AU2021105543A4 - A fuzzy entropy-based method for classification and selection of multi-faceted test case of software - Google Patents
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Abstract
A FUZZY ENTROPY-BASED METHOD FOR CLASSIFICATION AND
SELECTION OF MULTI-FACETED TEST CASE OF SOFTWARE
The present disclosure relates to a fuzzy entropy-based method for software multi-faceted
5 test case classification and selection. In an aspect, the fuzzy entropy-based method (100) for
software multi-faceted test case classification and selection, wherein said method (100)
comprises steps of identifying (102) parameters and sub-parameters for test case fitness
evaluation, determining (104) weight calculations and distribution approach for obtaining the
values of the weights of different parameters, sub-parameters, and objectives, calculating
10 (106) metric value of indexed items using inspection methods, estimating (108) ambiguity in
multi-criteria fitness of test cases using fuzzy entropy approach, selecting (110) test cases
using estimated ambiguity (108), classifying (112) the selected test cases using estimated
ambiguity (108).
15 (FIG. 1 will be the reference figure)
- 14-
100
Identifying parameters and sub-parameters.
102
Determining weight calculation and distribution
approach.
104
Calculating metric value of indexed items.
Estimating ambiguity.
Selecting test cases using estimated ambiguity.
110
Classifying the selected test cases.
112
5 Fig.1 Flowchart of the fuzzy entropy-based method for software multi-faceted test case
classification and selection.
- 15 -
Description
Identifying parameters and sub-parameters. 102
Determining weight calculation and distribution approach. 104
Calculating metric value of indexed items.
Estimating ambiguity.
Selecting test cases using estimated ambiguity. 110
Classifying the selected test cases. 112
Fig.1 Flowchart of the fuzzy entropy-based method for software multi-faceted test case classification and selection.
[0001] The present disclosure relates to a software multi-faceted test case classification and selection and in particular to a fuzzy entropy-based method for software multi-faceted test case classification and selection.
[0002] Background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0003] Software testing is complex, labor-intensive, ambiguous, error prone, costly, and a core activity of software development. Complexity, cost, risks, and fuzziness increase every day in software testing. Multi-criteria test cases fitness evaluation, multi-faceted test cases selection and classification are crucial problems in the software industry. Since software testing is a costly and time intense activity, delivering the human safety software without proper testing may lead to cost which is potentially much higher than that of testing. Low quality of software testing is because of legacy paradigm and outdated techniques for test cases optimization. It requires devising intelligent and soft computing techniques /
methods continuously to improve the software testing quality gradually. Quality of test cases optimization depends on the fitness, strategy, number of test cases exercised and fitness parameters. Input to the program under test are test cases. These test cases are also a set of conditions which are determined by the tester, whether a software system or an application is working correctly or not. Collection of test cases is a test cases pool and it may contain some irrelevant, redundant, and unfit test cases. Since testing is a very expensive process, unnecessary execution of redundant, irrelevant and unfit test cases will increase unnecessary burden of cost.
[00041 Efforts have been made in in the related prior art to provide different solutions for software test case classification and selection. For example, a Chinese patent no. CN105373469B provides a kind of automatic software test method and system based on interface, the system includes test case management module, test execution management module, database manipulation management module and test result management module, wherein, test case management module is generated and is preserved for use-case message, test execution management module is verified for interface parameters calling and return value, database manipulation management module is used for generation, execution and the verification of database statement, and test result management module is for counting, analyzing and format test report.
[0005] Further, efforts have been made in the related prior art to provide different solutions for software test case classification and selection. For example, a United States patent no. US8707268B2 discloses A method and system for processing test results from the testing operation of the software. A test result of a pass, fail status, or unperformed is received for each test case of a test performed for each release of the software. A group to which each test belongs is ascertained, which determines a group identifier of the group to which each test belongs. A test result stability index is calculated for each test case as being proportional to a total number of consecutive releases that include and are prior to the last release of the software such that the test result for each of the consecutive releases denotes a pass. The group identifier and the test result stability index are stored in a hardware storage unit.
[0006] Therefore, the present disclosure overcomes the above-mentioned problem associated with the traditionally available method or system, any of the above-mentioned inventions can be used with the presented disclosed technique with or without modification.
