CN114691521A - Software testing platform based on artificial intelligence - Google Patents
Software testing platform based on artificial intelligence Download PDFInfo
- Publication number
- CN114691521A CN114691521A CN202210402181.5A CN202210402181A CN114691521A CN 114691521 A CN114691521 A CN 114691521A CN 202210402181 A CN202210402181 A CN 202210402181A CN 114691521 A CN114691521 A CN 114691521A
- Authority
- CN
- China
- Prior art keywords
- software
- test
- analysis object
- reliability
- qualified
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Withdrawn
Links
- 238000013522 software testing Methods 0.000 title claims abstract description 26
- 238000013473 artificial intelligence Methods 0.000 title claims abstract description 13
- 238000012360 testing method Methods 0.000 claims abstract description 183
- 238000011056 performance test Methods 0.000 claims abstract description 78
- 238000012795 verification Methods 0.000 claims abstract description 43
- 238000000034 method Methods 0.000 claims abstract description 28
- 230000007547 defect Effects 0.000 claims description 20
- 238000012430 stability testing Methods 0.000 claims description 11
- 238000001514 detection method Methods 0.000 claims description 10
- 238000012544 monitoring process Methods 0.000 claims description 9
- 238000013112 stability test Methods 0.000 claims description 6
- 238000012423 maintenance Methods 0.000 claims description 4
- 230000002159 abnormal effect Effects 0.000 claims description 3
- 238000010998 test method Methods 0.000 description 4
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000012502 risk assessment Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/36—Preventing errors by testing or debugging software
- G06F11/3668—Software testing
- G06F11/3672—Test management
- G06F11/3688—Test management for test execution, e.g. scheduling of test suites
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/008—Reliability or availability analysis
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Quality & Reliability (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Computer Hardware Design (AREA)
- Debugging And Monitoring (AREA)
Abstract
The invention discloses a software testing platform based on artificial intelligence, which relates to the technical field of software testing and solves the technical problem that in the prior art, software cannot be matched with a proper testing mode according to the real-time running state of the software during testing, and the running state of a current analysis object is analyzed and judged through the analysis object, so that a proper testing method is matched for the performance testing of the analysis object, the accuracy of the analysis object testing is improved, and the working efficiency of the software testing can be improved by aiming at the testing; the method for matching and testing the performance test of the analysis object improves the accuracy of the software performance test, and simultaneously, the reasonable matching of the performance test mode can improve the working efficiency of the software performance test, reduce the error of the performance test and improve the reliability of performance data; the software is subjected to the reliability verification test, and the reliability of the software is verified and tested, so that the accuracy of the software test is improved.
Description
Technical Field
The invention relates to the technical field of software testing, in particular to a software testing platform based on artificial intelligence.
Background
With the rapid development of computer technology, computer software has been applied to more and more fields, in particular to the key industries of aerospace, finance, medical treatment and other related nationalities. In these fields, software systems are large in scale and complex in logical relationships, and the requirements on the reliability level and the safety level are quite strict. Therefore, people pay more and more attention to software reliability engineering. Meanwhile, as one of the important measures for improving the quality and reliability of software, software reliability testing is gradually becoming the main research direction of software reliability engineering at home and abroad.
However, in the prior art, when software is tested, a proper test mode cannot be matched according to the real-time running state of the software, so that the test of the software lacks pertinence, the test efficiency of the software is reduced, and the cost of the software test process cannot be controlled easily.
In view of the above technical drawbacks, a solution is proposed.
Disclosure of Invention
The invention aims to solve the problems, and provides a software testing platform based on artificial intelligence, which judges the running state of a current analysis object through analysis of the analysis object, so that a proper testing method is matched for the performance test of the analysis object, the accuracy of the test of the analysis object is improved, and the working efficiency of the software test can be improved by aiming at the test; the method for matching and testing the performance test of the analysis object improves the accuracy of the software performance test, and simultaneously, the reasonable matching of the performance test mode can improve the working efficiency of the software performance test, reduce the error of the performance test and improve the reliability of performance data.
