Automatic testing method, system and equipment based on internet of things model and medium

Abstract

The invention provides an automatic testing method and system based on an Internet of Things model, electronic equipment and a storage medium, and aims to solve the problems of low efficiency and incomplete test of compiling a model test case. The method comprises the following steps: exporting a specific format file of a product model on an Internet of Things platform, and analyzing the file to obtain a product model test case; extracting each field content under attributes, events and service function types; respectively storing the extracted field contents according to events, attributes and services; and automatically generating test input data for each test item in combination with the data type and the parameter value range in the field content through an effective equivalence class and boundary value method, and storing the automatically generated test input data in the test input data item of the corresponding test case file to complete the automatic generation of the test case. The test case can be automatically generated, the test efficiency is improved, and the timeliness, stability and reliability of the test case are effectively improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of Internet of Things, and in particular to an automated testing method based on an Internet of Things object model, an automated testing system based on an Internet of Things object model, an electronic device, and a computer-readable storage medium. Background Art

[0002] The technical background of the IoT platform standard object model mainly stems from the challenges of the diversity of IoT devices and the need for interoperability. With the continuous increase of IoT devices, how to ensure that these devices can communicate with the platform smoothly and efficiently has become an important issue. Therefore, the IoT platform has proposed the concept of a standard object model, which aims to achieve interoperability and data sharing between devices by defining a unified device description and control method, thereby simplifying the process of device access and management. These standard object models are usually formulated and promoted by industry standardization organizations to ensure that cross-brand and cross-category devices can be seamlessly integrated into the IoT ecosystem.

[0003] When testing the device to access the IoT platform, each identifier in the object model needs to be tested to ensure its uniqueness, accuracy, and functionality. Identifiers play a key role in the object model. They can uniquely identify each instance or attribute in the object model. When writing test cases, it is necessary to write relevant test cases based on the data type, range, length and other restrictions of each identifier in the object model. When there are many types of connected products and the functions of the product object models are more complex, such as when the object models of each connected product are different, it takes a lot of time to write test cases and manually execute test cases during the testing phase.

[0004] The current method of manually writing physical model test cases and executing tests is inefficient. When there are differences and changes in physical models, the manual test case reuse rate is low and it is difficult to repeat the execution. Especially when time is tight and the task is heavy, testers may not be able to cover all scenarios and test cases, which may lead to incomplete testing and potential risks. Summary of the invention

[0005] In order to at least solve the problems in the prior art that the current methods of writing object model test cases and executing tests are inefficient, and cannot cover all scenarios and test cases, which may lead to incomplete testing and potential risks, the present disclosure provides an automated testing method based on the Internet of Things object model, an automated testing system based on the Internet of Things object model, an electronic device, and a computer-readable storage medium, which can automatically generate test cases, save time in writing test cases, improve test efficiency, and effectively improve the timeliness, stability, and reliability of test cases.

[0006] In a first aspect, the present disclosure provides an automated testing method based on an IoT object model, the method comprising:

[0007] Export a specific format file of the product object model on the IoT platform, and extract the contents of each field under the attribute, event, and service function type by parsing the file;

[0008] The extracted field contents are saved separately by events, attributes, and services;

[0009] For each test item, the data type and parameter value range in the field content are combined, and the test input data is automatically generated through the effective equivalence class and boundary value method. The above automatically generated test input data is saved in the test input data item of the corresponding test case file to complete the automatic generation of the test case.

[0010] Furthermore, the extracted field contents are classified by events, attributes, services,

[0011] Don't save include:

[0012] The extracted field contents are saved into different sheets of the Excel file according to events, attributes and services;

[0013] The field content includes function name, function identifier, data type, parameter name, parameter identifier, and parameter value range.

[0014] Furthermore, the test items are combined with the data type and parameter value range in the field content to automatically generate test results through the effective equivalence class and boundary value method.

[0015] The input data includes:

[0016] For each test item, by combining the data type and its corresponding parameter value range, the valid equivalence class value and invalid equivalence class value of the test input data are automatically generated through the effective equivalence class partitioning method combined with the boundary value method to obtain the test input data.

