CN110543417A - method and device for evaluating effectiveness of software test - Google Patents
method and device for evaluating effectiveness of software test Download PDFInfo
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Abstract
The embodiment of the invention discloses a method and an evaluation device for evaluating the effectiveness of a software test, which are used for determining index weight based on evaluation index relevance by utilizing 2-additive fuzzy measure and improving the reliability of software test evaluation. The method provided by the embodiment of the invention comprises the following steps: acquiring an evaluation index set of a software test; determining a total 2-additionable ambiguity measure value according to the evaluation index set; carrying out non-dimensionalization processing on the evaluation index set to obtain a non-dimensionalized value; and obtaining a software test effectiveness evaluation value according to the total 2-additive fuzzy measurement value and the dimensionless value.
Description
Technical Field
The invention relates to the field of software testing, in particular to a method and an evaluation device for evaluating the effectiveness of software testing.
background
software testing is an important link in the software development process, is the final approval of software requirement analysis, design specification description and code realization before the software product is put into operation, and is the main link of software quality assurance throughout the whole process of software definition and development. In order to ensure the accuracy of the software test result, the effectiveness of the software test process needs to be evaluated to improve the confidence of the test method adopted by the test team. At present, researches on the effectiveness evaluation of the software testing process are mainly divided into single-angle evaluation based on defect analysis, coverage rate analysis and the like and qualitative evaluation based on a conceptual model.
the single-angle evaluation has the problems that the effectiveness of the test process is evaluated from one aspect and one level, and the hierarchical and all-around evaluation is lacked; the problem of conceptual model-based evaluation is that what should be done for the evaluation of the validity of the test process is only described, but how to do it is not specifically described, and the final evaluation result is only a qualitative evaluation and cannot be a quantitative evaluation value.
For these traditional model problems, most researchers have proposed an index-independent test validity assessment approach, such as: analytic hierarchy process, linear weighted sum, etc. However, in actual evaluation, due to the complexity of the test process, an evaluator cannot establish an index system meeting the requirement of index independence.
Disclosure of Invention
the embodiment of the invention provides a method and an evaluation device for evaluating the effectiveness of a software test, which are used for determining index weight based on evaluation index relevance by utilizing 2-additive fuzzy measure and improving the reliability of software test evaluation.
in view of the above, a first aspect of the present invention provides a method for evaluating the effectiveness of a software test, which may include:
Acquiring an evaluation index set of a software test;
determining a total 2-additionable ambiguity measure value according to the evaluation index set;
Carrying out non-dimensionalization processing on the evaluation index set to obtain a non-dimensionalized value;
and obtaining a software test effectiveness evaluation value according to the total 2-additive fuzzy measurement value and the dimensionless value.
Optionally, in some embodiments of the present invention, the determining a total 2-plus-ambiguity measure value according to the evaluation index set includes:
Constructing a swing matrix according to the evaluation index set;
calculating to obtain an initial measurement value of each evaluation index according to the swing matrix and a preset characteristic value method;
determining an association relation between evaluation index subsets in the evaluation index set, wherein the evaluation index subsets comprise two evaluation indexes;
constructing an association matrix according to the incidence relation among the evaluation index subsets and the swing matrix;
obtaining an association matrix according to the swing matrix and the intersection matrix;
calculating to obtain corresponding influence factors according to the initial measurement value of each evaluation index and the incidence matrix;
Calculating to obtain a 2-fuzzy metric value of the evaluation index subset according to the initial metric value of each evaluation index and the influence factor;
and carrying out normalization processing according to the initial measurement value of each evaluation index and the 2-fuzzy measurement values of the evaluation index subset, and calculating to obtain a total 2-fuzzy measurement value.
Optionally, in some embodiments of the present invention, constructing a swing matrix according to the evaluation index set includes:
Determining each evaluation index according to the evaluation index set;
Obtaining a plurality of evaluation working values corresponding to each evaluation index;
Removing abnormal values from the plurality of evaluation working values corresponding to each evaluation index by using a bias abnormal value determination method to obtain a plurality of normal evaluation working values corresponding to each evaluation index;
determining a corresponding first weight coefficient according to a plurality of normal evaluation working values corresponding to each evaluation index;
Obtaining a first total evaluation value of each evaluation index according to a plurality of normal evaluation working values corresponding to each evaluation index and the corresponding first weight coefficient;
and constructing a swing matrix according to the first total evaluation value of each evaluation index.
