CN111459830B - Test case generation method and device - Google Patents
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Abstract
The invention provides a test case generation method and a device, which relate to the technical field of testing, and the method comprises the following steps: acquiring a semantic network database; the semantic network database comprises sentences and case description data; each statement and at least one case description data form a mapping relation; the statement comprises business term data and predicate relation data; determining at least one data combination according to the statement and business term data and predicate relation data; each data combination includes one or more sentences, and for any one of the plurality of sentences, there is at least one sentence in the plurality of sentences that includes the same business term data as any one sentence; a plurality of test cases is generated from each data combination and case description data. According to the invention, the test cases can be automatically generated based on the semantic network database, so that the generation efficiency of the test cases is improved, more complete test cases can be obtained, and the sufficiency of the test can be improved.
Description
Technical Field
The present invention relates to the field of testing technologies, and in particular, to a method and apparatus for generating a test case.
Background
Test case design is an important behavioral activity in software testing. The sufficiency of the test cases is of great importance to whether the system is delivered with high quality. At present, in the design work of functional test cases, the test cases are often designed manually based on natural language, so that the method is not only low in efficiency, but also is easy to cause incomplete test cases and insufficient test due to capability difference of designers, so that important software quality defects cannot be found.
Disclosure of Invention
The invention provides a test case generation method and a device, which can effectively improve the efficiency of generating test cases, can obtain more complete test cases, and improve the sufficiency of testing so as to discover software quality defects in time.
In a first aspect, an embodiment of the present invention provides a test case generating method, including: acquiring a semantic network database; the semantic network database comprises sentences and case description data; each statement and at least one case description data form a mapping relation; the statement comprises business term data and predicate relation data; the case description data is test case summary information which is generated in advance based on sentences in the semantic network database and is used for describing the execution content and result of the test cases; determining at least one data combination from the statement and the business term data and the predicate relationship data; each of the data combinations comprises one or more sentences, and for any one of the plurality of sentences, there is at least one sentence in the plurality of sentences comprising the same business term data as the any one sentence; generating a plurality of test cases from each of the data combinations and the case description data;
before obtaining the semantic network database, the method further comprises:
acquiring an initial semantic network database; the initial semantic network database is generated according to natural language;
and performing form processing on the initial semantic network database by using an extensible markup language to obtain a semantic network database.
In a second aspect, an embodiment of the present invention further provides a test case generating apparatus, where the apparatus includes: the acquisition module is used for acquiring the semantic network database; the semantic network database comprises sentences and case description data; each statement and at least one case description data form a mapping relation; the statement comprises business term data and predicate relation data; the case description data is test case summary information which is generated in advance based on sentences in the semantic network database and is used for describing the execution content and result of the test cases; a determining module for determining at least one data combination from the sentence and the business term data and the predicate relationship data; each of the data combinations comprises one or more sentences, and for any one of the plurality of sentences, there is at least one sentence in the plurality of sentences comprising the same business term data as the any one sentence; a case generation module for generating a plurality of test cases according to each of the data combinations and the case description data;
the system also comprises a form processing module for:
acquiring an initial semantic network database; the initial semantic network database is generated according to natural language;
and performing form processing on the initial semantic network database by using an extensible markup language to obtain a semantic network database.
In a third aspect, an embodiment of the present invention further provides a computer device, including a memory, and a processor, where the memory stores a computer program that can run on the processor, and the processor implements the test case generating method when executing the computer program.
In a fourth aspect, embodiments of the present invention also provide a computer readable medium having non-volatile program code executable by a processor, the program code causing the processor to perform the test case generation method described above.