[0017] Furthermore, the method further comprises:

[0018] Read the automatically generated test case file, and concatenate the function identifier, parameter identifier, and test input data in the test case file into a message body;

[0019] Call the relevant data reporting interface to send the spliced ​​message body to complete the data reporting operation;

[0020] After the data is reported, the actual interface return results are written into the test case file.

[0021] In a second aspect, the present disclosure provides an automated testing system based on an Internet of Things model, the system comprising:

[0022] A parsing module is configured to export a specific format file of the product object model on the IoT platform, and extract the contents of each field under the attribute, event, and service function type by parsing the file;

[0023] A saving module is configured to save the extracted field contents by event, attribute, and service respectively;

[0024] The generation module is configured to automatically generate test input data for each test item in combination with the data type and parameter value range in the field content through the effective equivalence class and boundary value method, and save the above automatically generated test input data to the test input data item of the corresponding test case file to complete the automatic generation of test cases.

[0025] Furthermore, the storage module is specifically configured as follows:

[0026] The extracted field contents are saved into different sheets of the Excel file according to events, attributes and services;

[0027] The field content includes function name, function identifier, data type, parameter name, parameter identifier, and parameter value range.

[0028] Furthermore, the generation module is specifically configured as follows:

[0029] For each test item, by combining the data type and its corresponding parameter value range, the valid equivalence class value and invalid equivalence class value of the test input data are automatically generated through the effective equivalence class partitioning method combined with the boundary value method to obtain the test input data.

[0030] Furthermore, the system also includes a reporting module and a result writing module:

[0031] The reporting module is configured to read the automatically generated test case file, and splice the function identifier, parameter identifier, and test input data in the test case file into a message body; and

[0032] Call the relevant data reporting interface to send the spliced ​​message body to complete the data reporting operation;

[0033] The result writing module is configured to write the actual interface return result into the test case file after the data is reported.

[0034] In a third aspect, the present disclosure provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and when the processor runs the computer program stored in the memory, the processor executes the automated testing method based on the Internet of Things model as described in any one of the first aspects.

[0035] In a fourth aspect, the present disclosure provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the computer program implements any one of the automated testing methods based on the Internet of Things model described in the first aspect above.

[0036] Beneficial effects:

[0037] The automated testing method based on the Internet of Things object model, the automated testing system based on the Internet of Things object model, the electronic device and the storage medium provided by the present invention generate corresponding use case templates according to the differences in attributes, events and service characteristics by parsing the specific format file of the object model, and automatically generate test data using the equivalence class partitioning method and the boundary value method within the value range of different types of function identifiers of the object model. The test cases can be automatically generated, which saves the time of writing test cases, improves the testing efficiency, and effectively improves the timeliness, stability and reliability of the test cases. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 A flowchart of an automated testing method based on an Internet of Things object model provided in the first embodiment of the present disclosure;

[0039] Figure 2 A saved use case file diagram provided for an embodiment of the present disclosure;

[0040] Figure 3 A flowchart of an automated testing method based on an Internet of Things object model provided in Embodiment 2 of the present disclosure;

[0041] Figure 4 An architecture diagram of an automated testing system based on an Internet of Things object model provided in Embodiment 3 of the present disclosure;

[0042] Figure 5 This is an architecture diagram of an electronic device provided in Embodiment 4 of the present disclosure. DETAILED DESCRIPTION

[0043] In order to enable those skilled in the art to better understand the technical solution of the present disclosure, the present disclosure is further described in detail below in conjunction with the drawings and embodiments. It should be understood that the specific embodiments and drawings described herein are only used to explain the present invention, rather than to limit the present invention.

[0044] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence; and, in the absence of conflict, the embodiments in the present disclosure and the features in the embodiments can be arbitrarily combined with each other.

[0045] The terms used in the embodiments of the present disclosure are only for the purpose of describing specific embodiments and are not intended to limit the present disclosure. The singular forms of "a", "said" and "the" used in the embodiments of the present disclosure and the appended claims are also intended to include plural forms unless the context clearly indicates other meanings.

[0046] In the subsequent description, the suffixes such as "module", "component" or "unit" used to represent elements are only used to facilitate the description of the present disclosure, and have no specific meanings. Therefore, "module", "component" or "unit" can be used in a mixed manner.

