum spanning tree edges, U is a set of points, according to the thought, firstly, one point of G in the graph is selected arbitrarily as a starting point a, the point is added into the set U, then, another point b is found in G, so that the edge is the edge with the maximum weight in all edges related to a, at the moment, the point b is also added into the set U, and the edges (a and b) are added into the TE; by analogy, the current set is U ═ a, b }, another point c is found from G to enable the weight of the point c to any point in U to be maximum, at the moment, the point c is added into the set U, edges of the two points are placed into TE, and when all vertexes are added into U, a maximum spanning tree is constructed; in this case, there must be n-1 sides in TE, n vertices in U, and T ═ T<U,TE>A maximum spanning tree for G;
after the maximum spanning tree is formed, association exists among all nodes, a node comprehensive numerical value can be calculated through association degree calculation and node evaluation scores of all nodes, and the specific calculation process is shown as a formula 17:
wherein, PiIs the integrated evaluation value of the i-th index, BiAnd BjIs the weight of the ith and jth indices, AiAnd AjEvaluation values for the ith and jth indices, SijFor the correlation coefficient between the i-th and j-th indices, there is no correlation coefficient substituted by 0.
The invention has the technical effects that:
1. in the field of electromechanical products, universal software such as GaBi, SimaPro and the like can influence the product deviation on the environment of product life cycle evaluation, and part of the software has no evaluation method required by researchers, so that the actual requirements of the researchers cannot be met; therefore, the life cycle evaluation software evaluation index system based on the functional attributes is determined, the common functional attributes of the software are listed according to the ISO/IEC25010 software quality model, the life cycle evaluation software functional attributes which meet the requirements and can be quantized are cut, and the software evaluation index system is determined based on the life cycle evaluation process.
2. Researching a software quality comprehensive evaluation method to form a software evaluation flow; after the weight of each index is determined, the score of each software evaluation index is determined through expert scoring based on index relevance analysis and a maximum spanning tree algorithm, the total score of software evaluation is calculated, the difference between software and the limitation of the software function are analyzed, and guidance is provided for software improvement.
Drawings
Fig. 1 is a schematic diagram of a life cycle evaluation software functional attribute tailoring model.
Fig. 2 is a model view of GQM.
Fig. 3 is a schematic diagram of a life cycle evaluation software evaluation index determination process.
FIG. 4 is a software quality model to software evaluation index architecture.
Fig. 5 is a life cycle evaluation tool evaluation process diagram.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
Example 1.
A software quality comprehensive evaluation method aiming at the life cycle evaluation field comprises the following steps:
(1) tailoring software functional attributes
Cutting a software quality model by combining the life cycle evaluation field and the ISO/IEC25010 software quality characteristics and considering the characteristics of the software, user requirements and quality requirements;
(2) evaluation index system for determining software in life cycle evaluation field
Based on the life cycle evaluation process, comparing the novel life cycle evaluation software to be evaluated with the developed and mature life cycle evaluation software, and constructing a software evaluation index system;
(3) comprehensive evaluation of software quality
Firstly, determining the evaluation index weight of life cycle evaluation software based on an analytic hierarchy process and information entropy; after the weight is determined, performing relevance analysis on different evaluation indexes, determining the relevance between the different evaluation indexes, and constructing a maximum spanning tree of the evaluation indexes; the expert scores each index through using the tool, and uses the maximum spanning tree to carry out comprehensive evaluation, so as to provide guidance for the improvement of the life cycle evaluation tool of the electromechanical product;
the software quality model is cut in the step (1), and the method is specifically summarized as follows:
the software function is a defined object or characteristic action of a software system or a component, the ISO/IEC25010 software quality model describes software quality characteristics in detail and is also a functional attribute of the software, different software can determine the software functional attribute of the software according to the characteristics of the software, and determine an index for evaluating the software according to the functional attribute, so that the software is improved.
