Software directional testing method and system combined with Internet of Things

Abstract

The present invention relates to the field of software testing technology, and in particular to a software directional testing method and system combined with the Internet of Things, comprising a control layer, a processing layer and an evaluation layer; the real-time configuration network speed of the software application network is adjusted and controlled by the control layer, the software component subprograms are controlled to run based on the network speed control adjustment results in the control layer, and the subprogram running status under the configuration of each network speed control adjustment result is recorded, and the processing layer synchronously receives the software subprogram running status record data. The present invention can logically control the network speed of the software running application network, further drive the operation of each subprogram in the software based on the logical control of the network speed, collect the running results of the software subprogram under each network speed configuration state, and then use the collected data to perform distributed and continuous analysis and evaluation on the software subprogram and the software itself, so as to achieve the purpose of software testing, thereby reflecting the running robustness and feasibility of the software under the network configuration state.

Description

Technical Field

[0001] The present invention relates to the technical field of software testing, and in particular to a software directional testing method and system combined with the Internet of Things. Background Art

[0002] Software testing can be understood as a process used to promote the identification of the correctness, integrity, security and quality of software. In other words, software testing is a review or comparison process between actual output and expected output. The classic definition of software testing is: the process of operating a program under specified conditions to discover program errors, measure software quality, and evaluate whether it meets design requirements.

[0003] The invention patent with application number 202111035101.9 discloses an embedded software testing system based on the Internet of Things, which is characterized in that: the system includes a data acquisition module, a prediction module, a deviation adjustment module, a model building module, and an output module; the data acquisition module is used to collect real-time data generated in each step during the embedded software testing process of the communication equipment: the prediction module is used to predict the success rate of each step in the embedded software testing process of the communication equipment: the deviation adjustment module is used to perform deviation adjustment on the predicted data in the embedded software testing process of the communication equipment to improve the accuracy of the predicted data; the model building module is used to construct a deviation correction model for the predicted deviation value of the embedded software test, and adjust the predicted data: the output module is used to finally output a new predicted value: the output end of the data acquisition module is connected to the input end of the prediction module: the output end of the prediction module is connected to the input end of the deviation adjustment module: the output end of the deviation adjustment module is connected to the input end of the model building module: the output end of the model building module is connected to the input end of the output module.

[0004] The application aims to solve the problem that "it is extremely important to predict the success rate of each step in the embedded software testing of communication equipment, which can avoid the loss of test failure due to existing factors, but the existing technology lacks basic predictions, or the prediction level is difficult to achieve the desired effect."

[0005] However, current R&D software testing tends to focus on the integrity and rationality of the software's operating logic to see if there are any bugs, but tends to ignore the robustness and feasibility of the software's subroutines running under a network connection. This results in a situation where no matter how powerful or useful the software is, it cannot be used well by users. Summary of the invention

[0006] In view of the above-mentioned shortcomings of the prior art, the present invention provides a software directional testing method and system combined with the Internet of Things, which solves the technical problems raised in the above-mentioned background technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0008] First, a software-oriented test system combined with the Internet of Things includes the control layer, processing layer, and evaluation layer;

[0009] The real-time configuration network speed of the software application network is adjusted and controlled by the control layer. The software component subprograms are controlled and run in the control layer based on the network speed control adjustment results, and the subprogram running status under the configuration of each network speed control adjustment result is recorded. The processing layer synchronously receives the software subprogram running status record data, and analyzes the adaptability of each software subprogram based on the software subprogram running status record data. The evaluation layer evaluates the comprehensive performance of the software based on the adaptability of the software subprogram, and compares the subprograms with poor adaptability in the software for output;

[0010] The processing layer includes a receiving module and an analyzing module. The receiving module is used to receive the subprogram running status under each network speed control adjustment result recorded in the recording module. The analyzing module is used to traverse and read the subprogram running status data under each network speed control adjustment result received by the receiving module, and use the subprogram running status data under each network speed control adjustment result to analyze the adaptability of each software subprogram;

[0011] The software subroutine adaptability analysis logic is expressed as:

[0012]

[0013] Where: ψ is the performance value of the software subroutine adaptability; is the starting score of the software subroutine based on the first group of network speed adjustment results k; is the response time of the software subroutine based on the first group of network speed adjustment results k; M is the number of network speed adjustment gears; N is the total number of subroutines in the software;

[0014] Wherein, the (k1, k2, k3, ...) correspond to (kmin,

[0015]

[0016] The value of ε is 0 or 1. When the software subroutine fails to complete startup under the configured network speed adjustment result, ε=0, otherwise, ε=1.