[00071 All publications herein are incorporated by reference to the same extent as if each individual publication or patent application were specifically and individually indicated to be incorporated by reference. Where a definition or use of a term in an incorporated reference is inconsistent or contrary to the definition of that term provided herein, the definition of that term provided herein applies and the definition of that term in the reference does not apply.
[0008] It is an object of the present disclosure which provides a fuzzy entropy-based method for software multi-faceted test case classification and selection.
[0009] The present concept of the present invention is directed towards a fuzzy entropy-based method for software multi-faceted test case classification and selection.
[0010] In an aspect of the present disclosure, the fuzzy entropy-based method for software multi-faceted test case classification and selection, wherein said method comprises steps of identifying parameters and sub-parameters for test case fitness evaluation, determining weight calculations, and distribution approach for obtaining the values of the weights of different parameters, sub-parameters, and objectives, calculating the metric value of indexed items using inspection methods, estimating ambiguity in multi-criteria fitness of test cases using fuzzy entropy approach, selecting test cases using estimated ambiguity, classifying the selected test cases using estimated ambiguity.
[0011] In another aspect, estimating ambiguity in multi-criteria fitness of test cases using fuzzy entropy approach comprises steps of calculating the fitness of the particular test as the ratio of intra-class ambiguity to total ambiguity of the specific test case, wherein ambiguity in fitness is considered as the fitness of the test case, awarding ECIF certificate to the test cases having ambiguity values greater than or equal to the cut-point value, awarding EQCF certificate to the test cases having ambiguity values lesser than the cut-point value, removing the test cases having EICF certificate from the test suit.
[0012] In another aspect, the selection and classification of test cases are made by similarity-based selection and similarity-based classification using fuzzy entropy evaluating index (FFEI). And, similarity-based selection and classification comprise of creation of ideal vectors that represent a particular class, wherein this vector is user-defined or calculated from some sample set, calculating similarities values between the sample test cases, calculating the uncertainty in fitness and classification of the test case by using fuzzy entropy formula, removing all the test cases having high entropy value, exercising and auditing the remaining test cases on the SUT.
[0013] In yet another aspect, the mean-entropy discretization approach is used to split the test cases into two regions, high ambiguity and low ambiguity regions. And, the cut-point for ambiguity is calculated from the mean of the entropy values of all test cases.
[0014] One should appreciate that although the present disclosure has been explained with respect to a defined set of functional modules, any other module or set of modules can be added/deleted/modified/combined, and any such changes in architecture/construction of the proposed system are completely within the scope of the present disclosure. Each module can also be fragmented into one or more functional sub-modules, all of which are also completely within the scope of the present disclosure.
[00151 Various objects, features, aspects, and advantages of the inventive subject matter will become more apparent from the following detailed description of preferred embodiments, along with the accompanying drawing figures in which numerals represent like components.
[0016] The accompanying drawings are included to provide a further understanding of the present disclosure and are incorporated in and constitute a part of this specification. The drawings illustrate exemplary embodiments of the present disclosure and, together with the description, serve to explain the principles of the present disclosure.
[00171 FIG. 1 illustrates an exemplary flowchart of a fuzzy entropy-based method for software multi-faceted test case classification and selection.
[00181 It should be noted that the figures are not drawn to scale, and the elements of similar structure and functions are generally represented by like reference numerals for illustrative purposes throughout the figures. It should be noted that the figures do not illustrate every aspect of the described embodiments and do not limit the scope of the present disclosure.
[0019] Other objects, advantages and novel features of the invention will become apparent from the following detailed description of the present embodiment when taken in conjunction with the accompanying drawings.
[0020] In the following description, numerous specific details are set forth in order to provide a thorough understanding of embodiments of the present invention. It will be apparent to one skilled in the art that embodiments of the present invention may be practised without some of these specific details.
[0021] Embodiments of the present invention include various steps, which will be described below. The steps may be performed by hardware components or may be embodied in machine-executable instructions, which may be used to cause a general-purpose or special purpose processor programmed with the instructions to perform the steps. Alternatively, steps may be performed by a combination of hardware, software, and firmware and/or by human operators.