When the life cycle software functional attribute is evaluated, the software functional attribute which meets the requirement and can be quantified needs to be cut according to the software characteristic and the field of the software, and in addition, the software functional attribute also needs to consider the user group of the software, the software development process, the importance degree of software indexes and the like. Based on the above, in combination with the software function attribute model, the actual characteristics and attributes of the life cycle evaluation software are considered, a life cycle evaluation software function attribute clipping model is established, and the clipping of the software function attributes is specifically as follows: the quality characteristics meeting the requirements of the software life cycle field are selected through functional attribute clipping, and the essential and same attributes common to the software in the field, namely standard extraction characteristics, are extracted. For abstract extraction characteristics, firstly learning and integrating life cycle field knowledge, analyzing by combining information such as expert opinions, user survey conditions and the like and a plurality of software in the field, transversely comparing the same points and different points of life cycle evaluation software, upgrading the essential and same characteristics into a unified concept, and determining the relationship between attributes. The functional attribute tailoring model of the life cycle assessment software is shown in fig. 1.
The determination of the evaluation index system of the field software in the step (2) is specifically summarized as follows:
the index determination in the index system of the life cycle evaluation software needs to combine the actual situation in the life cycle evaluation field and the experience and knowledge of researchers, such as the modeling method of the software, the functional defect rate, etc., for which, the evaluation index system of the life cycle evaluation tool can be determined by adopting GQM (Goal-Question-Metric) method. GQM the core of the model consists essentially of three layers, targets, problems and metrics, respectively, as shown in FIG. 2. The target layer specifies a target for the metrology object, typically software-related activities related to those times, including the design, testing, etc. of the software; the problem layer is a method that describes those targets to be evaluated mainly by using a set of problems; the measurement layer is mainly used for carrying out quantitative answers on questions put forward by the question layer, and the result can be a combination of subjective and objective. The measure determined by the GQM method is directed to a particular problem, e.g., "time-wise" in efficiency characterization in the lifecycle assessment tool, specifically to the extent that response and processing time is satisfied. From this interpretation we first specify a goal for the metric object as "determining software computing power", and then set forth the relevant questions around that goal: "is there a specific steering technique for the data stream? "," is a computational backup employed? ", and finally gives specific measures of the temporal behavior of the software through the answers of the relevant personnel.
The life cycle evaluation process is mainly divided into four stages, including determination of targets and ranges, inventory analysis, impact evaluation and result interpretation. Before the product is evaluated by using the life cycle evaluation tool, relevant parameters of the product, including system boundary of the product, specific inventory data of the product and the like, need to be determined, and based on the relevant life cycle evaluation tool, the environmental influence of the product is analyzed and evaluated. Therefore, the life cycle process of determining the influence software evaluation index is mainly a list import process, an influence evaluation process and a result storage and interpretation process. The determination of the life cycle evaluation software index can dynamically compare mature life cycle evaluation software with novel electromechanical customized life cycle evaluation software, consider the functional attributes of LCA software, cut the LCA software, and determine an evaluation index system of the novel customized LCA software by adopting an GQM method, wherein the specific process is shown in FIG. 3. Therefore, the above implementation decomposes the measurement, expands the software quality model, and forms an evaluation index system of the life cycle evaluation software, as shown in fig. 4. The specific flow of the life cycle evaluation tool is shown in fig. 5.
Before evaluating and analyzing novel customized software of an electromechanical product, mature LCA software which can be clearly compared and the electromechanical product which needs to be evaluated by the two types of software are required to be determined, and the list data of the electromechanical product is determined through modes of actual research, experimental measurement and the like, so that the accuracy and the integrity of the data are ensured. After performing the manifest analysis, the determination process of the evaluation index is mainly developed from the following.
1) And importing the list. Before modeling, system boundary and Life Cycle Inventory data (LCI) should be clarified, and various data import methods exist, and certain indexes of evaluation software are reasonably scored by comparing Inventory data functions of the two types of software. Can "determine inventory import capability" be a metric objective for which to present a set of questions, can be "do there is good inventory pre-processing function? "," has a batch import function? And "does there be an import speed of mature LCA software? "etc., giving a metric answer based on the actual evaluation of the LCA researcher or related expert. And subdividing the evaluation index of the software according to the functional attribute of the software by the measurement answer of the expert.
2) And (5) influence evaluation. The purpose of the influence evaluation is to analyze the environmental emission of the product and quantify the environmental influence of the product, so that the product is better utilized for product improvement. Different evaluation methods will have different impact types and index parameters, and the purpose of the analysis will usually be different. Influence evaluation may relate to modeling modes, specific evaluation methods and the like, and a measurement target can be determined according to the GQM method, and then a set of targeted questions are provided for the measurement target, and a measurement answer is given through answers of related personnel. And subdividing the evaluation indexes of the software according to the functional attributes of the software by the metric answers of related personnel.