The purpose of the invention can be realized by the following technical scheme:
an artificial intelligence based software testing platform comprising:
the reliability verification testing unit is used for performing reliability verification testing on the software, marking the software to be tested as an analysis object, and dividing the analysis object into reliability qualified verification software and non-reliability qualified verification software through the reliability verification testing;
the reliability stability testing unit is used for performing reliability stability testing on the reliability qualified verification software and dividing the reliability qualified verification software into reliability qualified software and reliability unqualified software through the reliability stability testing;
the software analysis unit is used for analyzing the running state of the analysis object, generating a long-period test signal, a short-period test signal and a quality test signal through running state analysis, and sending the long-period test signal, the short-period test signal and the quality test signal to the software test mode matching unit;
the software testing mode matching unit is used for matching the performance test of the analysis object with the testing method;
and the software evaluation terminal is used for evaluating the analysis object and judging whether the test of the analysis object is qualified.
As a preferred embodiment of the present invention, the operation process of the reliability verification test is as follows:
setting a reliability test time period, acquiring the frequency of faults occurring in the operation process of the analysis object and the frequency of incapability of operating the analysis object due to the faults before the operation within the reliability test time period, and respectively marking the frequencies as GZi and YXi; collecting the type number of the corresponding faults of the analysis object in the reliability test time period, and marking the type number as SLi; comparing the reliability verification test coefficient Xi of the analysis object with a reliability verification test coefficient threshold value by analyzing the reliability verification test coefficient Xi of the analysis object obtained by the analysis:
if the reliability verification test coefficient Xi of the analysis object exceeds the reliability verification test coefficient threshold, judging that the reliability verification test of the current analysis object is qualified, marking the current analysis object as reliability qualified verification software, and sending the name of the reliability qualified verification software to a reliability stability test unit;
if the reliability verification test coefficient Xi of the analysis object does not exceed the reliability verification test coefficient threshold, judging that the reliability verification test of the current analysis object is unqualified, marking the current analysis object as non-reliability qualified verification software, and sending the name of the non-reliability qualified verification software to a mobile phone terminal of a manager.
As a preferred embodiment of the present invention, the reliability stability test is performed as follows:
the method comprises the following steps of collecting the frequency of completing a current operation task after a fault occurs in the operation process of the reliability qualification software and the frequency of detecting the fault before the operation of the reliability qualification software, and respectively comparing the frequency with a completion frequency threshold and a detection frequency threshold:
if the frequency of completing the current operation task after a fault occurs in the operation process of the reliability qualification software exceeds the completion frequency threshold and the frequency of detecting the fault before the operation of the reliability qualification software exceeds the detection frequency threshold, judging that the stability analysis of the current reliability qualification software is normal, marking the corresponding reliability qualification software as the reliability qualification software, and sending the name of the reliability qualification software to the software evaluation terminal;
if the frequency of completing the current operation task after the fault occurs in the operation process of the reliability qualified verification software does not exceed the completion frequency threshold, or the frequency of detecting the fault before the operation of the reliability qualified verification software does not exceed the detection frequency threshold, judging that the stability analysis of the current reliability qualified verification software is abnormal, marking the corresponding reliability qualified verification software as the reliability unqualified software, and sending the name of the reliability unqualified software to the mobile phone terminal of the manager.
As a preferred embodiment of the present invention, the operation process of the operation state analysis is as follows:
setting a performance test time period, acquiring a fault cycle of an analysis object in the performance test time period and a ratio of a predicted service life to an actual service life after maintenance is finished on a corresponding fault, and respectively marking the failure cycle and the actual service life as GZZi and SMCi; comparing the running state analysis coefficient Ci of the analysis object with a running state analysis coefficient threshold value by analyzing the running state analysis coefficient Ci of the analysis object:
if the running state analysis coefficient Ci of the analysis object exceeds the running state analysis coefficient threshold, judging that the test period of the current analysis object needs to be long, generating a long-period test signal and sending the long-period test signal and the corresponding analysis object to a software test mode matching unit;
if the running state analysis coefficient Ci of the analysis object does not exceed the running state analysis coefficient threshold, judging that the test period requirement of the current analysis object is short, generating a short-period test signal and sending the short-period test signal and the corresponding analysis object to a software test mode matching unit;
and if the time length difference value of the adjacent fault periods corresponding to the analysis object exceeds the corresponding difference value threshold value, judging that the fault period of the analysis object is unstable, generating a quality test signal and sending the quality test signal and the corresponding analysis object to a software test mode matching unit.
