CN114664074B - Test method and system of Bluetooth remote controller - Google Patents
Test method and system of Bluetooth remote controller Download PDFInfo
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- CN114664074B CN114664074B CN202210229856.0A CN202210229856A CN114664074B CN 114664074 B CN114664074 B CN 114664074B CN 202210229856 A CN202210229856 A CN 202210229856A CN 114664074 B CN114664074 B CN 114664074B
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- G—PHYSICS
- G08—SIGNALLING
- G08C—TRANSMISSION SYSTEMS FOR MEASURED VALUES, CONTROL OR SIMILAR SIGNALS
- G08C25/00—Arrangements for preventing or correcting errors; Monitoring arrangements
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- G—PHYSICS
- G08—SIGNALLING
- G08C—TRANSMISSION SYSTEMS FOR MEASURED VALUES, CONTROL OR SIMILAR SIGNALS
- G08C17/00—Arrangements for transmitting signals characterised by the use of a wireless electrical link
- G08C17/02—Arrangements for transmitting signals characterised by the use of a wireless electrical link using a radio link
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Abstract
The invention provides a test method and a test system for a Bluetooth remote controller, wherein the method comprises the following steps: step 1: based on a preset general test strategy, correspondingly testing the Bluetooth remote controller to be tested; step 2: acquiring reference information corresponding to the Bluetooth remote controller; and 3, step 3: determining whether to enter a specific test opportunity or not based on the reference information, and if so, making a proper specific test strategy; and 4, step 4: and performing corresponding relay aiming test on the Bluetooth remote controller based on the aiming test strategy. The testing method and the system of the Bluetooth remote controller automatically test the Bluetooth remote controller to be tested without manual completion, reduce the labor cost, and in addition, determine whether to enter the specific testing opportunity or not based on the reference information during the testing, if so, perform the corresponding specific testing, thereby avoiding the problems of lower pertinence and poorer testing efficiency caused by the testing strategy that the sampling is fixed and unchanged during the manual testing.
Description
Technical Field
The invention relates to the technical field of Bluetooth equipment testing, in particular to a testing method and a testing system of a Bluetooth remote controller.
Background
At present, a bluetooth remote controller is favored by users by virtue of the advantages of long control distance, no need of aligning to a remote controlled device and the like, but the bluetooth remote controller also has the problems of unstable connection with the remote controlled device, frequent disconnection, abnormal high power consumption speed and the like, so that the bluetooth remote controller needs to be strictly tested before leaving a factory, the traditional test is completed manually, the labor cost is high, in addition, a fixed and unchangeable test strategy is generally sampled during manual test, the pertinence is low, and the test efficiency is poor;
therefore, a solution is needed.
Disclosure of Invention
The invention provides a test method and a test system of a Bluetooth remote controller, which can automatically test the Bluetooth remote controller to be tested without manual completion, thereby reducing the labor cost.
The invention provides a test method of a Bluetooth remote controller, which comprises the following steps:
step 1: based on a preset general test strategy, correspondingly testing the Bluetooth remote controller to be tested;
and 2, step: acquiring reference information corresponding to the Bluetooth remote controller;
and 3, step 3: determining whether to enter a specific test opportunity or not based on the reference information, and if so, making a proper specific test strategy;
and 4, step 4: and performing corresponding relay aiming test on the Bluetooth remote controller based on the aiming test strategy.
Preferably, the step 2: acquiring reference information corresponding to the Bluetooth remote controller, including:
acquiring an acquisition node set corresponding to the Bluetooth remote controller, wherein the acquisition node set comprises: a plurality of first acquisition nodes;
acquiring an acquisition target type corresponding to the first acquisition node;
acquiring a preset verification type demand library, matching the acquired target type with the verification types in the verification type demand library, if the matching is in accordance with the verification type demand library, acquiring at least one first verification strategy corresponding to the verification type in accordance with the matching, and meanwhile, taking the corresponding first acquisition node as a second acquisition node and taking the rest first acquisition nodes as third acquisition nodes;
sequentially traversing the first verification strategy, and acquiring a verification flow corresponding to the traversed first verification strategy during each traversal;
splitting the verification flow into a plurality of first flows;
acquiring a process attribute corresponding to the first process, and acquiring a risk prediction value corresponding to the process attribute;
performing feature extraction on the first process to obtain a plurality of process features;
acquiring a preset verification feature demand library, and matching the process features with verification features in the verification feature demand library;
if the risk prediction value is greater than or equal to a preset risk prediction threshold value and/or the flow characteristic is matched and matched with any verification characteristic, taking the corresponding first flow as a second flow;
acquiring a flow execution sequence corresponding to the verification flow, determining a preset number of first flows executed before the second flow based on the flow execution sequence, and taking the first flows as third flows;
performing association analysis on the second flow and the third flow to obtain an association value;
if the correlation value is greater than or equal to a preset correlation threshold value, taking the corresponding third flow as a fourth flow;
integrating the second process and the fourth process to obtain a process to be simulated;
based on the flow to be simulated, carrying out flow simulation to obtain a flow simulation result, and simultaneously recording the simulation process of the flow simulation;
acquiring an actual execution result corresponding to the second process, and simultaneously performing difference analysis on the process simulation result and the actual execution result to acquire a difference value;
inputting the simulation process into a preset risk verification model to obtain a verification value;
if the difference value is greater than or equal to a preset difference threshold value and/or the check value is greater than or equal to a preset check threshold value, rejecting the first verification strategy which is traversed correspondingly;
when first verification strategies needing to be removed are all removed, the remaining first verification strategies are removed to serve as second verification strategies;
verifying the second acquisition node based on the second verification strategy, and if the second acquisition node passes the verification, taking the corresponding second acquisition node as a fourth acquisition node;
acquiring first target data through the third acquisition node and the fourth acquisition node;
and integrating the acquired first target data to acquire reference information corresponding to the Bluetooth remote controller, and finishing acquisition.
Preferably, in the step 3, determining whether to enter a test opportunity based on the reference information includes:
training a specific test opportunity determination model, inputting the reference information to the specific test opportunity determination model, obtaining a determination result, and determining whether to enter specific test opportunity or not based on the determination result.