3) And storing and interpreting the result. The interpretation phase is a key step of LCA studies, which guarantees their quality and consistency and makes the work meaningful by providing results (drawing conclusions and interpreting limitations) consistent with established goals and ranges; the evaluation results need to be fully saved for the next use. Thus, the primary function of this part in the lifecycle is to provide interpretation and storage of the results. The result interpretation is taken as a measurement target, and a targeted problem can be put forward from the aspects of the accuracy of the life cycle evaluation result, the uncertainty of data whether or not to be considered and the like, and related personnel can give a reasonable measurement value; targeting the result storage, it is possible to start from the viewpoint of convenience of storage and recall. And subdividing the evaluation indexes of the software according to the functional attributes of the software by the metric answers of related personnel.
The software quality evaluation method study in the step (3) is specifically summarized as follows:
after the determined life cycle evaluation tool evaluation index is obtained, the index weight is determined based on an analytic hierarchy process and an entropy weight method, the edge weight is obtained through correlation analysis among the indexes, a maximum spanning tree model among the indexes is constructed according to the edge weight, and then the indexes of the life cycle evaluation tool are subjected to scoring evaluation, so that a software quality evaluation flow is determined.
Firstly, the weight setting problem of software function attribute evaluation is solved by using an AHP method, and the calculation model is as follows:
1) building a hierarchical model
The construction of the evaluation index system firstly determines a target layer of a decision problem and then decomposes the target layer into corresponding related standard layers. And decomposing the standard layer into corresponding index layers to establish an evaluation index system of the decision problem.
The indexing system of the decision problem program is a multi-stage system. There is a need to establish an indexing system from multiple aspects and from multiple perspectives to more fully reflect the advantages and disadvantages of each procedure. The construction of the index must follow feasibility, hierarchy, system and generality. The principle of combining points and features can ensure the objectivity and accuracy of the result.
2) Structural judgment matrix
aijUsed as an evaluation index, aijThe important comparison between the element i and the element j is shown, and specific numerical values and meanings are shown in Table 1. Indexes on the same level are compared, and a comparison matrix is constructed according to formula (1).
Table 1 judgment matrix element values
3) Calculating the weight of the index
And obtaining the maximum eigenvalue of the matrix and the corresponding eigenvector by a summation product method. Each column of the matrix is normalized by equation (2):
the normalized matrix is summed row-by-row using equation (3):
normalizing vectors by equation (4)
The maximum eigenvalue is calculated by equation (5):
4) consistency check
After calculating the weight of each index, in order to ensure the validity and scientificity of the final result, a consistency test needs to be performed. Generally, when the conformance test coefficient CR is <0.1, it indicates that the standard is met. The consistency test coefficient formula is shown in formula (6). Where CI is a general consistency index and is obtained by equation (7), and RI is an average random consistency index, corresponding to the order of the decision matrix, as shown in table 2 below.
TABLE 2 Standard values of the average random homogeneity index RI
Secondly, although the problem of weight distribution of evaluation indexes of life cycle evaluation software can be solved by the analytic hierarchy process, the analytic hierarchy process has obvious subjectivity, and the weight distribution is possibly limited due to excessive human participation. The specific process is as follows:
1) determining a life cycle evaluation software evaluation index set U and an evaluation result set W
Setting a software evaluation index set U-U based on functional attributes as { U ═ U1,U2,…,UnIn which UiRepresenting a first-level index of the software, and satisfying that U is equal to U1∪U1∪…∪U1Each level of first-level index can correspond to specific function index of specific software, and U in each level of first-level index corresponds to specific function index of specific softwarei＝{Ui1,Ui2,…,UimAnd evaluating all aspects of the software by using the software at a uniform period to obtain an evaluation domain, namely an evaluation result set W of the software is { W ═ W }1,W2,…,WnDividing the software evaluation index evaluation into four levels A, B, C and D, wherein A represents that the software index is excellent in state, does not need to be optimized, completely meets the requirements of LCA researchers, and can provide corresponding reference and guidance for other software; the software index represented by B is good in state, can be simply optimized and adjusted, and basically meets the requirements of LCA researchers; c represents that the software index state is general, and the software is correspondingly optimized on the basis of considering the requirements of LCA researchers; d represents that the software index state is poor and must be selected fromEvery link of the software finds the problem and solves it.