As a preferred embodiment of the present invention, the software testing mode matching unit operates as follows:
after receiving the long-period test signal and the corresponding analysis object, setting the performance test mode of the corresponding analysis object as a quantity test mode, namely setting a test time period, monitoring the number of execution tasks of the corresponding analysis object in the test time period, and judging that the performance test of the corresponding analysis object is qualified if the number of the execution tasks of the analysis object in the test time period exceeds a threshold value of the number of the corresponding execution tasks; if the number of the executed tasks of the analysis object in the test time period does not exceed the threshold value of the number of the corresponding executed tasks, judging that the performance test of the corresponding analysis object is unqualified;
after receiving the short-period test signal and the corresponding analysis object, setting the performance test mode of the corresponding analysis object as a defect test mode, monitoring the operation defects of the corresponding analysis object in a test time period, and if the number of the operation defects of the analysis object in the test time period exceeds the threshold value of the number of the corresponding operation defects, judging that the performance test of the corresponding analysis object is unqualified; if the number of the operation defects of the analysis object in the test time period does not exceed the threshold value of the number of the corresponding operation defects, judging that the performance test of the corresponding analysis object is qualified;
after receiving a quality test signal and a corresponding analysis object, setting a performance test mode of the corresponding analysis object as a quality test mode, monitoring the operation quality of the corresponding analysis object in a test time period, acquiring the task execution time after the analysis object fails in the test time period and the frequency of generating early warning before the analysis object fails in the test time period, respectively marking the task execution time after the failure and the frequency of generating early warning, and respectively comparing the execution time after the failure and the frequency of generating early warning with an execution time threshold and an early warning frequency threshold:
if the execution time length after the fault exceeds the execution time length threshold value and the generated early warning frequency exceeds the early warning frequency threshold value, judging that the performance test of the corresponding analysis object is qualified; if the execution time length does not exceed the execution time length threshold value after the fault or the generated early warning frequency does not exceed the early warning frequency threshold value, judging that the performance test of the corresponding analysis object is unqualified;
marking the analysis object with unqualified performance test as unqualified performance test software; marking the analysis object qualified by the performance test as performance test qualified software; and the name of the qualified software of the performance test is sent to the software evaluation terminal.
In a preferred embodiment of the present invention, the software evaluation terminal operates as follows:
after receiving the corresponding names of the performance test qualified software and the reliability qualified software, analyzing an analysis object: if the analysis objects are performance test qualified software and reliability qualified software, judging that the test of the corresponding analysis object is qualified; if the analysis objects are not both performance test qualified software and reliability qualified software, judging that the test of the corresponding analysis object is unqualified, and performing software optimization on the corresponding analysis object; if the analysis objects are not performance test qualified software or reliability qualified software, judging that the test of the corresponding analysis object is unqualified, and performing software maintenance on the corresponding analysis object.
Compared with the prior art, the invention has the beneficial effects that:
according to the invention, the software is subjected to the reliability verification test, and the reliability of the software is subjected to the verification test, so that the accuracy of the software test is improved, and meanwhile, the reliability of the software is rectified in real time, and the capability of stable operation of the software is judged; the reliability stability test is carried out on the reliability qualified verification software, and the operation stability of the reliability qualified verification software when a fault occurs is judged, so that the software is further tested, and the accuracy of the software test is improved; the running state of the current analysis object is analyzed and judged through the analysis object, so that a proper test method is matched for the performance test of the analysis object, the accuracy of the analysis object test is improved, and the working efficiency of the software test can be improved by carrying out the specific test; the method for matching and testing the performance test of the analysis object improves the accuracy of the software performance test, and simultaneously, the working efficiency of the software performance test can be improved by reasonably matching the performance test mode, the error of the performance test is reduced, and the reliability of performance data is improved.
Drawings
In order to facilitate understanding for those skilled in the art, the present invention will be further described with reference to the accompanying drawings.