Preferably, training the determination model for the test occasion comprises:
acquiring a preset event node set, wherein the event node set comprises: a plurality of first event nodes;
determining a plurality of event generation records corresponding to the first event node based on a preset event generation record library, wherein the event generation records comprise: a first person determines an event, a generation time point and a generation party for the test opportunity;
acquiring a first contribution degree corresponding to the first manual work determination event aiming at the test opportunity;
acquiring a credit value and a second contribution degree corresponding to the generator;
accumulating and calculating the first contribution degree, the credit value and the second contribution degree to obtain a first rejection value;
generating a time weight based on the generation time point according to a preset weight setting rule;
giving the time weight to the first elimination value to obtain a second elimination value, and associating the second elimination value with the node corresponding to the first event;
accumulating and calculating the second elimination value associated with the first event node to obtain an elimination value sum;
if the rejection value sum is less than or equal to a preset rejection value and a preset threshold value, rejecting the corresponding first event node;
when the first event nodes needing to be removed are all removed, the remaining first event nodes are taken as second event nodes;
obtaining a plurality of second artificial determination events for the test occasions through the second event node;
and inputting each obtained artificial target test opportunity determination event into a preset neural network training model, performing model training, obtaining a target test opportunity determination model, and finishing training.
Preferably, the obtaining a first contribution degree corresponding to the first manual determination event for the test opportunity includes:
determining a plurality of first contribution events corresponding to the first manual determination event aiming at the test opportunity based on a preset first contribution event library;
performing event analysis on the first contribution event, and determining a first contribution proportion of the first manual work determination event corresponding to the first contribution event for a test opportunity;
acquiring a first contribution total value corresponding to the first contribution event;
determining a first contribution value for the first human-to-test-opportunity-determination event corresponding to the first contribution event based on the first contribution proportion and a first total contribution value, and associating with the first human-to-test-opportunity-determination event;
and accumulating the first contribution value associated with the event determined by the first person aiming at the test opportunity to obtain a first contribution degree, and finishing the acquisition.
Preferably, the obtaining the credit value and the second contribution degree corresponding to the producer comprises:
determining a plurality of association generators corresponding to the generators on the basis of a preset association generator library, and simultaneously determining an association relation between the generators and the association generators;
acquiring a first credit condition corresponding to the association generator;
analyzing the first credit condition to obtain a first analysis value;
obtaining a relation weight corresponding to the incidence relation, giving the relation weight corresponding to the first analysis value, and obtaining a second analysis value;
acquiring a second credit condition corresponding to the producer;
performing condition analysis on the second credit condition to obtain a third analysis value;
if the second analysis value is smaller than or equal to a preset first analysis threshold and/or the third analysis value is smaller than or equal to a preset second analysis threshold, acquiring a preset credit value threshold, and taking the credit value threshold as a credit value corresponding to the generator to finish acquisition;
otherwise, acquiring a preset credit value calculation model, inputting each second analytic value and each third analytic value into the credit value calculation model, acquiring a credit value corresponding to the generator, and finishing acquisition;
determining a plurality of second contribution events corresponding to the producer based on a preset second contribution event library;
performing event analysis on the second contribution event, and determining a second contribution proportion of the producer corresponding to the second contribution event;
acquiring a second contribution total value corresponding to the second contribution event;
determining a second contribution value of the producer corresponding to the second contribution event based on the second contribution proportion and a second total contribution value, and associating with the corresponding producer;
and accumulating and calculating the second contribution value associated with the generator to obtain a second contribution degree, and finishing the acquisition.
Preferably, in step 3, the making of an appropriate test-aiming strategy includes:
acquiring a preset test strategy set, wherein the test strategy set comprises: a plurality of first test strategies;
sequentially traversing the first test strategy, and acquiring at least one rule matching group corresponding to the traversed first test strategy during each traversal, wherein the rule matching group comprises: screening rules and corresponding first evaluation rules;
screening second target data from the reference information based on the screening rule;
evaluating the second target data based on the first evaluation rule to obtain an evaluation value;
acquiring an evaluation threshold corresponding to the first evaluation rule;
if the evaluation value is larger than or equal to the evaluation threshold value, correlating the evaluation value with the first test strategy, and meanwhile, taking the corresponding first evaluation rule as a second evaluation rule;
accumulating and calculating the evaluation values associated with the first test strategy to obtain a first evaluation value sum;
determining an evaluation value and a threshold value which are commonly corresponding to all the second evaluation rules based on a preset evaluation value and threshold value library;
if the first evaluation value sum is larger than or equal to the evaluation value sum threshold, taking the corresponding first test strategy as a second test strategy;
determining the first evaluation value sum corresponding to the second test strategy as a second evaluation value sum;
and sequencing and integrating the second test strategy according to the second evaluation value sum from large to small to obtain a proper specific test strategy, and finishing the formulation.
The invention provides a test system of a Bluetooth remote controller, which comprises:
the first testing module is used for correspondingly testing the Bluetooth remote controller to be tested based on a preset general testing strategy;
the acquisition module is used for acquiring reference information corresponding to the Bluetooth remote controller;
the determining module is used for determining whether the aiming test opportunity is entered or not based on the reference information, and if so, making a proper aiming test strategy;
and the second testing module is used for carrying out corresponding relay aiming test on the Bluetooth remote controller based on the aiming test strategy.