[0017] Furthermore, the control layer includes a setting module, a matching module and a recording module, the setting module is used to set the number of network speed adjustment gears, the matching module is used to configure the corresponding network speeds under different network speed adjustment gears for the software subprogram, control the software subprogram to drive and run based on different configured network speeds, and the recording module is used to receive the driving and running results of the control software subprogram based on different configured network speeds in the matching module;

[0018] Among them, after the setting module sets the number of network speed adjustment gears, the network speed of the software application network is evenly divided based on the number of network speed adjustment gears, so that the speed difference between each adjacent group of network speed adjustment gears is equal. The matching logic between the software subroutine and the network speed corresponding to the network speed adjustment gear in the matching module is that each software subroutine matches the network speed corresponding to all network speed adjustment gears. The recording module distinguishes and records the results of driving the software subroutine at different network speeds based on the software subroutine, and the content of the software subroutine running results includes: whether the software subroutine has completed the startup and the software subroutine response time.

[0019] Furthermore, the number of network speed adjustment gears set by the setting module is obtained by the following formula:

[0020]

[0021] Where: M is the number of network speed adjustment gears; m1 is the base number of gears to be associated with the first group; k max is the peak network speed; k min is the valley value of network speed; ε is the total number of network threads; m2 is the base number of gears for the second group to obtain the association; ζ is the complexity of the software; S size The memory size occupied by the software when it is first installed;

[0022] Among them, the result of M is rounded up, and the m1 and m2 correspond to the network level association and software level association of the number of network speed adjustment gears when obtaining, respectively. The values ​​of m1 and m2 are initially set to m1=m2=1. The number of network speed adjustment gears set in the setting module is obtained by the above formula or manually set by the system end user. After the number of network speed adjustment gears M is obtained, the corresponding network speed of each network speed adjustment gear is further obtained. The result of obtaining the corresponding network speed of the network speed adjustment gear is expressed as

[0023] Furthermore, the software complexity ζ is obtained based on the number of subroutines in the software, and the value of the software complexity ζ is subject to the setting logic that the more subroutines there are in the software, the greater the value of the software complexity ζ, and the fewer subroutines there are in the software, the smaller the value of the software complexity ζ, ζ≥0;

[0024] Among them, the logic of obtaining the software complexity ζ is expressed as:

[0025]

[0026] Where: η is the number of subroutines in the software; λ in The page jump logic contained in the software; all is the page jump logic contained in the software; f is the average usage frequency of the software relative to the software user group; g is the number of downloads counted by the software management backend.

[0027] Furthermore, after obtaining the software subroutine adaptability performance value ψ, further refinement processing is performed, and the refinement result based on the software subroutine adaptability performance value ψ is sent to the evaluation layer for use by the evaluation layer to evaluate the comprehensive performance of the software. The refinement logic of the software subroutine adaptability performance value ψ is expressed as:

[0028]

[0029] Where: ref is the result of refining the performance value ψ of the software subroutine's adaptability; l is the number of operable modes of the software subroutine; δ is the weight of the software subroutine for the overall software operation; υ is the running memory required by the software subroutine when running;

[0030] Among them, the operable modes l of the software subroutine include: voice control, gesture control, and touch control. The value of the weight δ follows the setting logic that the larger the value of υ, the larger the value of δ; the smaller the value of υ, the smaller the value of δ.

[0031] Furthermore, in the operation phase of the receiving module, when receiving the software subprogram operation status in the recording module, the software subprogram operation status data is sequentially received based on the source software subprogram of the software subprogram operation status, so that the software subprogram operation status data received by the receiving module each time is derived from the same software subprogram;

[0032] The software subroutine running status data refers to the software subroutine running result.