Fig. 1 is a schematic block diagram of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Therefore, in the using process of the software, the qualified running of the software directly affects the using quality of a user, so that the software testing is an important link of software maintenance, and the importance of the software testing is as follows: ensuring the quality of the software through software testing; providing information for developers to conveniently prepare for risk assessment; the software test runs through the whole software development process, so that the high quality of the whole software development process is ensured; the software testing is used for discovering the software errors, effectively defining and realizing the assembly process of the software components from the lower layer to the higher layer; whether the software meets the technical requirements specified by the task book and the system definition document is verified through software testing;
the system is used for analyzing the reliability and performance test of the software and evaluating the software according to the result of the reliability and performance test analysis; referring to fig. 1, a software testing platform based on artificial intelligence; the reliability verification test unit is used for carrying out the reliability verification test on the software, the reliability of the software is verified and tested, the accuracy of the software test is improved, meanwhile, the reliability of the software is rectified in real time, and the capability of stable operation of the software is judged;
marking software to be tested as an analysis object, setting a mark i as a natural number larger than 1, setting a reliability test time period, acquiring the frequency of faults occurring in the operation process of the analysis object and the frequency of failure of the analysis object before operation, and respectively marking the frequency of faults occurring in the operation process of the analysis object and the frequency of failure of the analysis object before operation as GZi and YXi; collecting the type number of the faults corresponding to the analysis object in the reliability test time period, and marking the type number of the faults corresponding to the analysis object in the reliability test time period as SLi;
by the formula
Obtaining a reliability verification test coefficient Xi of an analysis object, wherein a1, a2 and a3 are all preset proportionality coefficients, and a1 is more than a2 is more than a3 is more than 0; comparing the reliability verification test coefficient Xi of the analysis object with a reliability verification test coefficient threshold value:
if the reliability verification test coefficient Xi of the analysis object exceeds the reliability verification test coefficient threshold, judging that the reliability verification test of the current analysis object is qualified, marking the current analysis object as reliability verification software, and sending the name of the reliability verification software to a reliability stability test unit;
if the reliability verification test coefficient Xi of the analysis object does not exceed the reliability verification test coefficient threshold, judging that the reliability verification test of the current analysis object is unqualified, marking the current analysis object as non-reliability qualified verification software, sending the name of the non-reliability qualified verification software to a mobile phone terminal of a manager, and upgrading and maintaining the non-reliability qualified verification software by the manager, so that the fault rate of the corresponding non-reliability qualified verification software is reduced;
the reliability stability testing unit is used for performing reliability stability testing on the reliability qualified verification software and judging the operation stability of the reliability qualified verification software when a fault occurs, so that the software is further tested, and the accuracy of software testing is improved;
acquiring the frequency of completing the current running task after a fault occurs in the running process of the reliability qualification software and the frequency of detecting the fault before the running of the reliability qualification software, and comparing the frequency of completing the current running task after the fault occurs in the running process of the reliability qualification software and the frequency of detecting the fault before the running of the reliability qualification software with a completion frequency threshold and a detection frequency threshold respectively:
if the frequency of completing the current operation task after a fault occurs in the operation process of the reliability qualification software exceeds the completion frequency threshold and the frequency of detecting the fault before the operation of the reliability qualification software exceeds the detection frequency threshold, judging that the stability analysis of the current reliability qualification software is normal, marking the corresponding reliability qualification software as the reliability qualification software, and sending the name of the reliability qualification software to the software evaluation terminal; if the frequency of completing the current operation task after a fault occurs in the operation process of the qualified reliability verification software does not exceed the completion frequency threshold, or the frequency of detecting the fault before the operation of the qualified reliability verification software does not exceed the detection frequency threshold, judging that the stability analysis of the current qualified reliability verification software is abnormal, marking the corresponding qualified reliability verification software as unqualified reliability software, sending the name of the unqualified reliability software to a mobile phone terminal of a manager, and increasing the software fault detection strength of the unqualified reliability software by the manager;
the running state of the analysis object is analyzed through the software analysis unit, and the running state of the current analysis object is judged through analysis of the analysis object, so that a proper test method is matched for the performance test of the analysis object, the accuracy of the test of the analysis object is improved, and meanwhile, the working efficiency of the software test can be improved through targeted test;