2) Determining a blur matrix
And (3) establishing fuzzy mapping from W to U by knowing an evaluation index set U and a software evaluation result set W of the software, and generating a fuzzy mapping matrix T:
wherein, tijRepresents the evaluation result set WiBelongs to a U in an evaluation index set UiDegree of membership of, tij＝zijT, t represents the number of accesses to the evaluation index set U, zijRepresents the evaluation index UiIs evaluated as an evaluation grade WjThe number of times. A Delphi method and a data statistical method are adopted, and a plurality of LCA research experts are selected to form a software evaluation group to evaluate indexes.
3) Index weighting based on information entropy
Through the above description of the information entropy, the determination process of each index weight considering the information entropy is as follows:
calculating the proportion of the ith evaluation index in the jth evaluation grade according to the constructed fuzzy matrix, wherein a calculation formula is as follows:
calculating the system entropy e of the ith index in the systemi：
Wherein n is the number of evaluation results, k is 1/lnn, and pijSatisfy the requirement of
And when p isijWhen 1, ei＝0。
Computing entropy weight v of ith index in systemi：
Wherein m represents the number of evaluation indexes to be evaluated, and an index weight vector in the weight coefficient method is obtained through the formula: v ═ V (V)1,V2,…,Vm). The weight vectors of other level indexes can be obtained in the same way.
Fourthly, calculating the comprehensive weight
Obtaining the entropy weight V (V) of all indexes according to the step III1,V2,...,Vm) The index weight determined according to AHP is a ═ a1,a1,…am). Giving the comprehensive weight B of the indexiThe following were used:
finally, relevance analysis is carried out, wherein the relevance analysis is the dependence relationship among the research objects, and further exploration and research are carried out on the dependence relationship, so that the relevance analysis is a statistical analysis method for researching the relationship among the variables. The correlation coefficient is used for clarifying the strength of the correlation between variables, and the strength of the correlation between the life cycle evaluation software indexes can be measured. For the index X and the index Y, the value range of the correlation coefficient is between [ -1,1] and the closer the value of the correlation coefficient is to 0, the weaker the correlation of the X and the Y is, otherwise, the stronger the correlation is; when the correlation coefficient is 1, the X and the Y are completely positive linear correlation, when the correlation coefficient is-1, the X and the Y are completely negative linear correlation, when the correlation coefficient is 0, the X and the Y are completely independent and have no correlation, and the specific numerical value range is shown in Table 3.
The correlation coefficient calculation formula of the variable X and the variable Y is as follows:
wherein S isxyRepresents the covariance between variable X and variable Y, SxRepresents the standard deviation, S, of the variable XyRepresents the standard deviation of the variable Y.
The covariance between variable X and variable Y is calculated as follows:
the standard deviation calculation for variable X is as follows:
the standard deviation calculation formula for the variable Y is as follows:
TABLE 3 evaluation index correlation coefficient ranking
The maximum spanning tree algorithm is almost the same as the minimum spanning tree algorithm, and the edge with the maximum edge weight is selected according to the basic algorithm. Constructing a maximum spanning tree is similar to constructing a minimum spanning tree and must tightly combine two issues. Firstly, selecting a weight value, and paying attention to avoid a loop when selecting the side with the maximum weight value; second, we select an edge that covers all points. The maximum spanning tree algorithm may be classified into a circle avoidance method, a circle breakage method, and a Prim algorithm of the maximum spanning tree. The Prim algorithm of the maximum spanning tree is adopted in the patent.
The Prim algorithm of the maximum spanning tree has the basic idea that the maximum spanning tree is constructed by progressive layer by layer according to the one-by-one communication of vertexes. Suppose G ═<V,E,W>Is connected and has n vertexes, then a certain vertex u according to the specified condition from G0Starting and selectingSelecting the side (u) with the maximum degree of association0V), then the secondary edge and the two vertices form part of the tree. TE is a set of the maximum spanning tree edges, U is a set of points, according to the thought, firstly, one point of G in the graph is selected arbitrarily as a starting point a, the point is added into the set U, then, another point b is found in G, so that the edge is the edge with the maximum weight in all edges related to a, at the moment, the point b is also added into the set U, and the edges (a and b) are added into the TE; and so on, the current set is U ═ a, b }, another point c is found from G to make the weight value from the point c to any point in U maximum, at this time, the point c is added into the set U, the edges of the two points are put into TE, and when all the vertexes are added into U, a maximum spanning tree is constructed. In this case, there must be n-1 sides in TE, n vertices in U, and T ═ T<U,TE>The maximum spanning tree for G.