[0033] Furthermore, the evaluation layer includes a comprehensive evaluation module and a capture module. The comprehensive evaluation module is used to receive the refinement results of the adaptability of each software subroutine analyzed by the analysis module in the processing layer, and comprehensively evaluate the adaptability of the software based on the refinement results of the adaptability of each software subroutine. The capture module is used to set a judgment threshold to be optimized, compare the judgment threshold to be optimized with the refinement results of the adaptability of each software subroutine, obtain the software subroutine at the judgment threshold to be optimized, and use the obtained software subroutine as an output target to feed back to the system end user;

[0034] Among them, the software subroutine at the determination threshold to be optimized is a software subroutine with poor adaptability.

[0035] Furthermore, the software adaptability performance evaluation logic is expressed as:

[0036]

[0037] Where: CP is the software adaptability performance value; ψ ref1 is the refined result of the performance value ψ of the adaptability of the first group of software subroutines; δ1 is the weight of the first group of software subroutines for the overall software operation; q0 is the total amount of the refined results of the performance value ψ of the adaptability of the software subroutines;

[0038] Among them, the system-side user evaluates the comprehensive adaptability of the software based on the software adaptability performance value CP.

[0039] Furthermore, the setting module is electrically connected to the matching module and the recording module through a medium, the recording module is electrically connected to the receiving module through a medium, the receiving module is electrically connected to the analysis module through a medium, the analysis module is electrically connected to the comprehensive evaluation module through a medium, and the comprehensive evaluation module is electrically connected to the capture module through a medium;

[0040] Among them, the setting module is wirelessly connected to the software application network to set and control the network speed, and the matching module is connected to the computer device equipped with the software through the software application network to control each subroutine in the software.

[0041] The second aspect is to combine the software directional testing method of the Internet of Things, including the following steps:

[0042] Step 1: Set the real-time network speed of the software application network, and drive each subprogram of the software to run based on the set real-time network speed;

[0043] Step 11: Design phase of network real-time network setting logic;

[0044] Step 2: Record the running results of each subprogram of the software under the set real-time network speed;

[0045] Step 3: Obtain the running results of each software subprogram based on the real-time network speed of each network, and analyze the adaptability of each software subprogram based on the running result data;

[0046] Step 31: Software subroutine adaptability analysis logic configuration phase;

[0047] Step 32: software subroutine adaptability analysis result refinement stage;

[0048] Step 4: Receive the software subroutine adaptability analysis results, and evaluate the software comprehensive performance based on the analysis results;

[0049] Step 41: Setting phase of software comprehensive performance evaluation logic;

[0050] Step 5: Set a determination threshold, compare the determination threshold with the software subroutine adaptability analysis result, obtain the software subroutine within the determination threshold, and use the obtained software subroutine as the output target to output to the user end.

[0051] Compared with the known public technology, the technical solution provided by the present invention has the following advantages:

[0052] Beneficial effects:

[0053] 1. The present invention provides a software directional testing system combined with the Internet of Things. During operation, the system can perform logical control on the network speed of the software running application network, further drive the operation of each subroutine in the software based on the logical control of the network speed, collect the operation results of the software subroutine under each network speed configuration state, and then use the collected data to perform distributed and continuous analysis and evaluation on the software subroutine and the software itself, so as to achieve the purpose of software testing, thereby reflecting the running robustness and feasibility of the software based on the network configuration state, and ensuring that the software tested in this way has a better software usage experience when used by users.

[0054] 2. During the operation of the system of the present invention, the network speed gear is set and matched with all the subroutines of the software to control the operation and obtain data, so that the acquired data is more comprehensive, thereby making the output result of the method more accurate. In the output stage of the operation results of this system, the subroutines in the software that have optimization needs are output synchronously, so as to facilitate the software development users to further "upgrade" the software.