Optionally, in some embodiments of the present invention, constructing a swing matrix according to the evaluation index set includes:
Determining each evaluation index according to the evaluation index set;
obtaining a plurality of evaluation working values corresponding to each evaluation index;
Determining a corresponding second weight coefficient according to a plurality of evaluation working values corresponding to each evaluation index;
obtaining a second total evaluation value of each evaluation index according to the plurality of evaluation working values corresponding to each evaluation index and the corresponding second weight coefficient;
and constructing a swing matrix according to the second total evaluation value of each evaluation index.
optionally, in some embodiments of the present invention, the constructing an association matrix according to the association relationship between the evaluation index subsets and the swing matrix includes:
obtaining a plurality of values corresponding to the incidence relation among the evaluation index subsets;
removing abnormal values from a plurality of values corresponding to the association relationship among the evaluation index subsets by using a bias abnormal value determination method to obtain a plurality of normal values corresponding to the association relationship among the evaluation index subsets;
Determining a corresponding third weight coefficient according to a plurality of normal values corresponding to the incidence relation among the evaluation index subsets;
Obtaining a third total value corresponding to the incidence relation among the evaluation index subsets according to the multiple normal values corresponding to the incidence relation among the evaluation index subsets and the corresponding third weight coefficient;
and constructing an association matrix according to the third total value corresponding to the incidence relation among the evaluation index subsets and the swing matrix.
optionally, in some embodiments of the present invention, the constructing an association matrix according to the association relationship between the evaluation index subsets and the swing matrix includes:
obtaining a plurality of values corresponding to the incidence relation among the evaluation index subsets;
determining a corresponding fourth weight coefficient according to a plurality of values corresponding to the incidence relation among the evaluation index subsets;
obtaining a fourth total value corresponding to the incidence relation among the evaluation index subsets according to the plurality of values corresponding to the incidence relation among the evaluation index subsets and the corresponding fourth weight coefficient;
and constructing an association matrix according to the fourth total value corresponding to the incidence relation among the evaluation index subsets and the swing matrix.
optionally, in some embodiments of the present invention, the performing a non-dimensionalization on the evaluation index set to obtain a non-dimensionalized value includes:
And carrying out non-dimensionalization on the evaluation index set by adopting a standardized function to obtain a non-dimensionalized value.
Optionally, in some embodiments of the present invention, the obtaining a software test validity assessment value according to the total 2-plus-ambiguity measure value and the dimensionless value includes:
And performing aggregation processing by using fuzzy integration according to the total 2-additive fuzzy measurement value and the dimensionless value to obtain a software testing validity evaluation value.
a second aspect of the present invention provides an evaluation apparatus, which may include:
The acquisition module is used for acquiring an evaluation index set of the software test;
the processing module is used for determining a total 2-additive fuzzy measure value according to the evaluation index set; carrying out non-dimensionalization processing on the evaluation index set to obtain a non-dimensionalized value; and obtaining a software test effectiveness evaluation value according to the total 2-additive fuzzy measurement value and the dimensionless value.
alternatively, in some embodiments of the present invention,
The processing module is specifically configured to construct a swing matrix according to the evaluation index set; calculating to obtain an initial measurement value of each evaluation index according to the swing matrix and a preset characteristic value method; determining an association relation between evaluation index subsets in the evaluation index set, wherein the evaluation index subsets comprise two evaluation indexes; constructing an association matrix according to the incidence relation among the evaluation index subsets and the swing matrix; obtaining an association matrix according to the swing matrix and the intersection matrix; calculating to obtain corresponding influence factors according to the initial measurement value of each evaluation index and the incidence matrix; calculating to obtain a 2-fuzzy metric value of the evaluation index subset according to the initial metric value of each evaluation index and the influence factor; and carrying out normalization processing according to the initial measurement value of each evaluation index and the 2-fuzzy measurement values of the evaluation index subset, and calculating to obtain a total 2-fuzzy measurement value.
alternatively, in some embodiments of the present invention,
The processing module is specifically configured to determine each evaluation index according to the evaluation index set; obtaining a plurality of evaluation working values corresponding to each evaluation index; removing abnormal values from the plurality of evaluation working values corresponding to each evaluation index by using a bias abnormal value determination method to obtain a plurality of normal evaluation working values corresponding to each evaluation index; determining a corresponding first weight coefficient according to a plurality of normal evaluation working values corresponding to each evaluation index; obtaining a first total evaluation value of each evaluation index according to a plurality of normal evaluation working values corresponding to each evaluation index and the corresponding first weight coefficient; and constructing a swing matrix according to the first total evaluation value of each evaluation index.
alternatively, in some embodiments of the present invention,
The processing module is specifically configured to determine each evaluation index according to the evaluation index set; obtaining a plurality of evaluation working values corresponding to each evaluation index; determining a corresponding second weight coefficient according to a plurality of evaluation working values corresponding to each evaluation index; obtaining a second total evaluation value of each evaluation index according to the plurality of evaluation working values corresponding to each evaluation index and the corresponding second weight coefficient; and constructing a swing matrix according to the second total evaluation value of each evaluation index.