The embodiment of the invention has the following beneficial effects: the embodiment of the invention provides a test case generation scheme, which comprises the steps of obtaining sentences and case description data by acquiring a semantic network database, wherein each sentence and at least one case description data form a mapping relation, the sentences comprise business term data and predicate relation data, then determining at least one data combination according to the sentences, the business term data and the predicate relation data, each data combination comprises one or more sentences, and for any one sentence in a plurality of sentences, at least one sentence in the plurality of sentences comprises business term data identical to any sentence; the business term data in each data combination has a direct or indirect correlation, and the semantic network database comprises a mapping relation between sentences and case description data, so that more complete test cases can be generated according to the data combination and the case description data. According to the embodiment of the invention, the test cases can be automatically generated based on the semantic network database, so that the generation efficiency of the test cases is improved, more complete test cases can be obtained, and the sufficiency of the test can be improved.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
In order to make the above objects, features and advantages of the present invention more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a test case generation method according to an embodiment of the present invention;
fig. 2 is a flowchart of an implementation of a test case generation method according to an embodiment of the present invention;
fig. 3 is a block diagram of a test case generating device according to an embodiment of the present invention;
fig. 4 is a block diagram of another test case generating device according to an embodiment of the present invention;
fig. 5 is a block diagram of a computer device according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
At present, the existing first test case generation method carries out test case generation by constructing a model of software to be tested, and the specific implementation steps are as follows:
1. establishing a general software ontology model;
2. instantiating a model of software to be tested, and carrying out formal expression of an ontology by using an ontology editor prot (protein software for carrying out semantic description on the ontology) based on OWL (a network ontology language, which provides construction of ontology concept classes, relationships, attributes and instances, and shields a specific ontology description language, wherein a user only needs to carry out construction of an domain ontology model on a concept level);
3. establishing rules conforming to business logic, expressing by using SWRL (Semantic Web Rule Language, a language for presenting the rules in a semantic manner), wherein the concept of the rule part of the SWRL is evolved by RuleML (Rule Machine Learning, machine learning rule), and is formed by combining with OWL ontology;
4. converting OWL ontology into Jess (rule engine on Java platform) facts, and generating test cases by SWRL rule Jess rules and Jess facts;
5. the use case is entered and a scripting language is generated based on the script of Jena (related applications used to support the semantic web).
The existing second test case generation method generates test cases by processing the software specification, and the specific implementation steps are as follows:
1. formalizing a specification by adopting a Z language (a normalized language for describing a computer system by using 'mathematical characters' or 'mathematical symbols', and only performing functional description on a target software system);
2. dividing predicates of patterns in the specification into a plurality of sub-patterns;
3. determining valid test data for determining the designed variables in each sub-mode;
4. and generating a test case.
In the first test case generation method, the ontology editor prot g e based on OWL is adopted to perform the formalized expression of the ontology, and the software to be tested needs to be firstly subjected to the materialized formalized expression of the ontology. In the second test case generation method, the first step is to perform the Z-language formal expression on the requirement specification. Formalized grammar expression is generally based on set theory, mathematical logic or algebra, and has high technical threshold requirement on writers. However, the specification is usually written by a business person, and it is difficult to put formal engineering requirements on the specification. The requirement document needs to be converted for the second time, deviation is easy to occur in the conversion process, and formal mode is difficult to develop in actual work.
Based on this, the test case generation method and device provided by the embodiment of the invention can generate the test case through the requirement specification written based on the natural language written by the service personnel, without using a large number of training data sets, can describe service logic and limitation more accurately by using the principle, and can realize the improvement of the sufficiency and the generation efficiency of the semi-structured natural language generated test case processed based on the requirement specification. The method has stronger pertinence and can be suitable for a test case design analysis method of a financial system.
For the sake of understanding the present embodiment, a detailed description will be first given of a test case generating method disclosed in the present embodiment.
The embodiment of the invention provides a test case generation method, referring to a flow chart of the test case generation method shown in fig. 1, comprising the following steps:
step S102, acquiring a semantic network database.
In an embodiment of the invention, a semantic web database is used to store statement and case description data. Wherein the statement is determined by the business term data and the predicate relationship data. Each statement forms a mapping relationship with at least one case description data.
Wherein, the service term data is used for describing the term information commonly used in a certain service field. For example, for banking systems, business term statements may include transfer balance, account name, entry fields, and monetary value information. Predicate relationship data is used to describe relationships between business term data, e.g., predicate relationship data may include "greater than", "less than", "yes", "no", "and", "or" and "not", etc. The statement is used to describe predicate relationships between business term data, for example, the statement may be: "transfer balance greater than 5 ten thousand yuan", wherein "transfer balance" and "5 ten thousand yuan" are business term data, and "greater than" is predicate relation data. A statement may also describe various predicate relationships between multiple business term data. The case description statements are test case summary information that is generated in advance based on statements in the semantic web database for describing the contents and results of test case execution. For example, case description data corresponding to the statement "transfer balance greater than 5 ten thousand" may be: "when the transfer balance is greater than 5 ten thousand yuan, the a information is displayed", or the corresponding case description data may be: "when the transfer balance is less than 5 ten thousand yuan, B information is displayed", etc. Each statement may correspond to a plurality of case description data forming a one-to-many mapping relationship.