[0022] Embodiments of the present invention may be provided as a computing device, which may include one or more storage medium tangibly embodying thereon instructions and unique identities of the device, the instruction may be used to prevent the unauthorized user to alter/erase the unique identities of the device. The storage mediums may include, but is not limited to, semiconductor memories, such as ROMs, PROMs, random access memories (RAMs), programmable read-only memories (PROMs), erasable PROMs (EPROMs), electrically erasable PROMs (EEPROMs), flash memory, or other type of media/machine readable medium suitable for storing unique ID(s) of the device and electronic instructions (e.g., computer programming code, such as software or firmware).
[0023] Various methods described herein may be practiced by combining one or more machine-readable storage media containing the code/instruction according to the present invention with appropriate standard device hardware to execute the instruction contained therein. An apparatus for practicing various embodiments of the present invention may involve one or more computers (say server) (or one or more processors within a single computer) and storage systems containing or having network access to computer program(s) coded in accordance with various methods described herein, and the method steps of the invention could be accomplished by modules, devises, routines, subroutines, or subparts of a computer program product.
[0024] Exemplary embodiments will now be described more fully hereinafter with reference to the accompanying drawings, in which exemplary embodiments are shown. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. These embodiments are provided so that this disclosure will be thorough and complete and will fully convey the scope of the invention to those of ordinary skill in the art. Moreover, all statements herein reciting embodiments of the invention, as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof. Additionally, it is intended that such equivalents include both currently known equivalents as well as equivalents developed in the future (i.e., any elements developed that perform the same function, regardless of structure).
[0025] Thus, for example, it will be appreciated by those of ordinary skill in the art that the diagrams, schematics, illustrations, and the like represent conceptual views or processes illustrating systems and methods embodying this invention. The functions of the various elements shown in the figures may be provided through the use of dedicated hardware as well as hardware capable of executing associated software. Similarly, any switches shown in the figures are conceptual only. Their function may be carried out through the operation of program logic, through dedicated logic, through the interaction of program control and dedicated logic, or even manually, the particular technique being selectable by the entity implementing this invention. Those of ordinary skill in the art further understand that the exemplary hardware, software, processes, methods, and/or operating systems described herein are for illustrative purposes and, thus, are not intended to be limited to any particular named
[0026] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to clearly communicate the disclosure. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present disclosure as defined by the appended claims.
[00271 Each of the appended claims defines a separate invention, which for infringement purposes is recognized as including equivalents to the various elements or limitations specified in the claims. Depending on the context, all references below to the "invention" may in some cases refer to certain specific embodiments only. In other cases, it will be recognized that references to the "invention" will refer to subject matter recited in one or more, but not necessarily all, of the claims.
[0028] Various terms as used herein are shown below. To the extent a term used in a claim is not defined below, it should be given the broadest definition persons in the pertinent art have given that term as reflected in printed publications and issued patents at the time of filing.
[0029] In an embodiment of the present disclosure, FIG. 1 illustrates an exemplary flowchart of a fuzzy entropy-based method for software multi-faceted test case classification and selection.
[0030] In an aspect, the fuzzy entropy-based method (100) for software multi-faceted test case classification and selection, wherein said method (100) comprises steps of identifying (102) parameters and sub-parameters for test case fitness evaluation, determining
(104) weight calculations and distribution approach for obtaining the values of the weights of different parameters, sub-parameters, and objectives, calculating (106) metric value of indexed items using inspection methods, estimating (108) ambiguity in multi-criteria fitness of test cases using fuzzy entropy approach, selecting (110) test cases using estimated ambiguity (108), classifying (112) the selected test cases using estimated ambiguity (108).
[0031] In another aspect, estimating ambiguity (108) in multi-criteria fitness of test cases using fuzzy entropy approach comprises steps of calculating the fitness of the particular test as the ratio of intra-class ambiguity to total ambiguity of the specific test case, wherein ambiguity in fitness is considered as the fitness of the test case, awarding ECIF certificate to the test cases having ambiguity values greater than or equal to the cut-point value, awarding EQCF certificate to the test cases having ambiguity values lesser than the cut-point value, removing the test cases having EICF certificate from the test suit.