Alternatively, in some embodiments of the present invention,
The processing module is specifically configured to obtain a plurality of values corresponding to the association relationship between the evaluation index subsets; removing abnormal values from a plurality of values corresponding to the association relationship among the evaluation index subsets by using a bias abnormal value determination method to obtain a plurality of normal values corresponding to the association relationship among the evaluation index subsets; determining a corresponding third weight coefficient according to a plurality of normal values corresponding to the incidence relation among the evaluation index subsets; obtaining a third total value corresponding to the incidence relation among the evaluation index subsets according to the multiple normal values corresponding to the incidence relation among the evaluation index subsets and the corresponding third weight coefficient; and constructing an association matrix according to the third total value corresponding to the incidence relation among the evaluation index subsets and the swing matrix.
alternatively, in some embodiments of the present invention,
the processing module is specifically configured to obtain a plurality of values corresponding to the association relationship between the evaluation index subsets; determining a corresponding fourth weight coefficient according to a plurality of values corresponding to the incidence relation among the evaluation index subsets; obtaining a fourth total value corresponding to the incidence relation among the evaluation index subsets according to the plurality of values corresponding to the incidence relation among the evaluation index subsets and the corresponding fourth weight coefficient; and constructing an association matrix according to the fourth total value corresponding to the incidence relation among the evaluation index subsets and the swing matrix.
Alternatively, in some embodiments of the present invention,
the processing module is specifically configured to perform non-dimensionalization processing on the evaluation index set by using a standardized function to obtain a non-dimensionalized value.
alternatively, in some embodiments of the present invention,
and the processing module is specifically used for carrying out aggregation processing by using fuzzy integration according to the total 2-additive fuzzy measurement value and the dimensionless value to obtain a software test validity evaluation value.
a third aspect of the present invention provides an evaluation apparatus, which may include:
a processor, a memory, wherein the processor and the memory are connected by a bus;
The memory is used for storing operation instructions;
The processor is configured to invoke the operation instruction, and execute the steps of the method for evaluating the validity of the software test according to the first aspect of the present invention and any optional implementation manner of the first aspect of the present invention.
a fourth aspect of the present invention provides a computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the steps of the method for evaluating the validity of a software test according to the first aspect of the present invention and any one of the optional implementations of the first aspect.
according to the technical scheme, the embodiment of the invention has the following advantages:
In the embodiment of the invention, an evaluation index set of a software test is obtained; determining a total 2-additionable ambiguity measure value according to the evaluation index set; carrying out non-dimensionalization processing on the evaluation index set to obtain a non-dimensionalized value; and obtaining a software test effectiveness evaluation value according to the total 2-additive fuzzy measurement value and the dimensionless value. The method and the device are used for determining the index weight based on the relevance of the evaluation index by utilizing the 2-additive fuzzy measure, so that the reliability of software test evaluation is improved.
drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the embodiments and the drawings used in the description of the prior art, and obviously, the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained according to the drawings.
FIG. 1 is a schematic diagram of an embodiment of a method for evaluating the effectiveness of a software test according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a software test validity evaluation flow based on 2-fuzziness measure according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of an embodiment of an evaluation apparatus according to an embodiment of the present invention;
Fig. 4 is a schematic diagram of another embodiment of the evaluation device in the embodiment of the present invention.
Detailed Description
the embodiment of the invention provides a method and an evaluation device for evaluating the effectiveness of a software test, which are used for determining index weight based on evaluation index relevance by utilizing 2-additive fuzzy measure and improving the reliability of software test evaluation.
in order to make the technical solutions of the present invention better understood by those skilled in the art, the technical solutions in the embodiments of the present invention will be 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 embodiments. The embodiments based on the present invention should fall into the protection scope of the present invention.
In the embodiment of the invention, a software test effectiveness evaluation method based on index relevance can be established to obtain an accurate and actual quantitative software test effectiveness evaluation result.
In the following, a simple description is first made on the 2-fuzziness measure involved in the method for evaluating the effectiveness of the software test based on the index relevance in the embodiment of the present invention, as shown below:
In the multi-index decision problem based on index association, the additive property of the index weight is damaged due to the association between indexes, so that a linear weighting mode fails. In order to model the weights of indexes and an index set in a multi-index decision problem based on index association, a scholars puts forward a concept of fuzzy measure. It concludes that any correlation between things (correlation, complementary/redundant correlation, etc.) can be measured with a fuzzy measure. Particularly, the 2-fuzziness measure gives consideration to the scientificity and the practical operability of the algorithm, so that the method is widely applied to practical application. The blur integral is a non-linear aggregation function defined over the blur measure for use in combination with the blur measure value. In view of the mutual correlation between the software testing effectiveness evaluation indexes, 2-additive fuzzy measure is required to be introduced to be combined with fuzzy integration for evaluation.
1. fuzzy measure correlation concept
the fuzzy measure correlation concept is as follows:
For convenience of description, it is assumed that X ═ {1, 2, … n }, and μ ({ i }), μ ({ i, j }), and μ (K) are denoted by μ i, μ ij, and μ K, respectively.