[0047] The technical solution of the present invention and how the technical solution of the present invention solves the above-mentioned problems existing in the prior art are described in detail below with specific embodiments. It is understandable that in the embodiments of the present application, the execution subject can perform some or all of the steps in the embodiments of the present application, and these steps or operations are only examples. The embodiments of the present application can also perform other operations or deformations of various operations. In addition, each step can be performed in a different order presented in the embodiments of the present application, and it is possible not to perform all the operations in the embodiments of the present application. And, the following several specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments.

[0048] Figure 1 A flowchart of an automated testing method based on an IoT object model provided in the first embodiment of the present disclosure is shown in FIG. Figure 1 As shown, the method includes:

[0049] Step S101: export a specific format file of the product object model on the Internet of Things platform, and extract the contents of each field under the attribute, event and service function type by parsing the file;

[0050] Step S102: The extracted field contents are saved separately according to events, attributes and services;

[0051] Step S103: For each test item, respectively, the data type and parameter value range in the field content are combined to automatically generate test input data through the effective equivalence class and boundary value method, and the above automatically generated test input data is saved to the test input data item of the corresponding test case file to complete the automatic generation of the test case.

[0052] The disclosed embodiment utilizes a file of a standard object model (such as a json file) to automatically generate a test case of a preset template, and then combines the reported attributes, events, or service API data passed by the device in the interaction with the IoT platform to upload the identifier in the use case template and the input test data covering various scenarios to complete the data exchange between the device and the IoT platform.

[0053] For the object model, the object model describes what the entity is, what it can do, and what information it can provide to the outside world from the three dimensions of attributes, services, and events. Defining these three dimensions of the object model completes the definition of product functions. The description of each function type is shown in Table 1 below:

[0054] Table 1: Object model function types and descriptions

[0055]

[0056]

[0057] The disclosed embodiment exports a product object model .json format file on an Internet of Things platform, parses the json file through a tool such as a Python script json package, and extracts the contents of various fields under attributes, events, and service function types.

[0058] The field contents extracted through object model analysis are saved separately according to events, attributes, and services; if saved in Excel files, the files saved for events, attributes, and services are automatically generated according to each test item (and each function to be tested) through the effective equivalence class and boundary value method based on the data type and parameter value range in the parsed field content. The automatically generated test input data is saved in the test input data item of the corresponding test case file to complete the automatic generation of test cases; the test input data covers all scenarios and necessary test cases.

[0059] Equivalence class partitioning is an important and commonly used black box testing method. It does not need to consider the internal structure of the program, but only the input specifications of the program. The basic idea of ​​equivalence class partitioning is to first divide the possible data into different categories, and then select representative data from each category for testing. The data selected in this way can represent all the data in this category. In this way, the number of test cases can be reduced.

[0060] Equivalence classification requires dividing all possible user input data into several parts (several subsets), and then selecting a few representative data from each subset as test cases. By rationally classifying the test processes that cannot be exhaustively listed, the designed test cases are guaranteed to be complete and representative. With limited test resources, a small amount of representative data can achieve better test results.

[0061] By combining valid equivalence classes with the boundary value method, various valid equivalence class values ​​and invalid equivalence class values ​​can be automatically generated. Through the generated test cases, the device is required to not only receive reasonable data but also respond correctly to unreasonable data, ensuring that the device software has higher reliability.

[0062] The disclosed embodiments can automatically generate test case test data, saving the time of writing test cases, improving testing efficiency, and enabling test data backtracking; the generated test cases can cover all application scenarios, effectively improving the timeliness, stability and reliability of the test cases.

[0063] Furthermore, the extracting field contents are saved separately according to events, attributes and services, including:

[0064] The extracted field contents are saved into different sheets of the Excel file according to events, attributes and services;

[0065] The field content includes function name, function identifier, data type, parameter name, parameter identifier, and parameter value range.

[0066] The field contents extracted by object model analysis are saved into three sheets of Excel files according to events, attributes, and services. The function names, function identifiers, data types, parameter names, parameter identifiers, and parameter value ranges of events, attributes, and services are parsed and used as columns of the sheets respectively; Figure 2 As shown in the figure, it is the field content of the event sheet, and the function name and function identifier correspond to the event name and event identifier.