[0032] In another aspect, the ambiguity (108) in multi-criteria fitness of test cases and classify (112) the test cases is estimated more accurately. Hereafter, the test cases are selected on the basis of the ambiguity (108) value. The present disclosure uses similarity-based classification (112) techniques and provides support to the multi-faceted test case selection (110). The similarity-based test case selection (110) based on fuzzy entropy plays an important role in multi-faceted test case classification (112) and thus helps the tester in the identification of fit test cases amidst a large pool of test cases to exercise on the SUT. The present disclosure uses the fuzzy fitness evaluating index (FFEI) for multi-faceted test cases classification (112) and selection (110).
[0033] In this aspect, the fitness of the particular test case is defined as the ratio of intra class ambiguity (108) to total ambiguity (108) of the specific test case. The FFEI for the qth test case of jth class is defined as
(FFEI)q = (Hqj)/ Hqj
where n is the number of test cases, m is the number of classes, j = 1, 2, 3,..., 1 and q = 1, 2, 3,..., m. Hqj is the fuzzy entropy measure of the intra-class ambiguities values of the jth class, andf Hqj represents the total ambiguity (108) measure of the qth test case. For a
multiple class problem, the average of the (FFEI)q values of all the possible pairs of classes
is taken as the measure of goodness of the qth test cases.
[0034] In yet another aspect, the selection (110) and classification (112) of test cases are made by similarity-based selection (110) and similarity-based classification (112) using fuzzy entropy evaluating index (FFEI). Similarity-based selection (110) and classification (112) comprises of creation of ideal vectors that represent a particular class, wherein this vector is user-defined or calculated from some sample set, calculating similarities values between the sample test cases, calculating the uncertainty in fitness and classification (112) of the test case by using fuzzy entropy formula, removing all the test cases having high entropy value, exercising and auditing the remaining test cases on the SUT.
[0035] In yet another aspect, in similarity-based test case classification (112) and selection (110), first of all, the ideal vectors "Vi = (vi(p), ... , vi(pt))" is created that relates to the ith class. This vector is user-defined or is calculated from some sample set TCi, where TCi= (Tci(p), Tci(p2), Tci(p3), ... , Tci(pt)), is known as the array of degree belongingness of test cases to class Ci, wherein, Tci(pl) = (tcl(pl), tc2(pl), tc3(pl), ... , tcm(pl)), and, tcl(p1) represents the degree of membership of first test case to ith class on pl parameter. It is done in a simple way by using the generalized mean. Further, ideal vectors have been calculated, we have calculated the similarities values S(Tc, Vi) between the sample test case tc, which are to be classified along with the ideal vector. The sample test case tc class is decided according to the similarity value of the sample tc with the ideal vector (Vi). Now, in the ideal case, if a test case tc belongs to ith class, then the similarity value of that sample test case with ideal vector Vi of ith class is unity, i.e. , S (Tc, Vi)= 1. If the sample test case
'tc' does not belong to the ith class in the ideal case, then the similarity values of that sample test case with ideal vector Vi of ith class is zero, that is, S(Tc, Vi) = 0. The similarities calculations between the M sample test cases tcs with ideal vectors give M similarity values.
[00361 In yet another aspect, the fuzzy entropy measures are used to calculate the ambiguity (108) of the test case (tc) with ith class. Ambiguity (108) or uncertainty in fitness and classification (112) of the test case is calculated by using the fuzzy entropy formula, where gj(xi) are the similarity values of the test case q for class j on parameter value xi. If the test case tc has high or low similarity values, then it has low ambiguity (108) (low entropy) in fitness and classification (112). If the test case has similarity values close to 0.5, then it has high ambiguity (108) (high entropy values) in fitness and classification (112). Using this underlying idea, fuzzy entropy values for all the sample test cases of the vector are calculated by using similarity and ideal vector values. t x 1 entropy values are obtained for the sample test case tc as we have t as the fitness parameters and 1 as the number of classes. Thereafter, entropy values are obtained for the sample test case tc by summing the t entropy values. These values indicate the intra-classes ambiguity (108) of a particular test case tc. Further, the total entropy value of the particular test case tc is calculated by summing the 1 entropy values of all classes on all fitness parameters. Similarly, the total entropy values of the test suite of M sample test cases are calculated.