1) let X be the finite set of evaluation metrics, P (X) be the set of all subsets of X, if the set function μ: p (x) → [0, 1] satisfies the following condition, and μ is referred to as a blur measure defined at p (x):
(1)μ(Φ)＝0，μ(X)＝1；
(2)EεP(X)，FεP(X)，EεF，μ(E)≤μ(F)。
The fuzzy measure mu is a model of the relative importance of the index set, i.e. for the index set a epsilon X, mu (a) expresses the relative importance of the index set a. For any M, N ∈ p (x), M ≈ N ═ Φ, if μ (M) + μ (N) < μ (muu £ N), then the index set M is called, and N has a complementary relationship; if mu (M) + mu (N) > mu (M U N), the index set M is called, and redundant association exists between N; if μ (M) + μ (N) ═ μ (muu N), the index set M, N are called independent of each other. The relevance between the indexes and the index set is defined by fuzzy measure, and the following proposition can be obtained:
proposition 1: for any M ∈ X, N ∈ X, M ≡ N ═ Φ, and M ═ N ═ X, i.e., disjoint 2 partitions where M, N is X, if there is a complementary association between M, N, then μ (M) + μ (N) < 1.
proposition 2: for any M ∈ X, N ∈ X, M ≡ N ═ Φ, and M ═ N ═ X, i.e., disjoint 2 partitions where M, N is X, if there is a redundant association between M, N, then μ (M) + μ (N) > 1.
Proposition 3: if any two index subsets in the index set X ═ { X1, X2, … …, xn } are independent of each other, then
The above three propositions illustrate that when the association between index sets is not independent, their fuzzy measure sum (weight sum) is not necessarily 1.
2) for the 2-additive ambiguity measure, if the measure μ i, μ ij (i, j ε X and i ≠ j) is determined, then the other measure values are:
μ(K)＝∑μij-(|K|-2)∑μi，KεX，(i，jεK)
(1)
3) If X is a finite set { X1, X2, … xn }, the function f is a discrete function, the function value is { a1, a2, … an }, without loss of generality, assuming that a1 ≦ a2 ≦ … ≦ an, and the index values in the set are sorted from small to large, the fuzzy integral of f with respect to μ is defined as:
∫fdμ＝∑(ai-ai-1)μ(Ai)
(2)
wherein: a0 ═ 0, Ai ═ { xi, xi +1, …, xn }.
as shown in fig. 1, a schematic diagram of an embodiment of a method for evaluating the validity of a software test according to an embodiment of the present invention includes:
101. and acquiring an evaluation index set of the software test.
102. Determining a total 2-plus ambiguity measure value based on the set of assessment indicators.
in an embodiment of the present invention, the determining a total 2-plus-ambiguity measure value according to the evaluation index set may include: constructing a swing matrix according to the evaluation index set; calculating to obtain an initial measurement value of each evaluation index according to the swing matrix and a preset characteristic value method; determining an association relation between evaluation index subsets in the evaluation index set, wherein the evaluation index subsets comprise two evaluation indexes; constructing an association matrix according to the incidence relation among the evaluation index subsets and the swing matrix; obtaining an association matrix according to the swing matrix and the intersection matrix; calculating to obtain corresponding influence factors according to the initial measurement value of each evaluation index and the incidence matrix; calculating to obtain a 2-fuzzy metric value of the evaluation index subset according to the initial metric value of each evaluation index and the influence factor; and carrying out normalization processing according to the initial measurement value of each evaluation index and the 2-fuzzy measurement values of the evaluation index subset, and calculating to obtain a total 2-fuzzy measurement value.
wherein, constructing a swing matrix according to the evaluation index set may include, but is not limited to, the following implementation manners:
(1) determining each evaluation index according to the evaluation index set; obtaining a plurality of evaluation working values corresponding to each evaluation index; removing abnormal values from the plurality of evaluation working values corresponding to each evaluation index by using a bias abnormal value determination method to obtain a plurality of normal evaluation working values corresponding to each evaluation index; determining a corresponding first weight coefficient according to a plurality of normal evaluation working values corresponding to each evaluation index; obtaining a first total evaluation value of each evaluation index according to a plurality of normal evaluation working values corresponding to each evaluation index and the corresponding first weight coefficient; and constructing a swing matrix according to the first total evaluation value of each evaluation index.
Alternatively, the first and second electrodes may be,
(2) determining each evaluation index according to the evaluation index set; obtaining a plurality of evaluation working values corresponding to each evaluation index; determining a corresponding second weight coefficient according to a plurality of evaluation working values corresponding to each evaluation index; obtaining a second total evaluation value of each evaluation index according to the plurality of evaluation working values corresponding to each evaluation index and the corresponding second weight coefficient; and constructing a swing matrix according to the second total evaluation value of each evaluation index.
The constructing a joint matrix according to the association relationship between the evaluation index subsets and the swing matrix may include, but is not limited to, the following implementation manners:
(1) Obtaining a plurality of values corresponding to the incidence relation among the evaluation index subsets; removing abnormal values from a plurality of values corresponding to the association relationship among the evaluation index subsets by using a bias abnormal value determination method to obtain a plurality of normal values corresponding to the association relationship among the evaluation index subsets; determining a corresponding third weight coefficient according to a plurality of normal values corresponding to the incidence relation among the evaluation index subsets; obtaining a third total value corresponding to the incidence relation among the evaluation index subsets according to the multiple normal values corresponding to the incidence relation among the evaluation index subsets and the corresponding third weight coefficient; and constructing an association matrix according to the third total value corresponding to the incidence relation among the evaluation index subsets and the swing matrix.
