CN114816982A - SPEA 2-based RESTful API test suite minimization method in unmanned aerial vehicle PX4 - Google Patents
SPEA 2-based RESTful API test suite minimization method in unmanned aerial vehicle PX4 Download PDFInfo
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Abstract
The invention discloses a method for minimizing a RESTful API test suite in unmanned aerial vehicle PX4 based on SPEA2, which comprises the following steps: (1) determining the characteristics of a test case of RESTful API in unmanned aerial vehicle PX4 and designing and generating the test case; (2) three optimization targets, namely fitness functions, are formulated aiming at a test suite of the unmanned aerial vehicle PX 4; (3) labeling data sources of three optimization targets in each test case, and automatically collecting and summarizing data; (4) and (3) carrying out minimization processing on the test suite of the unmanned aerial vehicle PX4 by using a SPEA2 algorithm according to the optimization target in the step (2), so as to obtain a minimized test suite. The method is used for solving the problem of minimization of the test suite of RESTful API in the unmanned aerial vehicle PX4, the designed test suite is suitable for the unmanned aerial vehicle PX4, and the test suite minimized through the SPEA2 algorithm has a better detection effect.
Description
Technical Field
The invention belongs to the field of software testing, and particularly relates to a SPEA 2-based method for minimizing a RESTful API test suite in unmanned aerial vehicle PX 4.
Background
PX4 is platform independent autopilot software (or firmware) that can drive a drone or drone vehicle. It can be programmed in some hardware (e.g. Pixhawk v2) and combined with a ground control station to form a completely independent autopilot system.
The PX4 ground control station is called QGroundcontrol, is an integral part of a PX4 self-driving system, and can run on a plurality of platforms such as Windows, OS X or Linux. Adopt RESTful style to define the interface in unmanned aerial vehicle PX4, can easily realize cross-platform's call, let the user that uses different programming languages can both visit and call and control unmanned aerial vehicle PX 4.
For testing the RESTful API in drone PX4, a test suite is typically developed for testing. However, as the number of RESTful APIs in drone PX4 increases, the number of test cases also increases. Considering the time and resource costs, it is virtually impossible to execute all test cases. Therefore, there is a need to find a solution that effectively minimizes the test suite before executing test cases to reduce test costs.
There are two potential problems with Test Suite Minimization (TSM): one is that the minimized test suite may not be able to cover all test functions (i.e., test requirements); the second is that the minimized test suite may have lower fault detection capabilities than the original test suite. Therefore, in actual test work, test cases need to be screened based on various costs (e.g., execution time of the test cases) and validity criteria (e.g., failure detection capability). The overall goal of test case selection is to select test cases that can be executed within a limited time budget while optimally meeting various cost and efficiency goals. Therefore, we also face the challenge in testing RESTful APIs in cyber-physical systems, i.e., the need to minimize the test suite for testing products while enabling high fault detection capabilities.
Disclosure of Invention
The invention aims to: in order to test the RESTful API in the unmanned aerial vehicle PX4 and reduce the test cost, the invention aims to provide a design method of a RESTful API test case in the unmanned aerial vehicle PX4 and a test suite minimization method based on the SPEA2 algorithm.
The technical scheme is as follows: the invention relates to a method for minimizing a RESTful API test suite in an unmanned aerial vehicle PX4 based on SPEA2, which comprises the following steps:
(1) the method comprises the steps of logically analyzing the characteristics of RESTful APIs in a tested unmanned aerial vehicle PX4 according to the function of the unmanned aerial vehicle;
(2) designing a test case according to the attributes of RESTful APIs in unmanned aerial vehicle PX 4;
(3) defining an optimization target of a test suite of RESTful API in unmanned aerial vehicle PX4, and determining parameter setting of the optimization target;
(4) test suite minimization method using SPEA2 algorithm for RESTful API in drone PX4 according to optimization objectives.
Further, the step (1) includes the steps of:
(11) determining the priority level of each RESTful API according to the characteristics of the RESTful API in the tested unmanned aerial vehicle PX 4; when the priority level of each RESTful API is to be determined, the RESTful APIs need to be sequentially divided into the following levels according to functions: defining a basic control function API as a basic control layer; defining a complex control function API as a complex control layer; defining a logic policy function API as a logic policy layer;
(12) analyzing the sequence calling relation among RESTful APIs according to a logic implementation sequence;
(13) drawing a call graph according to the call relation.
Further, the step (2) comprises the steps of:
(21) setting a value range of RESTful API when designing a test case, and determining a test input value according to a provided RESTful API design description;
(22) and (3) calling the required API according to the calling relation diagram sequence in the step (1) when designing the test case.