setting a performance test time period, acquiring a fault cycle of an analysis object in the performance test time period and a ratio of a predicted service life to an actual service life after maintenance is finished corresponding to a fault, and respectively marking the fault cycle of the analysis object in the performance test time period and the ratio of the predicted service life to the actual service life after maintenance is finished corresponding to the fault as GZZi and SMCi; acquiring a running state analysis coefficient Ci of an analysis object by a formula Ci ═ beta (GZZi × s1+ SMCi × s2), wherein s1 and s2 are both preset proportional coefficients, s1 is larger than s2 is larger than 0, and beta is an error correction factor and takes a value of 1.24;
comparing the running state analysis coefficient Ci of the analysis object with a running state analysis coefficient threshold value:
if the running state analysis coefficient Ci of the analysis object exceeds the running state analysis coefficient threshold, judging that the test period of the current analysis object needs to be long, generating a long-period test signal and sending the long-period test signal and the corresponding analysis object to a software test mode matching unit; if the running state analysis coefficient Ci of the analysis object does not exceed the running state analysis coefficient threshold, judging that the test period requirement of the current analysis object is short, generating a short-period test signal and sending the short-period test signal and the corresponding analysis object to a software test mode matching unit;
if the time length difference value of the analysis object corresponding to the adjacent fault period exceeds the corresponding difference value threshold value, the fault period of the analysis object is judged to be unstable, a quality test signal is generated, and the quality test signal and the corresponding analysis object are sent to a software test mode matching unit; in the application, if the time length difference value of the adjacent fault periods corresponding to the analysis object does not exceed the corresponding difference value threshold, the corresponding analysis object is judged to have regularity, namely, the running state analysis coefficients of the corresponding analysis object are compared;
the performance test of the analysis object is matched with the test method through the software test mode matching unit, so that the accuracy of the software performance test is improved, meanwhile, the working efficiency of the software performance test can be improved by reasonably matching the performance test mode, the error of the performance test is reduced, and the reliability of performance data is improved;
after receiving the long-period test signal and the corresponding analysis object, setting the performance test mode of the corresponding analysis object as a quantity test mode, namely setting a test time period, monitoring the number of execution tasks of the corresponding analysis object in the test time period, and judging that the performance test of the corresponding analysis object is qualified if the number of the execution tasks of the analysis object in the test time period exceeds a threshold value of the number of the corresponding execution tasks; if the number of the executed tasks of the analysis object in the test time period does not exceed the threshold value of the number of the corresponding executed tasks, judging that the performance test of the corresponding analysis object is unqualified;
after receiving the short-period test signal and the corresponding analysis object, setting the performance test mode of the corresponding analysis object as a defect test mode, monitoring the operation defects of the corresponding analysis object in a test time period, and if the number of the operation defects of the analysis object in the test time period exceeds the threshold value of the number of the corresponding operation defects, judging that the performance test of the corresponding analysis object is unqualified; if the number of the operation defects of the analysis object in the test time period does not exceed the threshold value of the number of the corresponding operation defects, judging that the performance test of the corresponding analysis object is qualified; the operation defects are expressed as related operation defects such as fault frequency, buffer duration and the like in the software operation process;
after receiving a quality test signal and a corresponding analysis object, setting a performance test mode of the corresponding analysis object as a quality test mode, monitoring the operation quality of the corresponding analysis object in a test time period, acquiring the task execution time after the analysis object fails in the test time period and the frequency of generating early warning before the analysis object fails in the test time period, and comparing the task execution time after the analysis object fails in the test time period and the frequency of generating early warning before the analysis object fails in the test time period with an execution time threshold and an early warning frequency threshold respectively:
if the task execution time after the analysis object fails in the test time period exceeds the execution time threshold, and the frequency of generating early warning before the analysis object fails in the test time period exceeds the early warning frequency threshold, judging that the performance test of the corresponding analysis object is qualified; if the task execution time after the analysis object fails in the test time period does not exceed the execution time threshold, or the frequency of generating early warning before the analysis object fails in the test time period does not exceed the early warning frequency threshold, determining that the performance test of the corresponding analysis object is unqualified;
marking the analysis object with unqualified performance test as unqualified performance test software; marking the analysis object qualified by the performance test as performance test qualified software; sending the name of the qualified software of the performance test to a software evaluation terminal, and simultaneously performing performance rectification on the unqualified performance test software;
after the software evaluation terminal receives the corresponding names of the performance test qualified software and the reliability qualified software, analyzing the analysis object, and if the analysis objects are the performance test qualified software and the reliability qualified software, judging that the test of the corresponding analysis object is qualified; if the analysis objects are not both performance test qualified software and reliability qualified software, judging that the test of the corresponding analysis object is unqualified, and performing software optimization on the corresponding analysis object; if the analysis objects are not performance test qualified software or reliability qualified software, judging that the test of the corresponding analysis object is unqualified, and performing software maintenance on the corresponding analysis object.