[0055] 3. The present invention provides a software-oriented testing method combined with the Internet of Things. By executing the steps in the method, the stability of the system operation can be further maintained, and a more comprehensive operating logic is provided for the system operation during the execution of the steps of the method, ensuring that the technical solution composed of the system and the method brings a more stable testing effect to the software testing during the specific implementation stage. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0057] Figure 1 It is a structural diagram of a software directional testing system combined with the Internet of Things;

[0058] Figure 2 A flowchart of a software directional testing method combined with the Internet of Things;

[0059] Figure 3 It is a schematic diagram of the matching results of the network speed corresponding to each network speed adjustment gear and the software subroutine in the present invention. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0061] The present invention will be further described below in conjunction with the embodiments.

[0062] Example 1

[0063] The software directional testing system combined with the Internet of Things in this embodiment is as follows: Figure 1 As shown, it includes control layer, processing layer and evaluation layer;

[0064] The real-time configuration network speed of the software application network is adjusted and controlled by the control layer. The software component subprograms are controlled and run in the control layer based on the network speed control adjustment results, and the subprogram running status under the configuration of each network speed control adjustment result is recorded. The processing layer synchronously receives the software subprogram running status record data, and analyzes the adaptability of each software subprogram based on the software subprogram running status record data. The evaluation layer evaluates the comprehensive performance of the software based on the adaptability of the software subprogram, and compares the subprograms with poor adaptability in the software for output;

[0065] The processing layer includes a receiving module and an analyzing module. The receiving module is used to receive the subprogram running status under each network speed control adjustment result recorded in the recording module. The analyzing module is used to traverse and read the subprogram running status data under each network speed control adjustment result received by the receiving module, and use the subprogram running status data under each network speed control adjustment result to analyze the adaptability of each software subprogram;

[0066] The logic of software subroutine adaptability analysis is expressed as:

[0067]

[0068] Where: ψ is the performance value of the software subroutine adaptability; is the starting score of the software subroutine based on the first group of network speed adjustment results k; is the response time of the software subroutine based on the first group of network speed adjustment results k; M is the number of network speed adjustment gears; N is the total number of subroutines in the software;

[0069] Wherein, the (k1, k2, k3, ...) correspond to (kmin, ...), the value of ε is 0 or 1. When the software subroutine fails to start under the configured network speed adjustment result, ε=0, otherwise, ε=1;

[0070] The control layer includes a setting module, a matching module and a recording module. The setting module is used to set the number of network speed adjustment gears. The matching module is used to configure the corresponding network speeds under different network speed adjustment gears for the software subprogram, and the software subprogram is controlled to drive and run based on different configured network speeds. The recording module is used to receive the driving and running results of the software subprogram in the matching module based on different configured network speeds;

[0071] Among them, after the setting module sets the number of network speed adjustment gears, the network speed of the software application network is evenly divided based on the number of network speed adjustment gears, so that the speed difference between each adjacent group of network speed adjustment gears is equal, and the matching logic between the software subroutine and the network speed corresponding to the network speed adjustment gear in the matching module is that each software subroutine matches the network speed corresponding to all network speed adjustment gears, and the recording module distinguishes and records the software subroutine driving operation results under different network speeds based on the software subroutine, and the software subroutine operation results include: whether the software subroutine has completed the startup and the software subroutine response time;

[0072] The evaluation layer includes a comprehensive evaluation module and a capture module. The comprehensive evaluation module is used to receive the refinement results of the adaptability of each software subroutine analyzed by the analysis module in the processing layer, and comprehensively evaluate the adaptability of the software based on the refinement results of the adaptability of each software subroutine. The capture module is used to set a judgment threshold to be optimized, compare the judgment threshold to be optimized with the refinement results of the adaptability of each software subroutine, obtain the software subroutine at the judgment threshold to be optimized, and use the obtained software subroutine as an output target to feedback to the system end user;

[0073] Among them, the software subprograms at the threshold of determination to be optimized are software subprograms with poor adaptability;

[0074] The software adaptability performance evaluation logic is expressed as:

[0075]

[0076] Where: CP is the software adaptability performance value; ψ ref1is the refined result of the performance value ψ of the adaptability of the first group of software subroutines; δ1 is the weight of the first group of software subroutines for the overall software operation; q0 is the total amount of the refined results of the performance value ψ of the adaptability of the software subroutines;