Further, the step (3) includes the steps of:
(31) when a RESTful API in an unmanned aerial vehicle PX4 is tested, when a test structure obtained after a test case is executed is inconsistent with an expected result, a fault is detected from the test case result; classifying faults according to physical characteristics of PX4 and influences on operation of an unmanned aerial vehicle PX4 system, determining the severity of the faults, and selecting the severity grade of the faults as one of optimization targets;
(32) the fault detection capability is an important parameter for judging the quality of the test case, the test case with higher fault detection capability is regarded as the test case with better performance, and the fault detection capability is selected as one of optimization targets; the fault detection capability of the test case is measured by the following method:
wherein FDC is fault detection capability; SucR tci Is the percentage of execution success of test case i within a given number of executions; NumCuc tci Is the number of times the test case i was successfully executed within a given number of times; NumFail tci Is the number of execution failures of test case i within a given number of executions; the test case execution time takes the actual execution time as a reference;
(33) the test case execution time can be used for evaluating the quality of a test case, and the test case execution time is selected as one of optimization targets.
Further, the step (4) comprises the steps of:
(41) the method comprises the following steps of screening and optimizing three optimization targets of a test case by using a SPEA2 algorithm, initializing a population, and constructing a non-dominant solution set according to a dominant relationship:
the purpose of classifying and ordering the population P is to divide the population P into a plurality of mutually disjoint sub-populations, and the basis of classifying and ordering the individuals is a Pareto domination relationship; n is the scale of the evolution population P, M is the size of the archive set Q, and T is a preset evolution algebra;
initialization: generating an initial population P 0 While making the archive set Q 0 Is null, t is 0;
fitness distribution: calculating P t And Q t Fitness of all individuals in the population;
selecting an environment: will P t And Q t All non-dominated individuals of (1) are saved to Q t+1 In, if Q t+1 If the size of the tree exceeds M, the size of the tree is reduced by utilizing a pruning process; if Q t+1 Is smaller than M, then P is selected t And Q t Selected dominant individual filling Q t+1 ；
And (4) finishing conditions: if T is greater than or equal to T, orIf he's termination condition is satisfied, Q is set t+1 All non-dominant individuals in (1) are saved in the NDSet as a return result;
pairing selection: to Q t+1 Performing a tournament selection;
and (3) evolution operation: to Q t+1 Performing crossover, mutation operations and saving the results to Q t+1 Wherein t is t + 1;
(42) setting parameters of a selection operator, a crossover operator and a mutation operator according to the three optimization targets; selecting a test suite as an input, and performing test simulation in a simulation simulator; operating an operator and outputting a result;
(43) and combining the simplified test cases into a minimized test suite.
Has the advantages that: compared with the prior art, the invention has the beneficial effects that: the design of the test case fully considers the characteristics of the information physical system, and is more suitable for the test of the information physical system. Compared with the original test suite, the method defines three optimization targets, and the test suite is subjected to minimization processing through the optimization targets, so that the test suite with more excellent performance is obtained.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a RESTful API ranking graph of the present invention;
FIG. 3 is a RESTful API call sequence diagram of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
The invention provides a method for minimizing a RESTful API test suite in unmanned aerial vehicle PX4 based on SPEA2, which comprises the following steps as shown in figure 1:
step 1: and (4) analyzing the characteristics of each RESTful API in the tested unmanned aerial vehicle PX4 according to the unmanned aerial vehicle function implementation logic.
RESTful API classification. The functionality of the RESTful API in drone PX4 was first analyzed. The RESTful API function is the key for supporting the normal work of the information physical system, and the corresponding realization modes of different functions are different. The mode of operation of the RESTful API is analyzed to mark the functionality of the RESTful API.
As shown in fig. 2, when determining the priority level of each RESTful API, each RESTful API needs to be sequentially divided into three levels according to the function, and the basic control function corresponds to the basic control layer; the complex control function API corresponds to a complex control layer; the logic policy function API corresponds to a logic policy layer. And dividing the corresponding RESTful API into corresponding levels according to the analyzed functions of the RESTful APIs.
As shown in FIG. 3, the call relationships between the various APIs may be performed sequentially, possibly in parallel. When one API realizes the functions of the API, the APIs with other functions need to be called sequentially or simultaneously, so that when the mutual relation of the APIs is analyzed, the sequential calling relation among the RESTful APIs needs to be analyzed according to the logic realization sequence, and a calling graph is drawn according to the calling relation.
And 2, step: and designing a test case according to the attributes of RESTful APIs in the unmanned aerial vehicle PX 4.
The API assignment range needs to be considered when designing the test case. And calling the required APIs according to the calling relation diagram sequence in the step 1 when the test case is designed. A python project is created in Pycharm, and all test cases are imported without special symbols such as Chinese characters, blank spaces and the like in a project catalog.