The formulas are obtained by acquiring a large amount of data and performing software simulation, and the coefficients in the formulas are set by the technicians in the field according to actual conditions;
when the device is used, the reliability verification test unit is used for carrying out the reliability verification test on the software, the software for testing is marked as an analysis object, and the analysis object is divided into the reliability qualified verification software and the non-reliability qualified verification software through the reliability verification test; the reliability stability testing unit is used for carrying out reliability stability testing on the reliability qualified verification software, and the reliability qualified verification software is divided into reliability qualified software and reliability unqualified software through the reliability stability testing; analyzing the running state of an analysis object through a software analysis unit, generating a long-period test signal, a short-period test signal and a quality test signal through running state analysis, and sending the long-period test signal, the short-period test signal and the quality test signal to a software test mode matching unit; matching the performance test of the analysis object with a test method through a software test mode matching unit; and evaluating the analysis object through a software evaluation terminal, and judging whether the test of the analysis object is qualified.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (6)
1. A software testing platform based on artificial intelligence, comprising:
the reliability verification testing unit is used for performing reliability verification testing on the software, marking the software to be tested as an analysis object, and dividing the analysis object into reliability qualified verification software and non-reliability qualified verification software through the reliability verification testing;
the reliability stability testing unit is used for performing reliability stability testing on the reliability qualified verification software and dividing the reliability qualified verification software into reliability qualified software and reliability unqualified software through the reliability stability testing;
the software analysis unit is used for analyzing the running state of the analysis object, generating a long-period test signal, a short-period test signal and a quality test signal through running state analysis, and sending the long-period test signal, the short-period test signal and the quality test signal to the software test mode matching unit;
the software testing mode matching unit is used for matching the performance test of the analysis object with the testing method;
and the software evaluation terminal is used for evaluating the analysis object and judging whether the test of the analysis object is qualified.
2. The artificial intelligence based software testing platform of claim 1, wherein the reliability verification test is run as follows:
setting a reliability test time period, acquiring the frequency of faults occurring in the operation process of the analysis object and the frequency of incapability of operating the analysis object due to the faults before the operation within the reliability test time period, and respectively marking the frequencies as GZi and YXi; collecting the type number of the corresponding faults of the analysis object in the reliability test time period, and marking the type number as SLi; comparing the reliability verification test coefficient Xi of the analysis object with a reliability verification test coefficient threshold value by analyzing and acquiring the reliability verification test coefficient Xi of the analysis object:
if the reliability verification test coefficient Xi of the analysis object exceeds the reliability verification test coefficient threshold, judging that the reliability verification test of the current analysis object is qualified, marking the current analysis object as reliability verification software, and sending the name of the reliability verification software to a reliability stability test unit;
if the reliability verification test coefficient Xi of the analysis object does not exceed the reliability verification test coefficient threshold, judging that the reliability verification test of the current analysis object is unqualified, marking the current analysis object as non-reliability qualified verification software, and sending the name of the non-reliability qualified verification software to a mobile phone terminal of a manager.
3. The artificial intelligence based software testing platform of claim 1, wherein the reliability stability test is performed as follows:
the method comprises the following steps of collecting the frequency of completing a current operation task after a fault occurs in the operation process of the reliability qualification software and the frequency of detecting the fault before the operation of the reliability qualification software, and respectively comparing the frequency with a completion frequency threshold and a detection frequency threshold:
if the frequency of completing the current operation task after a fault occurs in the operation process of the reliability qualification software exceeds the completion frequency threshold and the frequency of detecting the fault before the operation of the reliability qualification software exceeds the detection frequency threshold, judging that the stability analysis of the current reliability qualification software is normal, marking the corresponding reliability qualification software as the reliability qualification software, and sending the name of the reliability qualification software to the software evaluation terminal;
if the frequency of completing the current operation task after the fault occurs in the operation process of the reliability qualified verification software does not exceed the completion frequency threshold, or the frequency of detecting the fault before the operation of the reliability qualified verification software does not exceed the detection frequency threshold, judging that the stability analysis of the current reliability qualified verification software is abnormal, marking the corresponding reliability qualified verification software as the reliability unqualified software, and sending the name of the reliability unqualified software to the mobile phone terminal of the manager.