[0077] Among them, the system end user evaluates the comprehensive adaptability of the software based on the software adaptability performance value CP;

[0078] The setting module is electrically connected to the matching module and the recording module through a medium, the recording module is electrically connected to the receiving module through a medium, the receiving module is electrically connected to the analysis module through a medium, the analysis module is electrically connected to the comprehensive evaluation module through a medium, and the comprehensive evaluation module is electrically connected to the capture module through a medium;

[0079] Among them, the setting module is wirelessly connected to the software application network to set and control the network speed, and the matching module is connected to the computer device equipped with the software through the software application network to control each subroutine in the software.

[0080] In this embodiment, the setting module runs to set the number of network speed adjustment gears, the matching module runs synchronously to configure the corresponding network speeds under different network speed adjustment gears for the software subroutine, the control software subroutine is driven to run based on different configured network speeds, the recording module is post-operated to receive the driving operation results of the control software subroutine in the matching module based on different configured network speeds, the receiving module further receives the subroutine running status under each network speed control adjustment result recorded in the recording module, and then the analysis module traverses and reads the subroutine running status data under each network speed control adjustment result received by the receiving module, and the subroutine running status data under each network speed control adjustment result is used to analyze the adaptability of each software subroutine, and finally the comprehensive evaluation module receives the refinement results of the adaptability of each software subroutine analyzed by the analysis module in the processing layer, and the adaptability of the software is comprehensively evaluated based on the refinement results of the adaptability of each software subroutine, and the capture module runs to set the judgment threshold to be optimized, and the judgment threshold to be optimized is compared with the refinement results of the adaptability of each software subroutine, and the software subroutine at the judgment threshold to be optimized is obtained, and the obtained software subroutine is used as an output target to feedback to the system end user.

[0081] By limiting the software subroutine adaptability analysis logic and the software adaptability performance evaluation logic through the above formula, necessary data support is further provided for the operation of subsequent modules of the system. In addition, such digital output method is used to provide system-side user data reference, so that system-side users can obtain clearer data and further optimize and improve the software.

[0082] See also Figure 3 As shown, through Figure 3 The connection between the network speed and the software subroutine is configured, which further demonstrates the test logic for the software subroutine in the system.

[0083] Example 2

[0084] In terms of specific implementation, based on Example 1, this example refers to Figure 1 The software directional testing system combined with the Internet of Things in Example 1 is further described in detail:

[0085] The number of network speed adjustment gears set by the setting module is obtained by the following formula:

[0086]

[0087] Where: M is the number of network speed adjustment gears; m1 is the base number of gears to be associated with the first group; k max is the peak network speed; k min is the network speed valley value; ε is the total number of network threads; m2 is the base number of the gears associated with the second group; ζ is the software complexity; S size The memory size occupied by the software when it is first installed;

[0088] Among them, the result of M is rounded up, and the m1 and m2 correspond to the network level association and software level association of the number of network speed adjustment gears when obtaining, respectively. The values ​​of m1 and m2 are initially set to m1=m2=1. The number of network speed adjustment gears set in the setting module is obtained by the above formula or manually set by the system end user. After the number of network speed adjustment gears M is obtained, the corresponding network speed of each network speed adjustment gear is further obtained. The result of obtaining the corresponding network speed of the network speed adjustment gear is expressed as

[0089] The software complexity ζ is calculated based on the number of subroutines in the software. The value of the software complexity ζ follows the setting logic that the more subroutines there are in the software, the greater the value of the software complexity ζ is, and the fewer subroutines there are in the software, the smaller the value of the software complexity ζ is, ζ≥0;

[0090] Among them, the logic of obtaining the software complexity ζ is expressed as:

[0091]

[0092] Where: η is the number of subroutines in the software; λ in The page jump logic contained in the software; all is the page jump logic contained in the software; f is the average usage frequency of the software relative to the software user group; g is the number of downloads counted by the software management backend.