Alternatively, the first and second electrodes may be,
(2) obtaining a plurality of values corresponding to the incidence relation among the evaluation index subsets; determining a corresponding fourth weight coefficient according to a plurality of values corresponding to the incidence relation among the evaluation index subsets; obtaining a fourth total value corresponding to the incidence relation among the evaluation index subsets according to the plurality of values corresponding to the incidence relation among the evaluation index subsets and the corresponding fourth weight coefficient; and constructing an association matrix according to the fourth total value corresponding to the incidence relation among the evaluation index subsets and the swing matrix.
illustratively, the determination method for the overall 2-plus-blur measure is as follows:
it should be noted that the total 2-fuzziness measure determining method may also be understood as a weight value determining method based on index relevance.
1) Structural oscillation amplitude matrix
The evaluation indexes in the evaluation index set X ═ { X1, X2, … xn } are compared pairwise, a swing matrix V ═ (vij) nxn is constructed, for any two indexes xi and xj, an index which is most expected to be evaluated firstly is selected by experts, the index is not set as xi, the evaluation work value performed by the experts is taken as 100, namely vij ═ 100, and a reasonable value V is selected from the interval [0, 100) as the value of the evaluation work of the index xj, namely vji ═ V. Obviously max (vij, vji) is 100, vii is 100, 0 ≦ vij ≦ 100, i, j is 1,2 … n.
2) calculating an initial measure
And (3) obtaining a pairwise comparison judgment matrix B which is (bij) nxn which is (vij/vji) nxn in the sense of scale by using the swing matrix, and calculating an initial measure mu 'i which is mu' (xi) of each evaluation index by using a characteristic value method, wherein i is 1,2, …, n. Carrying out consistency check on the judgment matrix B, wherein if the check is passed, mu' i is the initial measure of the n evaluation indexes; otherwise, an expert may be asked to adjust the assignment of the swing matrix V until the test is passed.
3) constructing a joint matrix
And determining the association relation among the evaluation index subsets. The evaluation index subset is complementary, namely the combined contribution of two groups of indexes E and F is larger than the sum of the independent contributions of the two groups of indexes, namely omega (E ^ F) > omega (E) + omega (F); the redundancy between index subsets means that the combined contribution of two groups of indexes E and F is smaller than the sum of the individual contributions of the two groups of indexes, namely omega (Eu.F) < omega (E) + omega (F); independent among the index subsets means that the combined contributions of the two groups of indexes E, F are equal to the sum of the individual contributions of the two groups of indexes, i.e. ω (E ═ F) + ω (E) + ω (F).
Constructing an association matrix C (cij) nxn according to the incidence relation and the swing matrix V between every two evaluation indexes, and selecting a reasonable value V from an interval [ max (vij, vji), vij + vji) as the value of simultaneously improving the indexes xi and xj when the indexes xi and xj are in a redundant relation, namely cij (V); when the indexes xi and xj are in a complementary relation, selecting a reasonable value v from the interval (vij + vji, + ∞) as the value of simultaneously improving the indexes xi and xj, namely cij is equal to v; when indices xi and xj are in independent relationship, cij is vij + vji, obviously, cij is cji, cii is 200, i, j is 1,2 … n.
4) obtaining a correlation matrix R
Determining a correlation matrix R ═ (rij) n × n from the swing matrix V and the intersection matrix C, where
rij＝(cij-vij-vji)/(vij+vji) (3)
Obviously, rij is rji, ri is 0, i, j is 1,2, 3 … n.
5) calculating an impact factor
influence factor λ min (1, k1, k2, …, kn)
(4)
Wherein the content of the first and second substances,
6) calculation of μ' ij
μ’ij＝μ’i+μ’j+λrij(μ’i+μ’j)，i，j＝1，2，…，n.i≠j。 (6)
7) Calculating 2-fuzzifiable measure, calculating other initial measure values through the formula (1) according to mu 'i (i is more than or equal to 1 and less than or equal to n) and mu' ij (i is not equal to j, i is more than or equal to 1 and j is less than or equal to n), and carrying out normalization processing on the initial measure values to obtain the total 2-fuzzifiable measure.
The method for determining the bias judgment abnormal value is as follows:
when the 2-addable fuzzy measurement value is used for determining the weight value, a plurality of experts are required to comprehensively compare and evaluate two indexes in the swing matrix and determine the value of each element in the combined matrix, in actual work, due to the preference of different experts and the one-sidedness in judging the cognition of objects, when the same objects are judged, some experts can give bias judgment, namely judge abnormal values, and the existence of the bias judgment abnormal values can influence the quality of comprehensive judgment, so that the bias judgment abnormal values can be effectively checked and eliminated.
Setting a calculation model of the mean value of each index element:
wherein: is the average score value of the ith index (between 0 and 100). ω ij is the score of the i index given by the j expert (between 0 and 100). K is the number of experts.