Preferably, the obtaining module performs the following operations:
acquiring an acquisition node set corresponding to the Bluetooth remote controller, wherein the acquisition node set comprises: a plurality of first acquisition nodes;
acquiring an acquisition target type corresponding to the first acquisition node;
acquiring a preset verification type demand library, matching the acquired target type with the verification type in the verification type demand library, if the matching is in accordance, acquiring at least one first verification strategy corresponding to the verification type in accordance with the matching, meanwhile, taking the corresponding first acquisition node as a second acquisition node, and taking the rest first acquisition nodes as third acquisition nodes;
sequentially traversing the first verification strategy, and acquiring a verification flow corresponding to the traversed first verification strategy during each traversal;
splitting the verification flow into a plurality of first flows;
acquiring a process attribute corresponding to the first process, and acquiring a risk predicted value corresponding to the process attribute;
performing feature extraction on the first process to obtain a plurality of process features;
acquiring a preset verification feature requirement library, and matching the process features with verification features in the verification feature requirement library;
if the risk prediction value is greater than or equal to a preset risk prediction threshold value and/or the flow characteristic is matched and matched with any verification characteristic, taking the corresponding first flow as a second flow;
acquiring a flow execution sequence corresponding to the verification flow, determining a preset number of first flows executed before the second flow based on the flow execution sequence, and taking the first flows as third flows;
performing association analysis on the second flow and the third flow to obtain an association value;
if the correlation value is greater than or equal to a preset correlation threshold value, taking the corresponding third flow as a fourth flow;
integrating the second process and the fourth process to obtain a process to be simulated;
based on the flow to be simulated, carrying out flow simulation to obtain a flow simulation result, and simultaneously recording the simulation process of the flow simulation;
acquiring an actual execution result corresponding to the second process, and simultaneously performing difference analysis on the process simulation result and the actual execution result to acquire a difference value;
inputting the simulation process into a preset risk verification model to obtain a verification value;
if the difference value is greater than or equal to a preset difference threshold value and/or the check value is greater than or equal to a preset check threshold value, rejecting the first verification strategy which is traversed correspondingly;
when first verification strategies needing to be removed are all removed, taking the first verification strategies which are removed as second verification strategies;
verifying the second acquisition node based on the second verification strategy, and if the second acquisition node passes the verification, taking the corresponding second acquisition node as a fourth acquisition node;
acquiring second target data through the third acquisition node and the fourth acquisition node;
and integrating the acquired second target data to acquire reference information corresponding to the Bluetooth remote controller, and finishing the acquisition.
Preferably, the determining module performs the following operations:
training a specific test opportunity determination model, inputting the reference information to the specific test opportunity determination model, obtaining a determination result, and determining whether to enter specific test opportunity based on the determination result.
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 the 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.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
fig. 1 is a flowchart of a testing method of a bluetooth remote controller according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a testing system of a bluetooth remote controller according to an embodiment of the present invention.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it should be understood that they are presented herein only to illustrate and explain the present invention and not to limit the present invention.
The invention provides a testing method of a Bluetooth remote controller, as shown in figure 1, comprising the following steps:
step 1: based on a preset general test strategy, correspondingly testing the Bluetooth remote controller to be tested;
step 2: acquiring reference information corresponding to the Bluetooth remote controller;
and step 3: determining whether to enter a specific test opportunity or not based on the reference information, and if so, making a proper specific test strategy;
and 4, step 4: and performing corresponding relay aiming test on the Bluetooth remote controller based on the aiming test strategy.
The working principle and the beneficial effects of the technical scheme are as follows:
firstly, testing the Bluetooth remote controller (generally in a shielding box) based on a preset general test strategy (executing all test items, such as power consumption speed test, connection stability test and the like); then, acquiring reference information corresponding to the Bluetooth remote controller (problems occurring in the tested Bluetooth remote controller, problems occurring in the Bluetooth remote controllers which use chips of the same batch or type and are used in user groups, and the like); secondly, determining whether a targeted test opportunity is entered or not based on the reference information (for example, if the problems of the effective information reflecting Bluetooth remote controller are completely concentrated on connection instability, only a connection stability test can be pertinently carried out), and if so, formulating a proper targeted test strategy (for example, executing a connection stability test project); finally, based on the target test strategy, carrying out relay target test on the Bluetooth remote controller;
the embodiment of the invention automatically tests the Bluetooth remote controller to be tested without manual completion, thereby reducing the labor cost.
The invention provides a testing method of a Bluetooth remote controller, which comprises the following steps: acquiring reference information corresponding to the Bluetooth remote controller, including:
acquiring an acquisition node set corresponding to the Bluetooth remote controller, wherein the acquisition node set comprises: a plurality of first acquisition nodes;
acquiring an acquisition target type corresponding to the first acquisition node;
acquiring a preset verification type demand library, matching the acquired target type with the verification type in the verification type demand library, if the matching is in accordance, acquiring at least one first verification strategy corresponding to the verification type in accordance with the matching, meanwhile, taking the corresponding first acquisition node as a second acquisition node, and taking the rest first acquisition nodes as third acquisition nodes;
sequentially traversing the first verification strategy, and acquiring a verification flow corresponding to the traversed first verification strategy during each traversal;
splitting the verification flow into a plurality of first flows;
acquiring a process attribute corresponding to the first process, and acquiring a risk prediction value corresponding to the process attribute;
performing feature extraction on the first process to obtain a plurality of process features;
acquiring a preset verification feature demand library, and matching the process features with verification features in the verification feature demand library;
if the risk prediction value is greater than or equal to a preset risk prediction threshold value and/or the flow characteristic is matched and matched with any verification characteristic, taking the corresponding first flow as a second flow;
acquiring a flow execution sequence corresponding to the verification flow, determining a preset number of first flows executed before the second flow based on the flow execution sequence, and taking the first flows as third flows;
performing association analysis on the second flow and the third flow to obtain an association value;
if the correlation value is greater than or equal to a preset correlation threshold value, taking the corresponding third flow as a fourth flow;
integrating the second process and the fourth process to obtain a process to be simulated;
based on the flow to be simulated, carrying out flow simulation to obtain a flow simulation result, and simultaneously recording the simulation process of the flow simulation;
acquiring an actual execution result corresponding to the second process, and simultaneously performing difference analysis on the process simulation result and the actual execution result to acquire a difference value;
inputting the simulation process into a preset risk verification model to obtain a verification value;
if the difference value is greater than or equal to a preset difference threshold value and/or the check value is greater than or equal to a preset check threshold value, rejecting the corresponding traversed first verification strategy;
when first verification strategies needing to be removed are all removed, taking the first verification strategies which are removed as second verification strategies;
verifying the second acquisition node based on the second verification strategy, and if the second acquisition node passes the verification, taking the corresponding second acquisition node as a fourth acquisition node;
acquiring first target data through the third acquisition node and the fourth acquisition node;
and integrating the acquired first target data to acquire reference information corresponding to the Bluetooth remote controller, and finishing acquisition.