[00371 In yet another aspect, based on this underlying assumption, those test cases tcs having the entropy values greater than the threshold or the cut-point value of entropy are found. The decision for removing the test cases is made on the basis of the entropy value. The test cases, which have the entropy (108) value greater than the cut-point or threshold value ambiguity (108), are removed from the test suite. It is to be assumed that the test cases having the ambiguity (108) value greater than the threshold value are not useful and not contributing much to the deviation between classes. The most informative test cases are the ones with the lowest entropy values. After removing all the test cases having (ambiguity (108)) values greater than the cut-point, the rest of the test cases are selected for exercising and auditing on the SUT.
[00381 While the foregoing describes various embodiments of the invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof. The scope of the invention is determined by the claims that follow. The invention is not limited to the described embodiments, versions or examples, which are included to enable a person having ordinary skill in the art to make and use the invention when combined with information and knowledge available to the person having ordinary skill in the art.
[00391 Thus, the scope of the present disclosure is defined by the appended claims and includes both combinations and sub-combinations of the various features described hereinabove as well as variations and modifications thereof, which would occur to persons skilled in the art upon reading the foregoing description.
Claims (6)
1. A fuzzy entropy-based method (100) for software multi-faceted test case classification and selection, wherein said method (100) comprises steps of:
identifying (102) parameters and sub-parameters for test case fitness evaluation;
determining (104) weight calculations and distribution approach for obtaining the values of the weights of different parameters, sub-parameters, and objectives;
calculating (106) metric value of indexed items using inspection methods;
estimating (108) ambiguity in multi-criteria fitness of test cases using fuzzy entropy approach;
selecting (110) test cases using estimated ambiguity (108);
classifying (112) the selected test cases using estimated ambiguity (108).
2. The fuzzy entropy-based method (100) for software multi-faceted test case classification and selection as claimed in claim 1, wherein estimating ambiguity (108) in multi-criteria fitness of test cases using fuzzy entropy approach comprises steps of:
calculating the fitness of the particular test as the ratio of intra-class ambiguity to total ambiguity of the specific test case, wherein ambiguity infitness is considered as the fitness of the test case;
awarding ECIF certificate to the test cases having ambiguity values greater than or equal to the cut-point value;
awarding EQCF certificate to the test cases having ambiguity values lesser than the cut-point value;
removing the test cases having EICF certificate from the test suit.
3. The fuzzy entropy-based method (100) for software multi-faceted test case classification and selection as claimed in claim 1, wherein the selection (110) and classification (112) of test cases are made by similarity-based selection and similarity based classification using fuzzy entropy evaluating index (FFEI).
4. The fuzzy entropy-based method (100) for software multi-faceted test case classification (112) and selection (110) as claimed in claims 1 and 3, wherein similarity based selection and classification comprises of:
creation of ideal vectors that represent a particular class, wherein this vector is user-defined or calculated from some sample set;
calculating similarities values between the sample test cases;
calculating the uncertainty in fitness and classification of the test case by using fuzzy entropy formula;
removing all the test cases having high entropy value;
exercising and auditing the remaining test cases on the SUT.
5. The fuzzy entropy-based method (100) for software multi-faceted test case classification and selection as claimed in claims 1 and 4, wherein the mean-entropy discretization approach is used to split the test cases into two regions, high ambiguity, and low ambiguity regions.
6. The fuzzy entropy-based method (100) for software multi-faceted test case classification and selection as claimed in claims 1 and 2, wherein the cut-point for ambiguity is calculated from the mean of the entropy values of all test cases.
Application no.: Total no. of sheets: 1 Aug 2021 2021105543 Page 1 of 1
Fig.1 Flowchart of the fuzzy entropy-based method for software multi-faceted test case classification and selection.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116804971A (en) * | 2023-08-22 | 2023-09-26 | 上海安般信息科技有限公司 | Fuzzy test method based on information entropy |
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| CN116804971A (en) * | 2023-08-22 | 2023-09-26 | 上海安般信息科技有限公司 | Fuzzy test method based on information entropy |
| CN116804971B (en) * | 2023-08-22 | 2023-11-07 | 上海安般信息科技有限公司 | Fuzzy test method based on information entropy |
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