The set variance calculation model:
The 3 sigma rule can be used as a criterion for eliminating bias judgment. The difference between each decision and the sample mean is:
If the | delta ik | is less than 3S, the score value of the ith index obtained by the kth expert meets the requirement, and ω ij is reserved as effective data for further data analysis and processing, otherwise, the value is removed and judged again.
further, the determination method for the expert decision weight coefficient is as follows:
And finally, aggregating the scores of the index elements judged by the experts by using the decision weight coefficients of the experts to determine the final score value of the index. The basic method is as follows:
1) Determining decision weight coefficients for each expert
Assuming that the degree of similarity between the decision of the pth expert and the decision of the qth expert is represented by the distance dpq (p, q is 1,2, …, T), then:
dpq satisfies the following condition:
①d＝d≥0；
the smaller dpq is, the closer Y (p) is to Y (q), namely the closer the judgment of two experts is. If and only if dpq is 0 and p is not equal to q, the judgment of the two experts is completely consistent;
yiq is the score value of the qth expert for the ith index, yip is the score value of the pth expert for the ith index.
Assuming that the similarity degree between the t-th expert decision and all other expert decisions is represented by dt, then:
in formula (11), j is 1,2, …, T.
as can be seen from equation (11), dt is greater than or equal to 0, and a smaller dt indicates that the t-th expert agrees with the decisions of other experts, and when dt is equal to 0, dt1 is equal to dt2 is equal to … is equal to dtT, which indicates that the judgment of each expert is the same.
To sum up, the final decision weight coefficient λ t of the tth expert is:
As can be seen from equation (12), the greater dt indicates that the more divergences between the expert and other experts, the smaller the decision weight coefficient; when dt is smaller, the less the divergence between the expert and other experts is indicated, the larger the decision weight coefficient is. Therefore, common understanding of indexes and score distribution among experts is highlighted, and the reasonability of index scoring is improved.
2) index score aggregation
The final index score vector is set as an index score aggregation model as follows:
And obtaining a final score vector Y of the group of indexes through the aggregation of the index scores.
and finally, comparing the final scores of all indexes in the final score vector Y pairwise to visually obtain the most important index, and constructing a swing matrix and a joint matrix according to the importance and relevance.
103. And carrying out non-dimensionalization processing on the evaluation index set to obtain a non-dimensionalized value.
The performing non-dimensionalization on the evaluation index set to obtain a non-dimensionalized value may include: and carrying out non-dimensionalization on the evaluation index set by adopting a standardized function to obtain a non-dimensionalized value.
104. and obtaining a software test effectiveness evaluation value according to the total 2-additive fuzzy measurement value and the dimensionless value.
the obtaining a software test validity assessment value according to the total 2-additionable ambiguity measure value and the dimensionless value may include: and performing aggregation processing by using fuzzy integration according to the total 2-additive fuzzy measurement value and the dimensionless value to obtain a software testing validity evaluation value.
It can be understood that fig. 2 is a schematic diagram of a software test validity evaluation flow based on a 2-fuzziness measure in the embodiment of the present invention.
(1) And on the premise of following design principles of completeness, comparability, feasibility and the like of the index system, constructing a software test effectiveness evaluation index system.
(2) requesting an expert group to analyze the relevance between indexes on the same layer in an evaluation index system and the importance between comparison indexes when constructing a swing matrix, and determining the 2-additive fuzzy measure of each layer of indexes and index subsets by using a 2-additive fuzzy measure method based on the analysis conclusion of the expert group so as to obtain an index weight value based on the index relevance.
(3) because the indexes in the existing software testing effectiveness evaluation index system mainly comprise a maximum index, a minimum index and an interval index, the indexes are subjected to dimensionless processing by adopting a common standardized function.
(4) And according to the 2-additive fuzzy measure of each layer of index in the evaluation index system and the dimensionless value of the bottom layer of index, aggregating from bottom to top by using fuzzy integration to finally obtain the software testing effectiveness evaluation value.
in the embodiment of the invention, the index weight based on relevance can be determined by using 2-fuzziness measure; when 2-additable fuzzy measurement values are used for determining weight values, multiple experts are required to comprehensively compare and evaluate two indexes in the swing matrix and determine values of elements in the combined matrix, in actual work, due to the preferences of different experts and the one-sidedness in judging the cognition of objects, when the same objects are judged, some experts can give bias judgment, namely judge abnormal values, and the existence of the bias judgment abnormal values can influence the quality of comprehensive judgment, so that a bias judgment abnormal value determination method is used for effectively detecting and eliminating judgment results. And finally, aggregating the scores of the index elements judged by the experts by using the decision weight coefficients of the experts to determine the final score value of the index.
as shown in fig. 3, which is a schematic diagram of an embodiment of the evaluation apparatus in the embodiment of the present invention, the evaluation apparatus may include:
an obtaining module 301, configured to obtain an evaluation index set of a software test;
A processing module 302, configured to determine a total 2-additively fuzzy metric value according to the evaluation index set; carrying out non-dimensionalization processing on the evaluation index set to obtain a non-dimensionalized value; and obtaining a software test effectiveness evaluation value according to the total 2-additive fuzzy measurement value and the dimensionless value.