The working principle and the beneficial effects of the technical scheme are as follows:
when reference information is acquired, a plurality of first acquisition nodes (corresponding to a collector for collecting reference information) are set, an acquisition target type (such as problems of a Bluetooth remote controller which uses chips of the same batch or model and is used in a user group) is acquired, the acquisition target type is matched with an authentication type in a preset authentication type demand library (a large number of acquisition target types needing authentication are stored), if the acquisition target type matches with the authentication type, a first authentication strategy corresponding to the matched authentication type is acquired (such as problems occurring when the Bluetooth remote controller is required to be acquired in use, whether a collection scene is correct or not is required to be authenticated, if the collection scene is a forum corresponding to the Bluetooth remote controller user, whether the forum user is correct or not is incorrect, and the like), the first authentication strategy is traversed sequentially, each time duration is acquired, an authentication flow corresponding to the traversed first authentication strategy is acquired, the first flow is split into a plurality of first flows, corresponding flow attributes (such as crawling content), a risk prediction value (risk prediction value) corresponding to the flow attribute is acquired, the risk prediction value is predicted, and the flow characteristic is further extracted, and if the risk value is matched with a second flow characteristic, the second risk value is extracted as a second flow characteristic, and if the risk value is matched with a second flow characteristic, the third flow attribute corresponding to the third flow attribute stored in the authentication target type is acquired; acquiring a flow execution sequence corresponding to the verification flow, and determining a preset number (for example: 3) of third flows to be executed before the second flow based on the flow execution sequence (correlation exists among the flows, and other flows related to the second flow need to be determined to be simulated together when flow simulation, namely risk verification, is required); performing association analysis on the second flow and the third flow to obtain an association value (the greater the association value is, the greater the association between the two flows is), and if the association value is greater, taking the corresponding third flow as a fourth flow; integrating the second flow and the fourth flow to obtain a flow to be simulated, and carrying out flow simulation based on the flow to be simulated; performing difference analysis on the process simulation result and the actual execution result to obtain a difference value, wherein the larger the difference value is, the larger the difference degree is; inputting the recorded simulation process into a preset risk verification model (a model trained in advance and used for risk verification) to obtain a verification value, wherein the larger the verification value is, the larger the risk is; if the difference value is larger and/or the risk check value is larger, rejecting the first verification strategy if the first verification strategy is unqualified; verifying the second acquisition node based on the second verification strategy which eliminates the surplus, and if the second acquisition node passes the verification, taking the corresponding second acquisition node as a fourth acquisition node; acquiring target data (corresponding data collected by the target data) by a third acquisition node (locally collecting the test result of the tested Bluetooth remote controller) which does not need risk verification and a fourth acquisition node qualified by risk verification, and integrating the target data to obtain reference information;
the embodiment of the invention is provided with a plurality of acquisition nodes for acquiring the reference information, thereby improving the comprehensiveness of acquiring the reference information; determining whether the obtaining node needs to carry out risk determination or not based on the obtaining target type of the obtaining node, so that the risk determination efficiency is improved; when the acquired nodes need to carry out risk verification, whether flow simulation, namely risk verification, is needed or not is determined on the basis of corresponding verification flows of the acquired nodes in real time, so that the risk determination efficiency is further improved, the accuracy of screening and verification of the acquired nodes is further improved, and the acquisition quality of reference information is ensured; the improvement of the comprehensiveness and the acquisition quality of the reference information is beneficial to improving the determination accuracy aiming at the test opportunity and the suitability for the formulation of the test strategy.
The invention provides a method for testing a Bluetooth remote controller, wherein in the step 3, whether to enter a test opportunity is determined based on the reference information, and the method comprises the following steps:
training a specific test opportunity determination model, inputting the reference information to the specific test opportunity determination model, obtaining a determination result, and determining whether to enter specific test opportunity or not based on the determination result.
The working principle and the beneficial effects of the technical scheme are as follows:
when determining whether to enter the specific test opportunity, training a specific test opportunity determination model to determine whether to enter the specific test opportunity;
the embodiment of the invention trains the determination model aiming at the test opportunity to determine whether to enter the test opportunity, thereby improving the convenience and being more intelligent.
The invention provides a test method of a Bluetooth remote controller, which trains a determination model aiming at test opportunity and comprises the following steps:
acquiring a preset event node set, wherein the event node set comprises: a plurality of first event nodes;
determining a plurality of event generation records corresponding to the first event node based on a preset event generation record library, wherein the event generation records comprise: a first person determines an event, a generation time point and a generation party for the test opportunity;
acquiring a first contribution degree corresponding to the first manual work determination event aiming at the test opportunity;
acquiring a credit value and a second contribution degree corresponding to the generator;
accumulating and calculating the first contribution degree, the credit value and the second contribution degree to obtain a first rejection value;
generating a time weight based on the generation time point according to a preset weight setting rule;
giving the time weight to the first elimination value to obtain a second elimination value, and associating the second elimination value with the node corresponding to the first event;
accumulating and calculating the second elimination value associated with the first event node to obtain an elimination value sum;
if the rejection value sum is less than or equal to a preset rejection value and a preset threshold value, rejecting the corresponding first event node;
when the first event nodes needing to be removed are all removed, the remaining first event nodes which are removed are used as second event nodes;
obtaining a plurality of second artificial determination events aiming at the test opportunity through the second event node;
and inputting each obtained artificial target test opportunity determination event into a preset neural network training model, performing model training, obtaining a target test opportunity determination model, and finishing training.