It should be noted that, in the embodiment of the present invention, related personnel may test related business term data and predicate relationships between the business term data in advance according to definitions, and after determining a sentence, establish a mapping relationship between terms and case description data to obtain a semantic web database.
Step S104, at least one data combination is determined according to the statement, the business term data and the predicate relation data.
In the embodiment of the invention, each statement in the semantic network database comprises business term data and predicate relations among the business term data. The data combination may include only one statement and predicate relation data between the business term data in the statement, or may include a plurality of statements and predicate relation data between the business term data in the plurality of statements.
Each data combination includes one or more sentences, and for any one of the plurality of sentences, there is at least one sentence in the plurality of sentences that includes the same business term data as any one sentence. For example, for one data combination, statement 1 is included: a or B, statement 2: b and C, statement 3: c, not D, statement 4: a is greater than E. Wherein, for statement 1, both present statement 4 and statement 1 include business term data a, and both present statement 2 and statement 1 include business term data B.
Step S106, generating a plurality of test cases according to each data combination and case description data.
In the embodiment of the invention, logic expansion is performed on each data combination, and according to the expansion result, the interrelationship between the business term data in the data combination is described, and as each statement and at least one case description data form a mapping relation, a plurality of test cases can be generated according to the interrelationship between the business term data in the data combination.
The embodiment of the invention provides a test case generation scheme, which comprises the steps of obtaining sentences and case description data by acquiring a semantic network database, wherein each sentence and at least one case description data form a mapping relation, the sentences comprise business term data and predicate relation data, then determining at least one data combination according to the sentences, the business term data and the predicate relation data, each data combination comprises one or more sentences, and for any one sentence in a plurality of sentences, at least one sentence in the plurality of sentences comprises business term data identical to any sentence; the business term data in each data combination has a direct or indirect correlation, and the semantic network database comprises a mapping relation between sentences and case description data, so that more complete test cases can be generated according to the data combination and the case description data. According to the embodiment of the invention, the test cases can be automatically generated based on the semantic network database, so that the generation efficiency of the test cases is improved, more complete test cases can be obtained, and the sufficiency of the test can be improved.
Considering that in order to facilitate reducing the technical threshold requirements for writers, the following steps may be further performed before the semantic web database is acquired:
acquiring an initial semantic network database; generating an initial semantic network database according to natural language; and performing form processing on the initial semantic network database by using the extensible markup language to obtain the semantic network database.
In the embodiment of the invention, the initial semantic network database can be generated based on a requirement specification by using natural language, and in order to facilitate the identification of a computer, the initial semantic network database is processed in a form by using extensible markup language (Extensible Markup Language, XML), so that the initial semantic network database in an XML form can be obtained, and the semantic network database is obtained.
In view of the fact that at least one data combination is determined from the statement and business term data and the predicate relation data in order to increase the processing efficiency, it may be performed as follows:
adding sentences including the same business term data to the same first packet; and determining predicate relations among the business term data in the first group according to the predicate relation data to obtain a data combination.
In the embodiment of the invention, if different sentences include the same business term data, such sentences can be added into the same first group, and the predicate relation among the business term data in the first group is determined according to the predicate relation data among the business term data in the sentences, so as to obtain the data combination.
For example, for statement 1: a or B, statement 2: b and C, statement 3: c, not D, statement 4: a is greater than E, statement 5: m is smaller than N, wherein statement 1 and statement 2 can be used as one data combination because both statement 1 and statement 2 contain business term data B, statement 3 can be added into the combination where statement 2 is located because both statement 2 and statement 3 contain business term data C, statement 4 and statement 1 contain business term data A, statement 4 can be added into the combination where statement 1 is located, statement 5 and the remaining four statements have no same business term data, and statement 5 can independently obtain one data combination. Thus, according to statement 1 through statement 5, two data combinations can be obtained, the first data combination including statement 1 through statement 4, business term data A, B, C, D, E, and predicate relationship terms between the business term data.
In order to enhance the richness of the test cases and thus the sufficiency of the test, generating a plurality of test cases according to each data combination and case description data may be performed as follows:
generating a disjunctive normal form or a conjunctive normal form according to each data combination; a plurality of test cases are generated according to the disjunctive normal form and the case description data, or a plurality of test cases are generated according to the conjunctive normal form and the case description data.