Alternatively, in some embodiments of the present invention,
a processing module 302, configured to construct a swing matrix according to the evaluation index set; calculating to obtain an initial measurement value of each evaluation index according to the swing matrix and a preset characteristic value method; determining an association relation between evaluation index subsets in the evaluation index set, wherein the evaluation index subsets comprise two evaluation indexes; constructing an association matrix according to the incidence relation among the evaluation index subsets and the swing matrix; obtaining an association matrix according to the swing matrix and the intersection matrix; calculating to obtain corresponding influence factors according to the initial measurement value of each evaluation index and the incidence matrix; calculating to obtain a 2-fuzzy metric value of the evaluation index subset according to the initial metric value of each evaluation index and the influence factor; and carrying out normalization processing according to the initial measurement value of each evaluation index and the 2-fuzzy measurement values of the evaluation index subset, and calculating to obtain a total 2-fuzzy measurement value.
Alternatively, in some embodiments of the present invention,
A processing module 302, specifically configured to determine each evaluation index according to the evaluation index set; obtaining a plurality of evaluation working values corresponding to each evaluation index; removing abnormal values from the plurality of evaluation working values corresponding to each evaluation index by using a bias abnormal value determination method to obtain a plurality of normal evaluation working values corresponding to each evaluation index; determining a corresponding first weight coefficient according to a plurality of normal evaluation working values corresponding to each evaluation index; obtaining a first total evaluation value of each evaluation index according to a plurality of normal evaluation working values corresponding to each evaluation index and the corresponding first weight coefficient; and constructing a swing matrix according to the first total evaluation value of each evaluation index.
alternatively, in some embodiments of the present invention,
a processing module 302, specifically configured to determine each evaluation index according to the evaluation index set; obtaining a plurality of evaluation working values corresponding to each evaluation index; determining a corresponding second weight coefficient according to a plurality of evaluation working values corresponding to each evaluation index; obtaining a second total evaluation value of each evaluation index according to the plurality of evaluation working values corresponding to each evaluation index and the corresponding second weight coefficient; and constructing a swing matrix according to the second total evaluation value of each evaluation index.
alternatively, in some embodiments of the present invention,
a processing module 302, configured to obtain a plurality of values corresponding to the association relationship between the evaluation index subsets; removing abnormal values from a plurality of values corresponding to the association relationship among the evaluation index subsets by using a bias abnormal value determination method to obtain a plurality of normal values corresponding to the association relationship among the evaluation index subsets; determining a corresponding third weight coefficient according to a plurality of normal values corresponding to the incidence relation among the evaluation index subsets; obtaining a third total value corresponding to the incidence relation among the evaluation index subsets according to the multiple normal values corresponding to the incidence relation among the evaluation index subsets and the corresponding third weight coefficient; and constructing an association matrix according to the third total value corresponding to the incidence relation among the evaluation index subsets and the swing matrix.
alternatively, in some embodiments of the present invention,
a processing module 302, configured to obtain a plurality of values corresponding to the association relationship between the evaluation index subsets; determining a corresponding fourth weight coefficient according to a plurality of values corresponding to the incidence relation among the evaluation index subsets; obtaining a fourth total value corresponding to the incidence relation among the evaluation index subsets according to the plurality of values corresponding to the incidence relation among the evaluation index subsets and the corresponding fourth weight coefficient; and constructing an association matrix according to the fourth total value corresponding to the incidence relation among the evaluation index subsets and the swing matrix.
alternatively, in some embodiments of the present invention,
The processing module 302 is specifically configured to perform non-dimensionalization processing on the evaluation index set by using a standardized function to obtain a non-dimensionalized value.
Alternatively, in some embodiments of the present invention,
The processing module 302 is specifically configured to perform aggregation processing by using fuzzy integration according to the total 2-additionable fuzzy metric value and the dimensionless value, so as to obtain a software testing validity evaluation value.
as shown in fig. 4, which is a schematic diagram of another embodiment of the evaluation apparatus in the embodiment of the present invention, the evaluation apparatus may include:
A processor 401 and a memory 402, wherein the processor 401 and the memory 402 are connected by a bus;
a memory 402 for storing operating instructions;
The processor 401 is configured to invoke the operation instruction to perform the steps of the method for evaluating the validity of the software test according to the embodiment of the present invention shown in fig. 1 or fig. 2.
in the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product.
The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the invention to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, from one website site, computer, server, or data center to another website site, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can store or a data storage device, such as a server, a data center, etc., that is integrated with one or more available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
it is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the embodiments provided in the present invention, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
the above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A method for evaluating the effectiveness of a software test, comprising: acquiring an evaluation index set of a software test;
determining a total 2-additionable ambiguity measure value according to the evaluation index set;
carrying out non-dimensionalization processing on the evaluation index set to obtain a non-dimensionalized value;
and obtaining a software test effectiveness evaluation value according to the total 2-additive fuzzy measurement value and the dimensionless value.