The working principle and the beneficial effects of the technical scheme are as follows:
when training the model for determining the test opportunity, setting a plurality of first event nodes (corresponding to a collector who manually performs record determination for the test opportunity), and determining a plurality of event generation records corresponding to the first event nodes, wherein the event generation records comprise the event determined by the first person for the test opportunity (manually performs record determination for the test opportunity), a generation time point and a generator (performed by participation of the generator for the test determination); acquiring a first contribution degree corresponding to a first manual test opportunity determination event (for example, the larger the times of model training historically used by the first manual test opportunity determination event is, the more tasks the trained model should determine for the test opportunity is, the larger the first contribution degree is), and acquiring a credit value of a generator (for example, the truer and more reliable the record of manual test opportunity determination historically provided by the generator is, the larger the credit value is) and a second contribution degree (for example, the more the record of manual test opportunity determination historically provided by the generator is, the larger the second contribution degree is); accumulating (summing) the first contribution degree, the credit value and the second contribution degree to obtain a first rejection value; generating a time weight based on the generation time point according to a preset weight setting rule (the farther the generation time point is from the current time, the smaller the weight is set), and giving a first rejection value (multiplying the two values) to obtain a second rejection value; accumulating and calculating a second elimination value associated with the first event node to obtain an elimination value sum, and if the second elimination value sum is smaller, indicating that the history situation corresponding to the first event node is not good, eliminating the second elimination value sum; acquiring a second artificial test opportunity determination event (generated within a certain time range in the recent past) by removing the remaining second event nodes, inputting the second artificial test opportunity determination event into a preset neural network training model for model training, and acquiring a test opportunity determination model;
according to the embodiment of the invention, when the model is determined aiming at the test opportunity, a plurality of event nodes are set, so that the comprehensiveness of the model training aiming at the test opportunity is improved; in addition, the event nodes are verified based on the event generation records of the event nodes, and the manual test opportunity determining events corresponding to the verified obtaining nodes are obtained and used for training the model determined according to the test opportunity, so that the training quality of the model determined according to the test opportunity is improved to the greatest extent, and whether the model is tested according to the test opportunity is determined more accurately.
The invention provides a test method of a Bluetooth remote controller, which obtains a first contribution degree corresponding to a first manual work determined event aiming at a test opportunity, and comprises the following steps:
determining a plurality of first contribution events corresponding to the first manual determination event aiming at the test opportunity based on a preset first contribution event library;
performing event analysis on the first contribution event, and determining a first contribution proportion of the first manual work determination event corresponding to the first contribution event for a test opportunity;
acquiring a first contribution total value corresponding to the first contribution event;
determining a first contribution value for the first human-to-test-opportunity-determination event corresponding to the first contribution event based on the first contribution proportion and a first total contribution value, and associating with the first human-to-test-opportunity-determination event;
and accumulating the first contribution value associated with the event determined by the first person aiming at the test opportunity to obtain a first contribution degree, and finishing the acquisition.
The working principle and the beneficial effects of the technical scheme are as follows:
when a first person determines a first contribution degree corresponding to an event aiming at a test opportunity, determining a plurality of corresponding first contribution events (such as a record of the first contribution events used for model training historically); performing event analysis on the first contribution event, determining a first contribution ratio of a first person for determining that the event corresponds to the first contribution event according to the test opportunity (for example, if the total number of events historically used for model training is 100, the contribution ratio is 1/100), and acquiring a first contribution total value corresponding to the first contribution event (for example, if the number of times that the trained model is used for coping with the opportunity determination is larger, the first contribution total value is larger); determining a first contribution value (multiplication of the two) based on the first contribution ratio and the first total contribution value; accumulating and calculating a first contribution value of a first manual work for determining event association aiming at the test opportunity to obtain a first contribution degree;
the embodiment of the invention determines the first contribution degree corresponding to the first manual determination event aiming at the test time based on the first contribution event, is reasonable in setting, and ensures the accuracy of determining the first contribution degree.
The invention provides a testing method of a Bluetooth remote controller, which obtains a credit value and a second contribution degree corresponding to a generator and comprises the following steps:
determining a plurality of association generators corresponding to the generators based on a preset association generator library, and simultaneously determining an association relation between the generators and the association generators;
acquiring a first credit condition corresponding to the association generator;
performing condition analysis on the first credit condition to obtain a first analysis value;
obtaining a relation weight corresponding to the incidence relation, giving the relation weight corresponding to the first analysis value, and obtaining a second analysis value;
acquiring a second credit condition corresponding to the generator;
performing condition analysis on the second credit condition to obtain a third analysis value;
if the second analysis value is smaller than or equal to a preset first analysis threshold and/or the third analysis value is smaller than or equal to a preset second analysis threshold, acquiring a preset credit value threshold, and taking the credit value threshold as a credit value corresponding to the generator to finish acquisition;
otherwise, acquiring a preset credit value calculation model, inputting each second analytic value and each third analytic value into the credit value calculation model, acquiring a credit value corresponding to the generator, and finishing acquisition;
determining a plurality of second contribution events corresponding to the producer based on a preset second contribution event library;
performing event analysis on the second contribution event, and determining a second contribution proportion of the producer corresponding to the second contribution event;
acquiring a second contribution total value corresponding to the second contribution event;
determining a second contribution value of the producer corresponding to the second contribution event based on the second contribution proportion and a second total contribution value, and associating with the corresponding producer;
and accumulating and calculating the second contribution value associated with the generator to obtain a second contribution degree, and finishing the acquisition.