The following classes were introduced:
flash class: for calling the flash framework to write the RESTful API.
request class: the method is used for acquiring data in a RESTful API, wherein the acquired method comprises post, get and the like.
json class: for parsing JSON objects from strings.
pprint: the pprint module provides a method of printing arbitrary python data structures.
time class: for obtaining the current time status
Setting a Fault Level as a label for the numerical value collection of the optimized target Fault severity Level;
setting a label as failed Detected Capacity for the numerical collection of the optimized target average Fault detection percentage;
setting a label as Time Execution for the numerical collection of the Execution Time of the optimized target test case;
and collecting data of each test case according to the definition of the optimization target and storing the data into a database.
And step 3: defining an optimization target of a test suite of RESTful API in unmanned aerial vehicle PX4, and determining parameter setting of the optimization target. The method comprises the following steps:
when testing the RESTful API in drone PX4, a failure will be detected from the test case results when the test results are inconsistent with the expected results. In the face of these faults, it is necessary to try to find the cause of each fault, analyze its impact on the operation of the cyber-physical system, and then classify the faults to determine the severity of the fault. And selecting the fault severity level as one of optimization targets.
Failures occurring in an cyber-physical system are roughly classified into four levels, which are fatal, serious, general, and mild failures, respectively.
The fault detection capability is an important parameter for judging the quality of the test case, and the fault detection capability is selected as one of optimization targets.
The test case execution time can be used for evaluating the quality of a test case, and the test case execution time is selected as one of optimization targets.
Setting a label as Fault Level for the numerical collection of the optimized target Fault severity Level; setting a label as failed Detected Capacity for the numerical collection of the optimized target average Fault detection percentage; setting a label as Time Execution for the numerical collection of the Execution Time of the optimized target test case; and collecting data of each test case according to the definition of the optimization target and storing the data into a database.
Faults occurring in drone PX4 are roughly classified into four levels, which are fatal, general and mild faults, respectively. Slight failure: a failure caused by no influence on the function of the cyber-physical system or external disturbance is regarded as a slight failure. General failure: the fault which has no influence on the whole operation of the information physical system and only has influence on partial functions is regarded as a general fault. Serious failure: a fault that has some influence on the overall operation of the cyber-physical system and affects a part of functions is regarded as a serious fault. Fatal failure: a failure that has a great influence on the overall operation of the cyber-physical system and thus causes the entire cyber-physical system to crash is regarded as a fatal failure.
The Failure Detection Capability (FDC) of a test case can be measured by the following method:
wherein FDC is fault detection capability; SucR tci Is the percentage of execution success of test case i within a given number of executions; NumCuc tci Is the number of times the test case i was successfully executed within a given number of times; NumFail tci Is the number of execution failures of test case i within a given number of executions; the test case execution time is based on the actual execution time.
And 4, step 4: test suite minimization method using SPEA2 algorithm for RESTful API in drone PX4 according to optimization objectives.
The method comprises the steps of using a SPEA2 algorithm to conduct screening optimization on three optimization targets of a test case, firstly initializing a population, and constructing a non-dominated solution set according to a dominated relation.
(1) The construction process is as follows:
the purpose of sorting and ordering the population P is to divide the population P into a plurality of mutually disjoint sub-populations, and the basis of sorting and ordering the individual is a Pareto dominant relationship.
N is the scale of the evolution population P, M is the size of the archive set Q, and T is a predetermined evolution algebra.
1: initialization: generating an initial population P 0 While making the archive set Q 0 Null, t ═ 0.
2: fitness distribution: calculating P t And Q t Fitness of all individuals.
3: selecting an environment: will P t And Q t All non-dominant individuals in (1) are saved to Q t+1 In (1). If Q t+1 If the size of the tree exceeds M, the size of the tree is reduced by utilizing a pruning process; if Q t+1 Is smaller than M, then P is selected t And Q t Selected dominant individual filling Q t+1 。
4: and (4) finishing conditions: if T is more than or equal to T or other termination conditions are met, Q is added t+1 As a return result, all non-dominant individuals in (a) are saved to the NDSet.
5: pairing selection: to Q t+1 A tournament selection is performed.
6: and (3) evolution operation: to Q t+1 Performing crossover, mutation operations and saving the results to Q t+1 And (5) turning to step 2 when t is t + 1.
(2) And (3) setting parameters of a selection operator, a crossover operator and a mutation operator according to three optimization targets (namely fitness functions) provided in the step (3).
(3) And selecting the test suite as an input, and performing test simulation in the simulator.
(4) And operating an operator and outputting a result.
The reduced test cases are combined into a minimized test suite, a python project is created in Pycharm, and a Jmetal framework is used for calling the SPEA2 algorithm.