[0067] Furthermore, the step of automatically generating test input data by combining the data type and parameter value range in the field content with each test item through the effective equivalence class and boundary value method includes:

[0068] For each test item, by combining the data type and its corresponding parameter value range, the valid equivalence class value and invalid equivalence class value of the test input data are automatically generated through the effective equivalence class partitioning method combined with the boundary value method to obtain the test input data.

[0069] For each test item (and the function to be tested), combined with the parsed data type and parameter value range, the test input data (corresponding to the "Input Test Data" column of the use case file) is generated through the effective equivalence class and boundary value method, such as Figure 2 , the data type (Data Type column) of the self-test reporting function is enum, and the data range (DataRange column) is an array [0, 1]. The test input data generated by the valid equivalence class partitioning method has N valid equivalence classes (values ​​0, 1) and 1 invalid equivalence class (any number other than 0 and 1), where N is the number of data in the enumeration value, and the valid equivalence class input data is 0, 1, and the invalid equivalence class is any number other than 0 and 1, such as the value 2; for example, the data type of the anti-dismantling alarm is float, and the data range is [0.0, 100.0]. Using the equivalence class partitioning method combined with the boundary value method, the generated test input data has 3 valid equivalence classes (0.0, 50.0, 100.0) and 2 invalid equivalence classes (-0.1, 100.1); according to the above algorithm, other data types such as int, double, text, bool in the Excel file are respectively used to generate relevant test input data using similar methods; the above automatically generated test input data is saved to the use case file Input Test In the Data column, the automatic generation of test cases is now completed.

[0070] Furthermore, the method further comprises:

[0071] Read the automatically generated test case file, and concatenate the function identifier, parameter identifier, and test input data in the test case file into a message body;

[0072] Call the relevant data reporting interface to send the spliced ​​message body to complete the data reporting operation;

[0073] After the data is reported, the actual interface return results are written into the test case file.

[0074] The code reads the automatically generated test case file. For example, for the function type of the event, the Event Identifier, Data Identifier, and Input Test Data in the test case file are read and spliced ​​into the following message body

[0075]

[0076] Then, the IoT device simulator calls the relevant data reporting interface to send the message body spliced ​​in the above steps to complete the data reporting operation.

[0077] After executing the data reporting operation, write the result returned by the actual interface into the test case file.

[0078] Through the above steps, the automatically generated test cases and test input data are spliced ​​into the reporting data message body, and the automated testing and test result writing are completed in combination with the automated testing tools or scripts, completing the whole process management from test case generation to execution and result monitoring in one stop.

[0079] The disclosed embodiment parses the standard object model json file and automatically generates a test case file through a json parsing tool such as a json library of a Python script, and then combines the reported attributes, events, or service API data message bodies of the interaction between the device and the Internet of Things platform to complete the data exchange between the device and the Internet of Things platform. It can automatically generate test case test data and automatically execute test cases, saving the time of writing and executing test cases, improving test efficiency, and achieving test data traceability; and the generated test data can cover all scenarios and test cases to ensure the reliability, timeliness and stability of the test. When a new product is connected to the Internet of Things platform or when the original object model changes, the new object model json file can be used to quickly regenerate test cases and test data to quickly respond to new test requirements. Testers no longer need to spend extra time and energy to manage and maintain test cases and test data; and the method of automatically executing test cases is compatible with a variety of product access protocols, such as MQTT, HTTP, CoAP, TR069 and other protocols.

[0080] The second embodiment of the present disclosure also provides an automated testing method based on the IoT object model, such as Figure 3 As shown, the method includes:

[0081] Step 1: Export the product object model .json format file on the IoT platform, parse the json file with the Python script json package, and extract the function name, function identifier, data type, parameter name, parameter identifier, parameter value range, etc. under the attribute, event, and service function type;

[0082] Step 2: Save the field contents extracted from the object model analysis in step 1 into three sheets of Excel files according to events, attributes, and services, such as Figure 2 As shown. Parse the event, attribute, service function name, function identifier, data type, parameter name, parameter identifier, and parameter value range and store them in the corresponding columns of the sheet.