The working principle and the beneficial effects of the technical scheme are as follows:
when the credit value corresponding to the generator is obtained, a plurality of association generators corresponding to the association party are determined based on a preset association generator library (association generators storing associations of different generators), and meanwhile, the association relationship (such as guarantee relationship and the like) between the association generators and the association generator is obtained; acquiring a first credit condition corresponding to a correlation generator (historically, the quality condition of a record determined by a person aiming at a test opportunity, and the like), and performing condition analysis to obtain a first analysis value, wherein the larger the first analysis value is, the better the condition is; obtaining a relation weight corresponding to the incidence relation (the closer the incidence relation is, the smaller the relation weight is), giving the first analysis value the relation weight (multiplying the two weights), and obtaining a second analysis value; acquiring a second credit condition corresponding to the generator, and performing condition analysis in the same way as the first credit condition to acquire a third analysis value; if the second analytic value is smaller and/or the third analytic value is smaller, taking a preset credit value threshold value (constant 0) as the credit value of the generator, otherwise, inputting the second analytic value and the third analytic value into a preset credit value calculation model (a pre-trained model for calculating the credit value) to obtain the credit value; when the second contribution degree of the generator is obtained, the second contribution degree is obtained based on a second contribution event (for example, a record determined by manpower provided historically aiming at the test opportunity) in the same way as the first contribution degree is obtained, and the second contribution ratio is analyzed in the same way as the first contribution degree because the record completion is possibly completed by multiple persons;
wherein the credit calculation model calculates the credit based on the second analytic value and the third analytic value according to the following calculation formula:
where σ is the credit value, D t Is the t-th of the second analytical value, Q is the total number of the second analytical values, alpha is the third analytical value, mu 1 And mu 2 The weight value is a preset weight value, and Z is a preset correction coefficient;
in the formula, the second analytic value and the third analytic value are positively correlated with the credit value, and the setting is reasonable.
When the embodiment of the invention acquires the credit value, if the credit condition generated by the association of the generator is poor, the generator is influenced to form an association binding, so that the behavior of providing low-quality records for the generator is restrained to a certain extent, and the quality of determining the event aiming at the test opportunity is indirectly improved; when the second contribution degree is obtained, the second contribution degree is determined based on the second contribution event, the setting is reasonable, and the accuracy of determining the second contribution degree is guaranteed.
The invention provides a testing method of a Bluetooth remote controller, wherein in the step 3, a proper testing strategy is formulated, and the testing method comprises the following steps:
acquiring a preset test strategy set, wherein the test strategy set comprises: a plurality of first test strategies;
sequentially traversing the first test strategy, and acquiring at least one rule matching group corresponding to the traversed first test strategy during each traversal, wherein the rule matching group comprises: screening rules and corresponding first evaluation rules;
screening second target data from the reference information based on the screening rule;
evaluating the second target data based on the first evaluation rule to obtain an evaluation value;
acquiring an evaluation threshold corresponding to the first evaluation rule;
if the evaluation value is larger than or equal to the evaluation threshold value, correlating the evaluation value with the first test strategy, and meanwhile, taking the corresponding first evaluation rule as a second evaluation rule;
accumulating and calculating the evaluation values associated with the first test strategy to obtain a first evaluation value sum;
determining an evaluation value and a threshold value which are corresponding to all the second evaluation rules based on a preset evaluation value and threshold value library;
if the first evaluation value sum is larger than or equal to the evaluation value sum threshold, taking the corresponding first test strategy as a second test strategy;
determining the first evaluation value sum corresponding to the second test strategy as a second evaluation value sum;
and sequencing and integrating the second test strategy according to the second evaluation value sum from large to small to obtain a proper specific test strategy, and finishing the formulation.
The working principle and the beneficial effects of the technical scheme are as follows:
when an appropriate test strategy is formulated, a plurality of first test strategies (test strategies corresponding to different test items) are obtained; sequentially traversing the first test strategy, and acquiring a rule matching group corresponding to the traversed first test strategy during each traversal, wherein the rule matching group comprises a screening rule and a corresponding first evaluation rule (for example, the test strategy is a connection stability test item, the screening rule is condition information about connection stability in screening reference information, and the evaluation rule is a characteristic degree of the condition information representing that the connection stability test item needs to be performed); screening target data (for example, information about connection stability) from the reference information based on a screening rule, evaluating the target data based on a corresponding evaluation rule, and obtaining an evaluation value (for example, the larger the evaluation value is, the larger the degree of characterization is); acquiring an evaluation threshold (constant) corresponding to a first evaluation rule, if the evaluation value is greater than or equal to the corresponding evaluation threshold, associating the evaluation value with a first test strategy, and taking the corresponding first evaluation rule as a second evaluation rule; accumulating and calculating evaluation values associated with the first test strategy to obtain a first evaluation value sum, wherein the larger the first evaluation value sum is, the larger the necessity of executing the first test strategy is; determining an evaluation value and a threshold value which are commonly corresponding to all second evaluation rules based on a preset evaluation value and threshold value library (comprising evaluation values and threshold values which are matched and corresponding to different evaluation rules), and if the first evaluation value sum is more than or equal to the evaluation value and the threshold value, executing a corresponding first test strategy as a second test strategy; sequencing and integrating the second test strategy according to the corresponding second evaluation value and the sum from large to small to obtain a specific test strategy;
according to the embodiment of the invention, when an appropriate test strategy is prepared, the selection is carried out from the first test strategy, and when the selection is carried out, the execution necessity of the first test strategy is quickly determined based on the rule pairing group, so that the selection efficiency of the selection is improved, and meanwhile, the adaptability of the test strategy preparation is improved.
The invention provides a testing system of a Bluetooth remote controller, as shown in figure 2, comprising:
the first testing module 1 is used for correspondingly testing the Bluetooth remote controller to be tested based on a preset general testing strategy;
the acquisition module 2 is used for acquiring reference information corresponding to the Bluetooth remote controller;
the determining module 3 is used for determining whether the specific test opportunity is entered or not based on the reference information, and if so, making a proper specific test strategy;
and the second testing module 4 is used for carrying out corresponding relay aiming test on the Bluetooth remote controller based on the aiming test strategy.