After the maximum spanning tree is formed, association exists among all nodes, a node comprehensive numerical value can be calculated through association degree calculation and node evaluation scores of all nodes, and the specific calculation process is shown as a formula 17:
wherein, PiIs the integrated evaluation value of the i-th index, BiAnd BjIs the weight of the ith and jth indices, AiAnd AjEvaluation values for the ith and jth indices, SijFor the correlation coefficient between the i-th and j-th indices, there is no correlation coefficient substituted by 0.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.
Claims (4)
1. A software quality comprehensive evaluation method aiming at the life cycle evaluation field is characterized by comprising the following steps:
(1) tailoring software functional attributes
Cutting a software quality model by combining the life cycle evaluation field and the ISO/IEC25010 software quality characteristics and considering the characteristics of the software, user requirements and quality requirements;
(2) evaluation index system for determining software in life cycle evaluation field
Based on the life cycle evaluation process, comparing the novel life cycle evaluation software to be evaluated with the developed and mature life cycle evaluation software, and constructing a software evaluation index system;
(3) comprehensive evaluation of software quality
Firstly, determining the evaluation index weight of life cycle evaluation software based on an analytic hierarchy process and information entropy; after the weight is determined, performing relevance analysis on different evaluation indexes, determining the relevance between the different evaluation indexes, and constructing a maximum spanning tree of the evaluation indexes; and (4) the expert scores each index by using the tool, and performs comprehensive evaluation by using the maximum spanning tree.
2. The method for comprehensively evaluating software quality in the life cycle evaluation field according to claim 1, wherein the software quality model in step (1) is tailored as follows:
combining a software function attribute model, considering the actual characteristics and attributes of life cycle evaluation software, establishing a life cycle evaluation software function attribute cutting model, and cutting the software function attributes, specifically comprising the following steps:
selecting quality characteristics meeting the requirements of the software life cycle evaluation field through functional attribute cutting, and extracting essential and same attributes, namely standard extraction characteristics, common to software in the life cycle evaluation field; for abstract extraction characteristics, firstly learning and integrating life cycle field knowledge, analyzing by combining expert opinions and user survey condition information and a plurality of software in the field, transversely comparing the same points and different points of life cycle evaluation software, upgrading the essential and same characteristics into a uniform concept, and determining the relationship between attributes.
3. The method for comprehensively evaluating software quality in the life cycle evaluation field according to claim 1, wherein the evaluation index system for determining software in the life cycle evaluation field in the step (2) is specifically as follows:
determining indexes in an evaluation index system of software in the life cycle evaluation field by adopting an GQM method in combination with actual conditions in the life cycle evaluation field and experiences and knowledge of researchers;
the life cycle evaluation process is mainly divided into four stages, including determination of targets and ranges, list analysis, influence evaluation and result interpretation; before the product is evaluated by using the life cycle evaluation tool, the related parameters of the product are determined, including the system boundary of the product and the specific inventory data of the product, and based on the determination, the environmental impact of the product is analyzed and evaluated by using the related life cycle evaluation tool; the determination of the life cycle evaluation software index can dynamically compare mature life cycle evaluation software with novel electromechanical customized life cycle evaluation software, consider the functional attribute of LCA software, cut the LCA software, and determine an evaluation index system of the novel customized LCA software by adopting an GQM method;
before evaluating and analyzing novel customized software of an electromechanical product, mature LCA software which can be clearly compared and the electromechanical product which needs to be evaluated by the two types of software are required to be determined, and the list data of the electromechanical product is determined by actual research and experimental measurement modes, so that the accuracy and the integrity of the data are ensured; after performing the manifest analysis, the determination of the evaluation index is mainly developed from:
1) list import: before modeling, system boundary and life cycle list data are required to be determined, a plurality of data importing methods exist, certain indexes of evaluation software are reasonably scored by comparing the list data functions of the two types of software, a measured answer is given according to actual evaluation of LCA researchers or related experts, and the evaluation indexes of the software are subdivided according to the measured answer of the experts and the functional attributes of the corresponding software;
2) evaluation of influence: firstly, determining a measurement target according to a method GQM, then providing a group of targeted questions for the measurement target, giving out a measurement answer through the answers of related personnel, and subdividing the evaluation index of the software according to the measurement answer of the related personnel and the functional attribute of the corresponding software;
3) result storage and interpretation: the result is interpreted as a measurement target, and from the aspects of the accuracy of the life cycle evaluation result, the uncertainty of data whether or not, and the like, a targeted problem is put forward, and related personnel are allowed to give a reasonable measurement value; and (4) aiming at result storage, and from the perspective of convenience of storage and calling, subdividing the evaluation indexes of the software according to the functional attributes of the software through the measurement answers of related personnel.