[0083] Step 3: Combine the data type and value range parsed in step 2, and generate test input data (corresponding to the "Input Test Data" column of the use case file) through the effective equivalence class and boundary value method, such as Figure 2The data type (Data Type column) of the self-test reporting function is enum, and the data range (Data Range column) is an array [0, 1]. The test input data generated by the valid equivalence class partitioning method has N valid equivalence classes (values ​​0, 1) and 1 invalid equivalence class (any number other than 0 and 1), where N is the number of data in the enumeration value, and the valid equivalence class input data is 0, 1, and the invalid equivalence class is any number other than 0 and 1, such as the value 2; for example, the data type of the anti-dismantling alarm function is float, and the data range is [0.0, 100.0]. The test input data generated by the equivalence class partitioning method combined with the boundary value method has 3 valid equivalence classes (0.0, 50.0, 100.0) and 2 invalid equivalence classes (-0.1, 100.1); according to the above algorithm, similar methods are used to generate relevant test input data for other data types such as int, double, text, bool, etc.; the above automatically generated test input data is saved in the Input Test Data column of the use case file, and the automatic generation of test cases is completed.

[0084] Step 4: The code reads the test case file automatically generated in step 3. For example, for an event, read the Event Identifier, Data Identifier, and Input Test Data in the test case file and splice them into the following message body

[0085]

[0086] Properties and services are read in a similar manner.

[0087] Step 5: The IoT device simulator calls the relevant data reporting interface to send the message body spliced ​​in step 4 to complete the data reporting operation.

[0088] Step 6: After executing the data reporting operation in step 5, write the result returned by the actual interface into the test case file.

[0089] The third embodiment of the present disclosure also provides an automated testing system based on the IoT object model, such as Figure 4 As shown, the system comprises:

[0090] A parsing module 11 is configured to export a specific format file of a product object model on the IoT platform, and extract the contents of each field under the attribute, event, and service function type by parsing the file;

[0091] A saving module 12, which is configured to save the extracted field contents according to events, attributes and services respectively;

[0092] The generation module 13 is configured to automatically generate test input data for each test item in combination with the data type and parameter value range in the field content through the effective equivalence class and boundary value method, and save the above automatically generated test input data to the test input data item of the corresponding test case file to complete the automatic generation of the test case.

[0093] Furthermore, the storage module 12 is specifically configured as follows:

[0094] The extracted field contents are saved into different sheets of the Excel file according to events, attributes and services;

[0095] The field content includes function name, function identifier, data type, parameter name, parameter identifier, and parameter value range.

[0096] Furthermore, the generating module 13 is specifically configured as follows:

[0097] For each test item, by combining the data type and its corresponding parameter value range, the valid equivalence class value and invalid equivalence class value of the test input data are automatically generated through the effective equivalence class partitioning method combined with the boundary value method to obtain the test input data.

[0098] Furthermore, the system also includes a reporting module 14 and a result writing module 15:

[0099] The reporting module 14 is configured to read the automatically generated test case file, and splice the function identifier, parameter identifier, and test input data in the test case file into a message body; and

[0100] Call the relevant data reporting interface to send the spliced ​​message body to complete the data reporting operation;

[0101] The result writing module 15 is configured to write the actual interface return result into the test case file after the data is reported.

[0102] The automated testing system based on the IoT object model in the disclosed embodiment is used to implement the automated testing method based on the IoT object model in method embodiment 1 and embodiment 2, so the description is relatively simple. For details, please refer to the relevant description in the previous method embodiment, which will not be repeated here.

[0103] In addition, if Figure 5 As shown, the fourth embodiment of the present disclosure further provides an electronic device, including a memory 100 and a processor 200, wherein the memory 100 stores a computer program, and when the processor 200 runs the computer program stored in the memory 100, the processor 200 executes the above-mentioned various possible methods.

[0104] The memory 100 is connected to the processor 200. The memory 100 may be a flash memory, a read-only memory or other memory, and the processor 200 may be a central processing unit or a single-chip microcomputer.