[0093] By calculating through the above formula, the number of network speed adjustment gears configured for the software subroutine in the setting module is logically limited to ensure that the software subroutine obtains stable test conditions, and thereby, the system operation can control the test accuracy of the software for the convenience of system end users.

[0094] Example 3

[0095] In terms of specific implementation, based on Example 1, this example refers to Figure 1 The software directional testing system combined with the Internet of Things in Example 1 is further described in detail:

[0096] After the software subroutine adaptability performance value ψ is obtained, it is further refined, and the refinement result based on the software subroutine adaptability performance value ψ is sent to the evaluation layer for use by the evaluation layer to evaluate the comprehensive performance of the software. The refinement logic of the software subroutine adaptability performance value ψ is expressed as:

[0097]

[0098] Where: ref is the result of refining the performance value ψ of the software subroutine's adaptability; l is the number of operable modes of the software subroutine; δ is the weight of the software subroutine for the overall software operation; υ is the running memory required by the software subroutine when running;

[0099] Among them, the operable modes l of the software subroutine include: voice control, gesture control, and touch control. The value of the weight δ follows the setting logic that the larger the value of υ, the larger the value of δ; the smaller the value of υ, the smaller the value of δ.

[0100] By calculating through the above formula, the software subroutine adaptability performance value ψ is further refined, thereby further improving the system operation accuracy.

[0101] like Figure 1 As shown, in the operation phase of the receiving module, when receiving the software subprogram operation status in the recording module, the software subprogram operation status data is sequentially received based on the source software subprogram of the software subprogram operation status, so that the software subprogram operation status data received by the receiving module each time is derived from the same software subprogram;

[0102] The software subroutine running status data refers to the software subroutine running result.

[0103] Through the above settings, the operation logic limitation of the receiving module in the processing layer is further provided to make the system operation more logical, thereby achieving the purpose of improving the stability of system operation.

[0104] Example 4

[0105] In terms of specific implementation, based on Example 1, this example refers to Figure 2 The software directional testing system combined with the Internet of Things in Example 1 is further described in detail:

[0106] The software directional testing method combined with the Internet of Things includes the following steps:

[0107] Step 1: Set the real-time network speed of the software application network, and drive each subprogram of the software to run based on the set real-time network speed;

[0108] Step 11: Design phase of network real-time network setting logic;

[0109] Step 2: Record the running results of each subprogram of the software under the set real-time network speed;

[0110] Step 3: Obtain the running results of each software subprogram based on the real-time network speed of each network, and analyze the adaptability of each software subprogram based on the running result data;

[0111] Step 31: Software subroutine adaptability analysis logic configuration phase;

[0112] Step 32: software subroutine adaptability analysis result refinement stage;

[0113] Step 4: Receive the software subroutine adaptability analysis results, and evaluate the software comprehensive performance based on the analysis results;

[0114] Step 41: Setting phase of software comprehensive performance evaluation logic;

[0115] Step 5: Set a determination threshold, compare the determination threshold with the software subroutine adaptability analysis result, obtain the software subroutine within the determination threshold, and use the obtained software subroutine as the output target to output to the user end.

[0116] In summary, in the above embodiments, the system can perform logical control on the network speed of the software application network during operation, further drive the operation of each subroutine in the software based on the logical control of the network speed, collect the operation results of the software subroutine under each network speed configuration state, and then use the collected data to perform distributed and continuous analysis and evaluation on the software subroutine and the software itself, so as to achieve the purpose of software testing, thereby reflecting the running robustness and feasibility of the software under the network configuration state, and ensuring that the software tested in this way has a better software experience when used by users; and during the operation of the system, the network speed is used to control the running results of the software subroutine under each network speed configuration state, and the software subroutine and the software itself are analyzed and evaluated in a distributed manner. The gear setting is matched with all the subroutines of the software to control the operation and obtain data, so that the obtained data is more comprehensive, thereby making the output result of the method more accurate. In the operation result output stage of this system, the subroutines with optimization needs in the software are output synchronously, so as to facilitate the software development users to further "upgrade" the software. At the same time, the method provided in the embodiment can further maintain the stability of the system operation, and provide a more comprehensive operation logic for the system operation during the execution of the steps of the method, ensuring that the technical solution composed of the system and method brings more stable test results to software testing in the specific implementation stage.