2. The method of claim 1, wherein determining a total 2-plus-ambiguity measure value from the set of assessment metrics comprises:
Constructing a swing matrix according to the evaluation index set;
calculating to obtain an initial measurement value of each evaluation index according to the swing matrix and a preset characteristic value method;
Determining an association relation between evaluation index subsets in the evaluation index set, wherein the evaluation index subsets comprise two evaluation indexes;
Constructing an association matrix according to the incidence relation among the evaluation index subsets and the swing matrix;
Obtaining an association matrix according to the swing matrix and the intersection matrix;
Calculating to obtain corresponding influence factors according to the initial measurement value of each evaluation index and the incidence matrix;
Calculating to obtain a 2-fuzzy metric value of the evaluation index subset according to the initial metric value of each evaluation index and the influence factor;
and carrying out normalization processing according to the initial measurement value of each evaluation index and the 2-fuzzy measurement values of the evaluation index subset, and calculating to obtain a total 2-fuzzy measurement value.
3. the method of claim 2, wherein constructing a swing matrix from the set of assessment metrics comprises:
Determining each evaluation index according to the evaluation index set;
Obtaining a plurality of evaluation working values corresponding to each evaluation index;
Removing abnormal values from the plurality of evaluation working values corresponding to each evaluation index by using a bias abnormal value determination method to obtain a plurality of normal evaluation working values corresponding to each evaluation index;
Determining a corresponding first weight coefficient according to a plurality of normal evaluation working values corresponding to each evaluation index;
obtaining a first total evaluation value of each evaluation index according to a plurality of normal evaluation working values corresponding to each evaluation index and the corresponding first weight coefficient;
and constructing a swing matrix according to the first total evaluation value of each evaluation index.
4. The method of claim 2, wherein constructing a swing matrix from the set of assessment metrics comprises:
Determining each evaluation index according to the evaluation index set;
Obtaining a plurality of evaluation working values corresponding to each evaluation index;
determining a corresponding second weight coefficient according to a plurality of evaluation working values corresponding to each evaluation index;
obtaining a second total evaluation value of each evaluation index according to the plurality of evaluation working values corresponding to each evaluation index and the corresponding second weight coefficient;
and constructing a swing matrix according to the second total evaluation value of each evaluation index.
5. the method according to claim 2, wherein constructing a convergence matrix according to the relationship between the subsets of evaluation metrics and the swing matrix comprises:
Obtaining a plurality of values corresponding to the incidence relation among the evaluation index subsets;
removing abnormal values from a plurality of values corresponding to the association relationship among the evaluation index subsets by using a bias abnormal value determination method to obtain a plurality of normal values corresponding to the association relationship among the evaluation index subsets;
determining a corresponding third weight coefficient according to a plurality of normal values corresponding to the incidence relation among the evaluation index subsets;
Obtaining a third total value corresponding to the incidence relation among the evaluation index subsets according to the multiple normal values corresponding to the incidence relation among the evaluation index subsets and the corresponding third weight coefficient;
And constructing an association matrix according to the third total value corresponding to the incidence relation among the evaluation index subsets and the swing matrix.
6. the method according to claim 2, wherein constructing a convergence matrix according to the relationship between the subsets of evaluation metrics and the swing matrix comprises:
Obtaining a plurality of values corresponding to the incidence relation among the evaluation index subsets;
determining a corresponding fourth weight coefficient according to a plurality of values corresponding to the incidence relation among the evaluation index subsets;
obtaining a fourth total value corresponding to the incidence relation among the evaluation index subsets according to the plurality of values corresponding to the incidence relation among the evaluation index subsets and the corresponding fourth weight coefficient;
and constructing an association matrix according to the fourth total value corresponding to the incidence relation among the evaluation index subsets and the swing matrix.
7. The method according to any one of claims 1 to 6, wherein the non-dimensionalizing the set of evaluation indicators to obtain a non-dimensionalized value comprises:
and carrying out non-dimensionalization on the evaluation index set by adopting a standardized function to obtain a non-dimensionalized value.
8. The method according to any one of claims 1-6, wherein deriving a software test validity assessment value from the total 2-fuzziness measure value and the dimensionless value comprises:
And performing aggregation processing by using fuzzy integration according to the total 2-additive fuzzy measurement value and the dimensionless value to obtain a software testing validity evaluation value.
9. an evaluation device, comprising: the acquisition module is used for acquiring an evaluation index set of the software test;
The processing module is used for determining a total 2-additive fuzzy measure value according to the evaluation index set;
carrying out non-dimensionalization processing on the evaluation index set to obtain a non-dimensionalized value;
And obtaining a software test effectiveness evaluation value according to the total 2-additive fuzzy measurement value and the dimensionless value.
10. a computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method of validity assessment of a software test according to any one of claims 1 to 8.
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