4. The artificial intelligence based software testing platform according to claim 1, wherein the running process of the running state analysis is as follows:
setting a performance test time period, acquiring a fault period of an analysis object in the performance test time period and a ratio of a predicted service life to an actual service life after maintenance of a corresponding fault is completed, and respectively marking the fault period as GZZi and SMCi; the running state analysis coefficient Ci of the analysis object is obtained through analysis, and compared with a running state analysis coefficient threshold value:
if the running state analysis coefficient Ci of the analysis object exceeds the running state analysis coefficient threshold, judging that the test period of the current analysis object needs to be long, generating a long-period test signal and sending the long-period test signal and the corresponding analysis object to a software test mode matching unit;
if the running state analysis coefficient Ci of the analysis object does not exceed the running state analysis coefficient threshold, judging that the test period requirement of the current analysis object is short, generating a short-period test signal and sending the short-period test signal and the corresponding analysis object to a software test mode matching unit;
and if the time length difference value of the analysis object corresponding to the adjacent fault period exceeds the corresponding difference value threshold value, judging that the fault period of the analysis object is unstable, generating a quality test signal and sending the quality test signal and the corresponding analysis object to a software test mode matching unit.
5. The artificial intelligence based software testing platform as claimed in claim 1, wherein the software testing mode matching unit is operated as follows:
after receiving the long-period test signal and the corresponding analysis object, setting the performance test mode of the corresponding analysis object as a quantity test mode, namely setting a test time period, monitoring the number of execution tasks of the corresponding analysis object in the test time period, and judging that the performance test of the corresponding analysis object is qualified if the number of the execution tasks of the analysis object in the test time period exceeds a threshold value of the number of the corresponding execution tasks; if the number of the executed tasks of the analysis object in the test time period does not exceed the threshold value of the number of the corresponding executed tasks, judging that the performance test of the corresponding analysis object is unqualified;
after receiving the short-period test signal and the corresponding analysis object, setting the performance test mode of the corresponding analysis object as a defect test mode, monitoring the operation defects of the corresponding analysis object in a test time period, and if the number of the operation defects of the analysis object in the test time period exceeds the threshold value of the number of the corresponding operation defects, judging that the performance test of the corresponding analysis object is unqualified; if the number of the operation defects of the analysis object in the test time period does not exceed the threshold value of the number of the corresponding operation defects, judging that the performance test of the corresponding analysis object is qualified;
after receiving a quality test signal and a corresponding analysis object, setting a performance test mode of the corresponding analysis object as a quality test mode, monitoring the operation quality of the corresponding analysis object in a test time period, acquiring the task execution time after the analysis object fails in the test time period and the frequency of generating early warning before the analysis object fails in the test time period, respectively marking the task execution time after the failure and the frequency of generating early warning, and respectively comparing the execution time after the failure and the frequency of generating early warning with an execution time threshold and an early warning frequency threshold:
if the execution time length after the fault exceeds the execution time length threshold value and the generated early warning frequency exceeds the early warning frequency threshold value, judging that the performance test of the corresponding analysis object is qualified; if the execution time length does not exceed the execution time length threshold value after the fault or the generated early warning frequency does not exceed the early warning frequency threshold value, judging that the performance test of the corresponding analysis object is unqualified;
marking the analysis object with unqualified performance test as unqualified performance test software; marking the analysis object qualified by the performance test as performance test qualified software; and the name of the qualified software of the performance test is sent to the software evaluation terminal.