[0117] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A software-oriented testing system combined with the Internet of Things, characterized in that: It includes control layer, processing layer and evaluation layer; The real-time configuration network speed of the software application network is adjusted and controlled by the control layer. The software component subprograms are controlled and run in the control layer based on the network speed control adjustment results, and the subprogram running status under the configuration of each network speed control adjustment result is recorded. The processing layer synchronously receives the software subprogram running status record data, and analyzes the adaptability of each software subprogram based on the software subprogram running status record data. The evaluation layer evaluates the comprehensive performance of the software based on the adaptability of the software subprogram, and compares the subprograms with poor adaptability in the software for output; The control layer includes a setting module, a matching module and a recording module. The setting module is used to set the number of network speed adjustment gears. The matching module is used to configure the corresponding network speeds under different network speed adjustment gears for the software subprogram, and control the software subprogram to drive and run based on different configured network speeds. The recording module is used to receive the driving and running results of the control software subprogram based on different configured network speeds in the matching module; Among them, after the setting module sets the number of network speed adjustment gears, the network speed of the software application network is evenly divided based on the number of network speed adjustment gears, so that the network speed difference between each adjacent group of network speed adjustment gears is equal, and the matching logic between the software subprogram and the network speed corresponding to the network speed adjustment gear in the matching module is that each software subprogram matches the network speed corresponding to all network speed adjustment gears, and the recording module distinguishes and records the software subprogram driving operation results under different network speeds based on the software subprogram, and the software subprogram operation results include: whether the software subprogram has completed the startup and the software subprogram response time; The number of network speed adjustment gears set by the setting module is obtained by the following formula: Where: M is the number of network speed adjustment gears; m1 is the base number of gears to be associated with the first group; k max is the peak network speed; k min is the valley value of network speed; ε is the total number of network threads; m2 is the base number of gears for the second group to obtain the association; ζ is the complexity of the software; S size The memory size occupied by the software when it is first installed; Among them, the result of M is rounded up, and the m1 and m2 correspond to the network level association and software level association of the number of network speed adjustment gears when obtaining, respectively. The values ​​of m1 and m2 are initially set to m1=m2=1. The number of network speed adjustment gears set in the setting module is obtained by the above formula or manually set by the system end user. After the number of network speed adjustment gears M is obtained, the corresponding network speed of each network speed adjustment gear is further obtained. The result of obtaining the corresponding network speed of the network speed adjustment gear is expressed as The processing layer includes a receiving module and an analyzing module. The receiving module is used to receive the subprogram running status under each network speed control adjustment result recorded in the recording module. The analyzing module is used to traverse and read the subprogram running status data under each network speed control adjustment result received by the receiving module, and use the subprogram running status data under each network speed control adjustment result to analyze the adaptability of each software subprogram; The software subroutine adaptability analysis logic is expressed as: Where: ψ is the performance value of the software subroutine adaptability; is the starting score of the software subroutine based on the first group of network speed adjustment results k; is the response time of the software subroutine based on the first group of network speed adjustment results k; M is the number of network speed adjustment gears; N is the total number of subroutines in the software; Among them, the (k1, k2, k3, ...) correspond to The value of ε is 0 or 1. When the software subroutine fails to complete startup under the configured network speed adjustment result, ε=0, otherwise, ε=1.

2. The software directional testing system combined with the Internet of Things according to claim 1 is characterized in that: The software complexity ζ is obtained based on the number of subroutines in the software, and the value of the software complexity ζ is subject to the setting logic that the more subroutines there are in the software, the greater the value of the software complexity ζ is, and the fewer subroutines there are in the software, the smaller the value of the software complexity ζ is, ζ≥0; Among them, the logic of obtaining the software complexity ζ is expressed as: Where: η is the number of subroutines in the software; λ in The page jump logic contained in the software; all is the page jump logic contained in the software; f is the average usage frequency of the software relative to the software user group; g is the number of downloads counted by the software management backend.