[0105] In addition, an embodiment of the present disclosure further provides a computer-readable storage medium, on which a computer program is stored, and the computer program is executed by a processor to perform the above-mentioned various possible methods.

[0106] The computer-readable storage medium includes volatile or non-volatile, removable or non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, computer program modules or other data). Computer-readable storage media include, but are not limited to, RAM (Random Access Memory), ROM (Read-Only Memory), EEPROM (Electrically Erasable Programmable read only memory), flash memory or other memory technology, CD-ROM (Compact Disc Read-Only Memory), Digital Video Disc (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer.

[0107] It is to be understood that the above embodiments are merely exemplary embodiments used to illustrate the principles of the present disclosure, but the present disclosure is not limited thereto. For those of ordinary skill in the art, various modifications and improvements can be made without departing from the spirit and substance of the present disclosure, and these modifications and improvements are also considered to be within the scope of protection of the present disclosure.

Claims

1. An automated testing method based on an Internet of Things model, characterized in that: The method comprises: Export a specific format file of the product object model on the IoT platform, and extract the contents of each field under the attribute, event, and service function type by parsing the file; The extracted field contents are saved separately by events, attributes, and services; For each test item, the data type and parameter value range in the field content are combined, and the test input data is automatically generated through the effective equivalence class and boundary value method. The above automatically generated test input data is saved in the test input data item of the corresponding test case file to complete the automatic generation of the test case.

2. The method according to claim 1, characterized in that The extracted field contents are saved separately according to events, attributes and services, including: The extracted field contents are saved into different sheets of the Excel file according to events, attributes and services; The field content includes function name, function identifier, data type, parameter name, parameter identifier, and parameter value range.

3. The method according to claim 2, characterized in that The method of automatically generating test input data by combining the data type and parameter value range in the field content of each test item through the effective equivalence class and boundary value method includes: For each test item, by combining the data type and its corresponding parameter value range, the valid equivalence class value and invalid equivalence class value of the test input data are automatically generated through the effective equivalence class partitioning method combined with the boundary value method to obtain the test input data.

4. The method according to claim 2 or 3, characterized in that: The method further comprises: Read the automatically generated test case file, and concatenate the function identifier, parameter identifier, and test input data in the test case file into a message body; Call the relevant data reporting interface to send the spliced ​​message body to complete the data reporting operation; After the data is reported, the actual interface return results are written into the test case file.

5. An automated testing system based on an Internet of Things model, characterized in that: The system comprises: A parsing module is configured to export a specific format file of the product object model on the IoT platform, and extract the contents of each field under the attribute, event, and service function type by parsing the file; A saving module is configured to save the extracted field contents by event, attribute, and service respectively; The generation module is configured to automatically generate test input data for each test item in combination with the data type and parameter value range in the field content through the effective equivalence class and boundary value method, and save the above automatically generated test input data to the test input data item of the corresponding test case file to complete the automatic generation of test cases.

6. The system according to claim 5, characterized in that The storage module is specifically configured as follows: The extracted field contents are saved into different sheets of the Excel file according to events, attributes and services; The field content includes function name, function identifier, data type, parameter name, parameter identifier, and parameter value range.

7. The system according to claim 6, characterized in that The generation module is specifically configured as follows: For each test item, by combining the data type and its corresponding parameter value range, the valid equivalence class value and invalid equivalence class value of the test input data are automatically generated through the effective equivalence class partitioning method combined with the boundary value method to obtain the test input data.

8. The system according to claim 6 or 7, characterized in that: The system also includes a reporting module and a result writing module: The reporting module is configured to read the automatically generated test case file, and splice the function identifier, parameter identifier, and test input data in the test case file into a message body; and Call the relevant data reporting interface to send the spliced ​​message body to complete the data reporting operation; The result writing module is configured to write the actual interface return result into the test case file after the data is reported.

9. An electronic device, characterized in that: It includes a memory and a processor, wherein a computer program is stored in the memory, and when the processor runs the computer program stored in the memory, the processor executes the automated testing method based on the Internet of Things model as described in any one of claims 1 to 4.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the automated testing method based on the Internet of Things object model as described in any one of claims 1 to 4 is implemented.

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