6. The artificial intelligence based software testing platform according to claim 1, wherein the software evaluation terminal is operated as follows:
after receiving the corresponding names of the performance test qualified software and the reliability qualified software, analyzing an analysis object: if the analysis objects are performance test qualified software and reliability qualified software, judging that the test of the corresponding analysis object is qualified; if the analysis objects are not both performance test qualified software and reliability qualified software, judging that the test of the corresponding analysis object is unqualified, and performing software optimization on the corresponding analysis object; if the analysis objects are not performance test qualified software or reliability qualified software, the test of the corresponding analysis object is judged to be unqualified, and software maintenance is carried out on the corresponding analysis object.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210402181.5A CN114691521A (en) | 2022-04-18 | 2022-04-18 | Software testing platform based on artificial intelligence |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210402181.5A CN114691521A (en) | 2022-04-18 | 2022-04-18 | Software testing platform based on artificial intelligence |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114691521A true CN114691521A (en) | 2022-07-01 |
Family
ID=82143357
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210402181.5A Withdrawn CN114691521A (en) | 2022-04-18 | 2022-04-18 | Software testing platform based on artificial intelligence |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114691521A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115357518A (en) * | 2022-10-20 | 2022-11-18 | 深圳市国佳高鑫科技有限公司 | Method for realizing software service based on cloud service, client and cloud server |
| CN116954624A (en) * | 2023-09-20 | 2023-10-27 | 广州晨安网络科技有限公司 | Compiling method based on software development kit, software development system and server |
-
2022
- 2022-04-18 CN CN202210402181.5A patent/CN114691521A/en not_active Withdrawn
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115357518A (en) * | 2022-10-20 | 2022-11-18 | 深圳市国佳高鑫科技有限公司 | Method for realizing software service based on cloud service, client and cloud server |
| CN116954624A (en) * | 2023-09-20 | 2023-10-27 | 广州晨安网络科技有限公司 | Compiling method based on software development kit, software development system and server |
| CN116954624B (en) * | 2023-09-20 | 2023-12-01 | 广州晨安网络科技有限公司 | Compiling method based on software development kit, software development system and server |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN114691521A (en) | Software testing platform based on artificial intelligence | |
| US8850272B2 (en) | Method and system to measure preventability of failures of an application | |
| CN110083514B (en) | Software test defect evaluation method and device, computer equipment and storage medium | |
| KR101953558B1 (en) | Apparatus and Method for Fault Management of Smart Devices | |
| CN114255784A (en) | Substation equipment fault diagnosis method based on voiceprint recognition and related device | |
| CN113326612A (en) | Power distribution network reliability assessment method and device | |
| CN115265635A (en) | Industrial machine vision detection management system based on data analysis | |
| CN116954624B (en) | Compiling method based on software development kit, software development system and server | |
| CN115114124A (en) | Host risk assessment method and device | |
| CN116773239A (en) | Intelligent gas meter controller reliability life prediction method | |
| CN115936266A (en) | Method, system, device and medium for predicting reliability of rail transit device | |
| US20240289209A1 (en) | Method and apparatus for detecting and explaining anomalies | |
| CN114626758A (en) | Effect evaluation system for medical equipment maintenance | |
| CN114553331A (en) | Method, device, processor and computer readable storage medium for realizing system self-test for radio monitoring station | |
| KR20140055292A (en) | System and method for relating between failed component and performance criteria of manintenance rule by using component database of functional importance determination of nuclear power plant | |
| CN113110393A (en) | Analysis method of outboard motor electric control system based on Internet | |
| CN113507397A (en) | Method for collecting terminal equipment state automatic inspection based on cloud operation and maintenance | |
| CN117971627B (en) | Server host power-off test analysis system based on data analysis | |
| CN109388829B (en) | Electronic product service life measuring and calculating method | |
| CN118118379B (en) | Equipment operation monitoring method and system based on Internet of things | |
| CN118200949B (en) | Fault monitoring system and method for communication equipment | |
| CN112762976B (en) | Automatic method and device for comprehensive test of BMC (baseboard management controller) sensor | |
| CN117648252A (en) | Function test method and device for software application, electronic equipment and storage medium | |
| CN116383068B (en) | Quick test method, device and storage medium of C++ program interface | |
| CN116633664B (en) | Evaluation system for network security monitoring |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| WW01 | Invention patent application withdrawn after publication |
Application publication date: 20220701 |
|
| WW01 | Invention patent application withdrawn after publication |