4. The method for comprehensively evaluating the quality of software in the field of life cycle assessment according to claim 1,
the software quality comprehensive evaluation in the step (3) is as follows:
after obtaining the determined life cycle evaluation tool evaluation index, determining index weight based on an analytic hierarchy process and an entropy weight method, obtaining edge weight through correlation analysis among indexes, constructing a maximum spanning tree model among the indexes according to the edge weight, further performing scoring evaluation on the indexes of the life cycle evaluation tool, and determining a software quality evaluation flow;
firstly, the weight setting problem of software function attribute evaluation is solved by using an AHP method, and the calculation model is as follows:
1) building a hierarchical model
The construction of an evaluation index system comprises the steps of firstly determining a target layer of a decision problem, then decomposing the target layer into corresponding related standard layers, and decomposing the standard layers into corresponding index layers to establish an evaluation index system of the decision problem;
2) structural judgment matrix
aijUsed as an evaluation index, aijRepresenting an important comparison of element i and element j, comparing the indicators at the same level,and constructing a comparison matrix according to equation (1):
3) calculating the weight of the index
The maximum eigenvalue and corresponding eigenvector of the matrix are obtained by the sum-product method, and each column of the matrix is normalized by equation (2):
the normalized matrix is summed row-by-row using equation (3):
normalizing vectors by equation (4)
The maximum eigenvalue is calculated by equation (5):
4) consistency check
After the weight of each index is calculated, in order to ensure the validity and scientificity of the final result, a consistency test needs to be carried out, and when a consistency test coefficient CR is less than 0.1, the consistency test coefficient CR is in accordance with the standard; the consistency test coefficient formula is shown as a formula (6); where CI is a general consistency index, found by equation (7), and RI is an average random consistency index:
5) on the basis of an analytic hierarchy process, introducing a concept of information entropy, calculating uncertainty of the index by using an entropy weight method, and improving distribution weight of the evaluation index through objective information entropy; the specific process is as follows:
a) determining a life cycle evaluation software evaluation index set U and an evaluation result set W
Setting a software evaluation index set U-U based on functional attributes as { U ═ U1，U2，...，UnIn which UiRepresenting a first-level index of the software, and satisfying that U is equal to U1∪U1∪...∪U1Each level of first-level index can correspond to specific function index of specific software, and U in each level of first-level index corresponds to specific function index of specific softwarei＝{Ui1，Ui2，...，UimAnd evaluating all aspects of the software by using the software at a uniform period to obtain an evaluation domain, namely an evaluation result set W of the software is { W ═ W }1，W2，...，WnDividing the software evaluation index evaluation into four levels A, B, C and D, wherein A represents that the software index is excellent in state, does not need to be optimized, completely meets the requirements of LCA researchers, and can provide corresponding reference and guidance for other software; the software index represented by B is good in state, can be simply optimized and adjusted, and basically meets the requirements of LCA researchers; c represents that the software index state is general, and the software is correspondingly optimized on the basis of considering the requirements of LCA researchers; d represents that the software index state is poor, and problems must be found and solved from each link of the software;
b) determining a blur matrix
And (3) establishing fuzzy mapping from W to U by knowing an evaluation index set U and a software evaluation result set W of the software, and generating a fuzzy mapping matrix T:
wherein, tijRepresents the evaluation result set WiBelongs to a U in an evaluation index set UiDegree of membership of, tij＝zijT, t represents the number of accesses to the evaluation index set U, zijRepresents the evaluation index UiIs evaluated as an evaluation grade WjThe number of times of (c); selecting a plurality of LCA research experts to form a software evaluation group by adopting a Delphi method and a data statistical method, and evaluating indexes;
c) index weighting based on information entropy
Through the above description of the information entropy, the determination process of each index weight considering the information entropy is as follows:
calculating the proportion of the ith evaluation index in the jth evaluation grade according to the constructed fuzzy matrix, wherein a calculation formula is as follows:
calculating the system entropy e of the ith index in the systemi：