3. The software directional testing system combined with the Internet of Things according to claim 1 is characterized in that: After the software subroutine adaptability performance value ψ is obtained, it is further refined, and the refinement result based on the software subroutine adaptability performance value ψ is sent to the evaluation layer for use by the evaluation layer in evaluating the comprehensive performance of the software. The refinement logic of the software subroutine adaptability performance value ψ is expressed as: Where: ref is the refinement result of the performance value ψ of the software subroutine's adaptability; l is the number of operable modes of the software subroutine; δ is the weight of the software subroutine for the global software operation; υThe memory required by the software subroutine when running; Among them, the operable modes l of the software subroutine include: voice control, gesture control, and touch control. The value of the weight δ follows the setting logic that the larger the value of υ, the larger the value of δ; the smaller the value of υ, the smaller the value of δ.

4. The software directional testing system combined with the Internet of Things according to claim 1 is characterized in that: The receiving module operation phase, when receiving the software subprogram operation status in the recording module, sequentially receives the software subprogram operation status data based on the source software subprogram of the software subprogram operation status, so that the software subprogram operation status data received by the receiving module each time is from the same software subprogram; The software subroutine running status data refers to the software subroutine running result.

5. The software directional testing system combined with the Internet of Things according to claim 1 is characterized in that: The evaluation layer includes a comprehensive evaluation module and a capture module. The comprehensive evaluation module is used to receive the refinement results of the adaptability of each software subroutine analyzed by the analysis module in the processing layer, and comprehensively evaluate the adaptability of the software based on the refinement results of the adaptability of each software subroutine. The capture module is used to set a judgment threshold to be optimized, compare the judgment threshold to be optimized with the refinement results of the adaptability of each software subroutine, obtain the software subroutine at the judgment threshold to be optimized, and use the obtained software subroutine as an output target to feed back to the system end user; Among them, the software subroutine at the determination threshold to be optimized is a software subroutine with poor adaptability.

6. The software directional testing system combined with the Internet of Things according to claim 5 is characterized in that: The software adaptability performance evaluation logic is expressed as: Where: CP is the software adaptability performance value; ψ ref1 is the refined result of the performance value ψ of the adaptability of the first group of software subroutines; δ1 is the weight of the first group of software subroutines for the overall software operation; q0 is the total amount of the refined results of the performance value ψ of the adaptability of the software subroutines; Among them, the system-side user evaluates the comprehensive adaptability of the software based on the software adaptability performance value CP.

7. The software directional testing system combined with the Internet of Things according to claim 1 is characterized in that: The setting module is electrically connected to the matching module and the recording module through a medium, the recording module is electrically connected to the receiving module through a medium, the receiving module is electrically connected to the analysis module through a medium, the analysis module is electrically connected to the comprehensive evaluation module through a medium, and the comprehensive evaluation module is electrically connected to the capture module through a medium; Among them, the setting module is wirelessly connected to the software application network to set and control the network speed, and the matching module is connected to the computer device equipped with the software through the software application network to control each subroutine in the software.

8. A software directional testing method in combination with the Internet of Things, the method being an implementation method of a software directional testing system in combination with the Internet of Things as claimed in any one of claims 1 to 7, characterized in that: The following steps are involved: Step 1: Set the real-time network speed of the software application network, and drive each subprogram of the software to run based on the set real-time network speed; Step 11: Design phase of network real-time network setting logic; Step 2: Record the running results of each subprogram of the software under the set real-time network speed; Step 3: Obtain the running results of each software subprogram based on the real-time network speed of each network, and analyze the adaptability of each software subprogram based on the running result data; Step 31: Software subroutine adaptability analysis logic configuration phase; Step 32: software subroutine adaptability analysis result refinement stage; Step 4: Receive the software subroutine adaptability analysis results, and evaluate the software comprehensive performance based on the analysis results; Step 41: Setting phase of software comprehensive performance evaluation logic; Step 5: Set a determination threshold, compare the determination threshold with the software subroutine adaptability analysis result, obtain the software subroutine within the determination threshold, and use the obtained software subroutine as the output target to output to the user end.

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