In the embodiment of the present invention, a disjunctive form composed of a limited number of simple conjunctive forms is called a disjunctive paradigm. A conjunctive form consisting of a limited number of simple disjunctive forms is called a conjunctive normal form. According to the business term data and predicate relation data of each statement in the data combination, the mutual relation can be determined to be a disjunctive normal form or a conjunctive normal form, and then the disjunctive normal form or the conjunctive normal form is respectively combined with the case description data, so that a plurality of test cases can be obtained.
For example, for statement 6: w is greater than X, statement 7: x is Q, statement 6 corresponds to case description data 1 and case description data 2, statement 7 corresponds to case description data 3 and case description data 4, statement 6 and statement 7 may generate data combination 1, logical expansion is performed on data combination 1, e.g., resulting in (W is greater than X) and (X is Q), and case description data 1 through 4 may be combined, e.g., case description data 1 and case description 3, case description data 2 and case description 3, case description data 1 and case description 4, case description data 2 and case description 3, and test cases are generated from the resulting combination of case description data.
The embodiment of the invention provides a test case generation method or device, referring to a flow chart of the test case generation method implementation shown in fig. 2, the method defines related terms, inter-term predicate relation and test classification mapping for test services to form a semantic network database, formalizes the database by utilizing an XML-based ontology semantic network description structure, constructs an engine based on the data model, performs semantic network search construction on input natural language to form a semantic tree and an attribute filling table, logically expands the constructed semantic tree and attribute table to form a paradigm, and forms text generation of a complete test set through the paradigm.
The embodiment of the invention formalizes by establishing a semantic database of the business and XML description of the ontology semantic network instead of using other deep learning methods. The semantic network database is obtained through natural language, a semantic tree is generated according to the semantic network database, logic is clear, the problems of uncertainty and unrepeatable in methods such as deep learning are eliminated, and the method is more applicable to the problem of strict logic requirements. The generation of the text test set by the logical expansion of the paradigm, rather than using other best effort approaches, is mathematically complete.
The embodiment of the invention also provides a test case generation device, referring to a structural block diagram of the test case generation device shown in fig. 3, the device comprises:
an acquisition module 71, configured to acquire a semantic network database; the semantic network database comprises sentences and case description data; each statement and at least one case description data form a mapping relation; the statement comprises business term data and predicate relation data; a determination module 72 for determining at least one data combination from the statement and business term data and the predicate relation data; each data combination includes one or more sentences, and for any one of the plurality of sentences, there is at least one sentence in the plurality of sentences that includes the same business term data as any one sentence; a case generation module 73 for generating a plurality of test cases from each data combination and case description data.
In one embodiment, referring to another block diagram of a test case generation apparatus shown in fig. 4, the apparatus further includes a form processing module 74 for: acquiring an initial semantic network database; generating an initial semantic network database according to natural language; and performing form processing on the initial semantic network database by using the extensible markup language to obtain the semantic network database.
In one embodiment, the determining module is specifically configured to: adding sentences including the same business term data to the same first packet; and determining predicate relations among the business term data in the first group according to the predicate relation data to obtain a data combination.
In one embodiment, the generating module is specifically configured to: generating a disjunctive normal form or a conjunctive normal form according to each data combination; a plurality of test cases are generated according to the disjunctive normal form and the case description data, or a plurality of test cases are generated according to the conjunctive normal form and the case description data.