Wherein n is the number of evaluation results, k is 1/lnn, and pijSatisfy the requirement of
And when p isijWhen 1, ei＝0；
Computing entropy weight v of ith index in systemi：
Wherein m represents the number of evaluation indexes to be evaluated, and an index weight vector in the weight coefficient method is obtained through the formula: v ═ V (V)1,V2,…,Vm) In the same way, the weight direction of other level indexes can be solved;
fourthly, calculating the comprehensive weight
Obtaining the entropy weight V (V) of all indexes according to the step III1,V2,...,Vm) The index weight determined according to AHP is a ═ a1,a1,…am) (ii) a Giving the comprehensive weight B of the indexiThe following were used:
finally, relevance analysis is carried out, wherein the relevance analysis is the dependence relationship among the research objects, and further exploration and research are carried out on the dependence relationship, so that a statistical analysis method for researching the relationship among the variables is adopted; the correlation coefficient is used for clarifying the strength of the correlation among variables, and the strength of the correlation among life cycle evaluation software indexes can be measured; for the index X and the index Y, the value range of the correlation coefficient is between [ -1,1] and the closer the value of the correlation coefficient is to 0, the weaker the correlation of the X and the Y is, otherwise, the stronger the correlation is; when the correlation coefficient is 1, the X and the Y are completely positive linear correlation, when the correlation coefficient is-1, the X and the Y are completely negative linear correlation, and when the correlation coefficient is 0, the X and the Y are completely independent and have no correlation;
the correlation coefficient calculation formula of the variable X and the variable Y is as follows:
wherein S isxyRepresents the covariance between variable X and variable Y, SxRepresents the standard deviation, S, of the variable XyRepresents the standard deviation of the variable Y；
The covariance between variable X and variable Y is calculated as follows:
the standard deviation calculation for variable X is as follows:
the standard deviation calculation formula for the variable Y is as follows:
the maximum spanning tree algorithm is almost the same as the minimum spanning tree algorithm, and the edge with the maximum edge weight is selected according to the basic algorithm; the Prim algorithm of the maximum spanning tree is adopted by the maximum spanning tree algorithm;
the Prim algorithm of the maximum spanning tree has the basic idea that the maximum spanning tree is constructed by progressive layer by layer according to the one-by-one communication of vertexes; set G ═<V,E,W>Is connected and has n vertexes, then a certain vertex u according to the specified condition from G0Starting from this, the side (u) with the highest degree of association is selected0V), then the secondary edge and the two vertices form part of the tree; TE is a set of the maximum spanning tree edges, U is a set of points, according to the thought, firstly, one point of G in the graph is selected arbitrarily as a starting point a, the point is added into the set U, then, another point b is found in G, so that the edge is the edge with the maximum weight in all edges related to a, at the moment, the point b is also added into the set U, and the edges (a and b) are added into the TE; by analogy, the current set is U ═ a, b }, another point c is found from G to enable the weight of the point c to any point in U to be maximum, at the moment, the point c is added into the set U, edges of the two points are placed into TE, and when all vertexes are added into U, a maximum spanning tree is constructed; at this time, there must be n-1 sides in TE and n tops in UPoint, T ═<U,TE>A maximum spanning tree for G;
after the maximum spanning tree is formed, association exists among all nodes, a node comprehensive numerical value can be calculated through association degree calculation and node evaluation scores of all nodes, and the specific calculation process is shown as a formula 17:
wherein, PiIs the integrated evaluation value of the i-th index, BiAnd BjIs the weight of the ith and jth indices, AiAnd AjEvaluation values for the ith and jth indices, SijFor the correlation coefficient between the i-th and j-th indices, there is no correlation coefficient substituted by 0.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113626323A (en) * | 2021-07-31 | 2021-11-09 | 西南电子技术研究所（中国电子科技集团公司第十研究所） | Quality Bug test evaluation method for each stage of software life cycle |
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| CN113626323A (en) * | 2021-07-31 | 2021-11-09 | 西南电子技术研究所（中国电子科技集团公司第十研究所） | Quality Bug test evaluation method for each stage of software life cycle |
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| CN115618771A (en) * | 2022-12-16 | 2023-01-17 | 中国空气动力研究与发展中心计算空气动力研究所 | CFD software reliability quantitative evaluation method |
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