The working principle and the beneficial effects of the technical scheme are already explained in the method claim, and are not described in detail.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (7)
1. A test method of a Bluetooth remote controller is characterized by comprising the following steps:
step 1: based on a preset general test strategy, correspondingly testing the Bluetooth remote controller to be tested;
and 2, step: acquiring reference information corresponding to the Bluetooth remote controller;
and step 3: determining whether to enter a specific test opportunity or not based on the reference information, and if so, making a proper specific test strategy;
and 4, step 4: performing corresponding relay aiming test on the Bluetooth remote controller based on the aiming test strategy;
the step 2: acquiring reference information corresponding to the Bluetooth remote controller, including:
acquiring an acquisition node set corresponding to the Bluetooth remote controller, wherein the acquisition node set comprises: a plurality of first acquisition nodes;
acquiring an acquisition target type corresponding to the first acquisition node;
acquiring a preset verification type demand library, matching the acquired target type with the verification types in the verification type demand library, if the matching is in accordance with the verification type demand library, acquiring at least one first verification strategy corresponding to the verification type in accordance with the matching, and meanwhile, taking the corresponding first acquisition node as a second acquisition node and taking the rest first acquisition nodes as third acquisition nodes;
sequentially traversing the first verification strategy, and acquiring a verification flow corresponding to the traversed first verification strategy during each traversal;
splitting the verification flow into a plurality of first flows;
acquiring a process attribute corresponding to the first process, and acquiring a risk predicted value corresponding to the process attribute;
performing feature extraction on the first process to obtain a plurality of process features;
acquiring a preset verification feature requirement library, and matching the process features with verification features in the verification feature requirement library;
if the risk prediction value is greater than or equal to a preset risk prediction threshold value and/or the flow characteristic is matched and matched with any verification characteristic, taking the corresponding first flow as a second flow;
acquiring a flow execution sequence corresponding to the verification flow, determining a preset number of first flows executed before the second flow based on the flow execution sequence, and taking the first flows as third flows;
performing association analysis on the second flow and the third flow to obtain an association value;
if the correlation value is greater than or equal to a preset correlation threshold value, taking the corresponding third flow as a fourth flow;
integrating the second process and the fourth process to obtain a process to be simulated;
based on the flow to be simulated, carrying out flow simulation to obtain a flow simulation result, and simultaneously recording the simulation process of the flow simulation;
acquiring an actual execution result corresponding to the second process, and simultaneously performing difference analysis on the process simulation result and the actual execution result to acquire a difference value;
inputting the simulation process into a preset risk verification model to obtain a verification value;
if the difference value is greater than or equal to a preset difference threshold value and/or the check value is greater than or equal to a preset check threshold value, rejecting the corresponding traversed first verification strategy;
when first verification strategies needing to be removed are all removed, the remaining first verification strategies are removed to serve as second verification strategies;
verifying the second acquisition node based on the second verification strategy, and if the second acquisition node passes the verification, taking the corresponding second acquisition node as a fourth acquisition node;
acquiring first target data through the third acquisition node and the fourth acquisition node;
integrating the acquired first target data to acquire reference information corresponding to the Bluetooth remote controller, and finishing acquisition;
in the step 3, a suitable test strategy for the target is formulated, which includes:
acquiring a preset test strategy set, wherein the test strategy set comprises: a plurality of first test strategies;
sequentially traversing the first test strategy, and acquiring at least one rule matching group corresponding to the traversed first test strategy during each traversal, wherein the rule matching group comprises: screening rules and corresponding first evaluation rules;
screening second target data from the reference information based on the screening rule;
evaluating the second target data based on the first evaluation rule to obtain an evaluation value;
acquiring an evaluation threshold corresponding to the first evaluation rule;
if the evaluation value is larger than or equal to the evaluation threshold value, correlating the evaluation value with the first test strategy, and meanwhile, taking the corresponding first evaluation rule as a second evaluation rule;
accumulating and calculating the evaluation values associated with the first test strategy to obtain a first evaluation value sum;
determining an evaluation value and a threshold value which are corresponding to all the second evaluation rules based on a preset evaluation value and threshold value library;
if the first evaluation value sum is larger than or equal to the evaluation value sum threshold, taking the corresponding first test strategy as a second test strategy;
determining the first evaluation value sum corresponding to the second test strategy as a second evaluation value sum;
and sequencing and integrating the second test strategy according to the second evaluation value sum from large to small to obtain a proper specific test strategy, and finishing the formulation.
2. The method as claimed in claim 1, wherein the step 3 of determining whether to enter a test opportunity based on the reference information comprises:
training a specific test opportunity determination model, inputting the reference information to the specific test opportunity determination model, obtaining a determination result, and determining whether to enter specific test opportunity or not based on the determination result.
3. The method of claim 2, wherein training the model for testing the timing comprises:
acquiring a preset event node set, wherein the event node set comprises: a plurality of first event nodes;
determining a plurality of event generation records corresponding to the first event node based on a preset event generation record library, wherein the event generation records comprise: a first person determines an event, a generation time point and a generation party for the test opportunity;
acquiring a first contribution degree corresponding to the first manual work determination event aiming at the test opportunity;
acquiring a credit value and a second contribution degree corresponding to the generator;
accumulating and calculating the first contribution degree, the credit value and the second contribution degree to obtain a first rejection value;
generating a time weight based on the generation time point according to a preset weight setting rule;
giving the time weight to the first elimination value to obtain a second elimination value, and associating the second elimination value with the node corresponding to the first event;
accumulating and calculating the second elimination value associated with the first event node to obtain an elimination value sum;
if the rejection value sum is less than or equal to a preset rejection value sum threshold value, rejecting the node corresponding to the first event;
when the first event nodes needing to be removed are all removed, the remaining first event nodes are taken as second event nodes;
obtaining a plurality of second artificial determination events aiming at the test opportunity through the second event node;
and inputting the obtained artificial determination events aiming at the test opportunity into a preset neural network training model, performing model training, obtaining a determination model aiming at the test opportunity, and finishing the training.
4. The method as claimed in claim 3, wherein the obtaining of the first contribution degree corresponding to the event determined by the first human for the test opportunity comprises:
determining a plurality of first contribution events corresponding to the first manual determination event aiming at the test opportunity based on a preset first contribution event library;
performing event analysis on the first contribution event, and determining a first contribution proportion of the first manual work determination event corresponding to the first contribution event for a test opportunity;
acquiring a first contribution total value corresponding to the first contribution event;
determining a first contribution value for the first human-to-test-occasion determination event corresponding to the first contribution event based on the first contribution proportion and a first total contribution value, and associating with the first human-to-test-occasion determination event;
and accumulating the first contribution value associated with the event determined by the first person aiming at the test opportunity to obtain a first contribution degree, and finishing the acquisition.