The embodiment of the present invention further provides a computer device, referring to the schematic block diagram of the structure of the computer device shown in fig. 5, where the computer device includes a memory 81 and a processor 82, and the memory stores a computer program that can be run on the processor, and when the processor executes the computer program, the processor implements the steps of any of the methods described above.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described computer device may refer to corresponding procedures in the foregoing method embodiments, which are not repeated here
Embodiments of the present invention also provide a computer readable medium having non-volatile program code executable by a processor, the program code causing the processor to perform the steps of any of the methods described above.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above examples are only specific embodiments of the present invention, and are not intended to limit the scope of the present invention, but it should be understood by those skilled in the art that the present invention is not limited thereto, and that the present invention is described in detail with reference to the foregoing examples: any person skilled in the art may modify or easily conceive of the technical solution described in the foregoing embodiments, or perform equivalent substitution of some of the technical features, while remaining within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention, and are intended to be included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (4)
1. A method of generating test cases, comprising:
acquiring a semantic network database; the semantic network database comprises sentences and case description data; each statement and at least one case description data form a mapping relation; the statement comprises business term data and predicate relation data; the case description data is test case summary information which is generated in advance based on sentences in the semantic network database and is used for describing the execution content and result of the test cases; business term data is used to describe term information commonly used in a business field; predicate relation data is used to describe the relation between business term data;
determining at least one data combination from the statement and the business term data and the predicate relationship data; each of the data combinations comprises one or more sentences, and for any one of the plurality of sentences, there is at least one sentence in the plurality of sentences comprising the same business term data as the any one sentence; if different sentences comprise the same business term data, adding the sentences into the same first group, and determining predicate relations among the business term data in the first group according to predicate relation data among the business term data in the sentences to obtain a data combination;
generating a plurality of test cases from each of the data combinations and the case description data;
before obtaining the semantic network database, the method further comprises:
acquiring an initial semantic network database; the initial semantic network database is generated according to natural language;
performing form processing on the initial semantic network database by using an extensible markup language to obtain a semantic network database;
wherein generating a plurality of test cases from each of the data combinations and the case description data comprises:
generating a disjunctive normal form or a conjunctive normal form according to each data combination;
generating a plurality of test cases according to the disjunctive normal form and the case description data, or generating a plurality of test cases according to the conjunctive normal form and the case description data.
2. A test case generation apparatus, comprising:
the acquisition module is used for acquiring the semantic network database; the semantic network database comprises sentences and case description data; each statement and at least one case description data form a mapping relation; the statement comprises business term data and predicate relation data; the case description data is test case summary information which is generated in advance based on sentences in the semantic network database and is used for describing the execution content and result of the test cases; business term data is used to describe term information commonly used in a business field; predicate relation data is used to describe the relation between business term data;
a determining module for determining at least one data combination from the sentence and the business term data and the predicate relationship data; each of the data combinations comprises one or more sentences, and for any one of the plurality of sentences, there is at least one sentence in the plurality of sentences comprising the same business term data as the any one sentence; if different sentences comprise the same business term data, adding the sentences into the same first group, and determining predicate relations among the business term data in the first group according to predicate relation data among the business term data in the sentences to obtain a data combination;
a case generation module for generating a plurality of test cases according to each of the data combinations and the case description data;
the system also comprises a form processing module for:
acquiring an initial semantic network database; the initial semantic network database is generated according to natural language;
performing form processing on the initial semantic network database by using an extensible markup language to obtain a semantic network database;
the case generation module is specifically used for:
generating a disjunctive normal form or a conjunctive normal form according to each data combination;
generating a plurality of test cases according to the disjunctive normal form and the case description data, or generating a plurality of test cases according to the conjunctive normal form and the case description data.
3. A computer device comprising a memory, a processor, the memory having stored therein a computer program executable on the processor, characterized in that the processor implements the steps of the method of claim 1 when the computer program is executed.
4. A computer readable medium having non-volatile program code executable by a processor, the program code causing the processor to perform the method of claim 1.
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Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN103853652A (en) * | 2012-11-29 | 2014-06-11 | 百度在线网络技术(北京)有限公司 | Test case generation method and device |
CN108399130A (en) * | 2018-02-28 | 2018-08-14 | 平安科技(深圳)有限公司 | Automatically generate the method, apparatus, equipment and readable storage medium storing program for executing of test cases |
CN108763070A (en) * | 2018-05-16 | 2018-11-06 | 北京金山云网络技术有限公司 | Generation method, device and the electronic equipment of test data |
CN109815147A (en) * | 2019-01-21 | 2019-05-28 | 深圳乐信软件技术有限公司 | Test cases generation method, device, server and medium |
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Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN103853652A (en) * | 2012-11-29 | 2014-06-11 | 百度在线网络技术(北京)有限公司 | Test case generation method and device |
CN108399130A (en) * | 2018-02-28 | 2018-08-14 | 平安科技(深圳)有限公司 | Automatically generate the method, apparatus, equipment and readable storage medium storing program for executing of test cases |
CN108763070A (en) * | 2018-05-16 | 2018-11-06 | 北京金山云网络技术有限公司 | Generation method, device and the electronic equipment of test data |
CN109815147A (en) * | 2019-01-21 | 2019-05-28 | 深圳乐信软件技术有限公司 | Test cases generation method, device, server and medium |
Non-Patent Citations (1)
Title |
---|
张志等.《高职高专国家示范性学校"十三五"规划教材 Android移动应用测试实战》.西安:西安电子科技大学出版社,2017,(第1版),18-27. * |
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