5. The method as claimed in claim 3, wherein obtaining the credit value and the second contribution degree corresponding to the generating party comprises:
determining a plurality of association generators corresponding to the generators based on a preset association generator library, and simultaneously determining an association relation between the generators and the association generators;
acquiring a first credit condition corresponding to the association generator;
performing condition analysis on the first credit condition to obtain a first analysis value;
obtaining a relation weight corresponding to the incidence relation, giving the relation weight corresponding to the first analysis value, and obtaining a second analysis value;
acquiring a second credit condition corresponding to the generator;
performing situation analysis on the second credit situation to obtain a third analysis value;
if the second analysis value is smaller than or equal to a preset first analysis threshold and/or the third analysis value is smaller than or equal to a preset second analysis threshold, acquiring a preset credit value threshold, and taking the credit value threshold as a credit value corresponding to the generator to finish acquisition;
otherwise, acquiring a preset credit value calculation model, inputting each second analytic value and each third analytic value into the credit value calculation model, acquiring a credit value corresponding to the generator, and finishing acquisition;
determining a plurality of second contribution events corresponding to the producer based on a preset second contribution event library;
performing event analysis on the second contribution event, and determining a second contribution proportion of the producer corresponding to the second contribution event;
acquiring a second contribution total value corresponding to the second contribution event;
determining a second contribution value of the producer corresponding to the second contribution event based on the second contribution proportion and a second total contribution value, and associating with the corresponding producer;
and accumulating and calculating the second contribution value associated with the generator to obtain a second contribution degree, and finishing the acquisition.
6. A test system of a Bluetooth remote controller is characterized by comprising:
the first testing module is used for correspondingly testing the Bluetooth remote controller to be tested based on a preset general testing strategy;
the acquisition module is used for acquiring reference information corresponding to the Bluetooth remote controller;
the determining module is used for determining whether the aiming test opportunity is entered or not based on the reference information, and if so, making a proper aiming test strategy;
the second testing module is used for carrying out corresponding relay aiming test on the Bluetooth remote controller based on the aiming test strategy;
the acquisition module performs the following operations:
acquiring an acquisition node set corresponding to the Bluetooth remote controller, wherein the acquisition node set comprises: a plurality of first acquisition nodes;
acquiring an acquisition target type corresponding to the first acquisition node;
acquiring a preset verification type demand library, matching the acquired target type with the verification types in the verification type demand library, if the matching is in accordance with the verification type demand library, acquiring at least one first verification strategy corresponding to the verification type in accordance with the matching, and meanwhile, taking the corresponding first acquisition node as a second acquisition node and taking the rest first acquisition nodes as third acquisition nodes;
sequentially traversing the first verification strategy, and acquiring a verification flow corresponding to the traversed first verification strategy during each traversal;
splitting the verification flow into a plurality of first flows;
acquiring a process attribute corresponding to the first process, and acquiring a risk prediction value corresponding to the process attribute;
performing feature extraction on the first process to obtain a plurality of process features;
acquiring a preset verification feature demand library, and matching the process features with verification features in the verification feature demand library;
if the risk prediction value is greater than or equal to a preset risk prediction threshold value and/or the flow characteristic is matched and matched with any verification characteristic, taking the corresponding first flow as a second flow;
acquiring a flow execution sequence corresponding to the verification flow, determining a preset number of first flows executed before the second flow based on the flow execution sequence, and taking the first flows as third flows;
performing association analysis on the second flow and the third flow to obtain an association value;
if the correlation value is greater than or equal to a preset correlation threshold value, taking the corresponding third flow as a fourth flow;
integrating the second process and the fourth process to obtain a process to be simulated;
based on the flow to be simulated, carrying out flow simulation to obtain a flow simulation result, and simultaneously recording the simulation process of the flow simulation;
acquiring an actual execution result corresponding to the second process, and simultaneously performing difference analysis on the process simulation result and the actual execution result to acquire a difference value;
inputting the simulation process into a preset risk verification model to obtain a verification value;
if the difference value is greater than or equal to a preset difference threshold value and/or the check value is greater than or equal to a preset check threshold value, rejecting the first verification strategy which is traversed correspondingly;
when first verification strategies needing to be removed are all removed, taking the first verification strategies which are removed as second verification strategies;
verifying the second acquisition node based on the second verification strategy, and if the second acquisition node passes the verification, taking the corresponding second acquisition node as a fourth acquisition node;
acquiring first target data through the third acquisition node and the fourth acquisition node;
integrating the acquired first target data to acquire reference information corresponding to the Bluetooth remote controller, and finishing acquisition;
the determination module performs the following operations:
making an appropriate targeted test strategy, comprising:
acquiring a preset test strategy set, wherein the test strategy set comprises: a plurality of first test strategies;
sequentially traversing the first test strategy, and acquiring at least one rule matching group corresponding to the traversed first test strategy during each traversal, wherein the rule matching group comprises: screening rules and corresponding first evaluation rules;
screening second target data from the reference information based on the screening rule;
evaluating the second target data based on the first evaluation rule to obtain an evaluation value;
acquiring an evaluation threshold corresponding to the first evaluation rule;
if the evaluation value is larger than or equal to the evaluation threshold value, correlating the evaluation value with the first test strategy, and meanwhile, taking the corresponding first evaluation rule as a second evaluation rule;
accumulating and calculating the evaluation values associated with the first test strategy to obtain a first evaluation value sum;
determining an evaluation value and a threshold value which are commonly corresponding to all the second evaluation rules based on a preset evaluation value and threshold value library;
if the first evaluation value sum is larger than or equal to the evaluation value sum threshold, taking the corresponding first test strategy as a second test strategy;
determining the first evaluation value sum corresponding to the second test strategy, and using the first evaluation value sum as a second evaluation value sum;
and sequencing and integrating the second test strategy according to the second evaluation value sum from large to small to obtain a proper specific test strategy, and finishing the formulation.
7. The system for testing a bluetooth remote controller according to claim 6, wherein the determining module performs the following operations:
training a specific test opportunity determination model, inputting the reference information to the specific test opportunity determination model, obtaining a determination result, and determining whether to enter specific test opportunity based on the determination result.
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