The following classes were introduced:
fromjmetal.algorithm.multiobjective import SPEA2
fromjmetal.operator import BitFlipMutation,SPXCrossover
from JiaChengSearching.problem import BinarySelection
fromjmetal.util.termination_criterion import StoppingByEvaluations
fromjmetal.lab.visualization import Plot
from jmetal.util.solution import get_non_dominated_solutions,print_function_values_to_file
import time
taking the data in the database as input, the SPEA2 algorithm is set as follows:
population_size＝100,
offspring_population_size＝100,
mutation＝BitFlipMutation(probability＝1.0/problem.number_of_bits),
crossover＝SPXCrossover(probability＝1.0),
termination_criterion＝StoppingByEvaluations(max_evaluations＝35000)
the initialization population size is 100, the offspring size is 100, the cross mutation probability is 1.0, and the termination condition is 350 executions. And (3) putting the final data under an output directory in a csv file form, wherein different file names corresponding to different RESTful APIs: algorithm name _ API name.
And (3) an encoding mode: the population adopts a binary coding mode, wherein 0 represents that the current test case is not selected, and 1 represents that the current test case is selected.
Calculating an inverse generation distance evaluation index IGD of the optimized test suite to evaluate the quality:
wherein, V is a set of points uniformly distributed on the real pareto surface, | V | represents the number of individuals of the set of points P distributed on the real pareto surface; u is an optimal solution set obtained by using an optimization algorithm, and d (t, U) represents the Euclidean distance from the individual t in V to the optimal solution set U. And combining the simplified test cases into a minimized test suite.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
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 (5)
1. A method for minimizing a RESTful API test suite in a SPEA 2-based drone PX4, comprising the steps of:
(1) the method comprises the steps of logically analyzing the characteristics of RESTful APIs in a tested unmanned aerial vehicle PX4 according to the function of the unmanned aerial vehicle;
(2) designing a test case according to the attributes of RESTful APIs in unmanned aerial vehicle PX 4;
(3) defining an optimization target of a test suite of RESTful API in unmanned aerial vehicle PX4, and determining parameter setting of the optimization target;
(4) test suite minimization method using SPEA2 algorithm for RESTful API in drone PX4 according to optimization objectives.
2. The method of minimizing a RESTful API test suite in drone PX4 based on the SPEA2 algorithm in claim 1, wherein the step (1) includes the steps of:
(11) determining the priority level of each RESTful API according to the characteristics of the RESTful API in the tested unmanned aerial vehicle PX 4; when the priority level of each RESTful API is to be determined, the RESTful APIs need to be sequentially divided into the following levels according to functions: defining a basic control function API as a basic control layer; defining a complex control function API as a complex control layer; defining a logic policy function API as a logic policy layer;
(12) analyzing the sequence calling relation among RESTful APIs according to a logic implementation sequence;
(13) drawing a call graph according to the call relation.
3. The method of claim 1 for minimizing a RESTful API test suite in a drone PX4 based on a SPEA2 algorithm, wherein the step (2) comprises the steps of:
(21) setting a value range of RESTful API when designing a test case, and determining a test input value according to a provided RESTful API design description;
(22) and (3) calling the required API according to the calling relation diagram sequence in the step (1) when designing the test case.
4. The method of claim 1 for minimizing a RESTful API test suite in a drone PX4 based on a SPEA2 algorithm, wherein the step (3) includes the steps of:
(31) when a RESTful API in an unmanned aerial vehicle PX4 is tested, when a test structure obtained after a test case is executed is inconsistent with an expected result, a fault is detected from the test case result; classifying faults according to physical characteristics of PX4 and influences on operation of an unmanned aerial vehicle PX4 system, determining the severity of the faults, and selecting the severity grade of the faults as one of optimization targets;
(32) the fault detection capability is an important parameter for judging the quality of the test case, the test case with higher fault detection capability is regarded as the test case with better performance, and the fault detection capability is selected as one of optimization targets; the failure detection capability of the test case is measured by the following method:
wherein FDC is fault detection capability; SucR tci Is the percentage of execution success of test case i within a given number of executions; NumCuc tci Is the number of times the test case i was successfully executed within a given number of times; NumFail tci Is the number of execution failures of test case i within a given number of executions; the test case execution time takes the actual execution time as a reference;
(33) the test case execution time can be used for evaluating the quality of a test case, and the test case execution time is selected as one of optimization targets.
5. The method of claim 1 for minimizing a RESTful API test suite in a drone PX4 based on a SPEA2 algorithm, wherein the step (4) includes the steps of:
(41) the method comprises the following steps of screening and optimizing three optimization targets of a test case by using a SPEA2 algorithm, initializing a population, and constructing a non-dominant solution set according to a dominant relationship:
the purpose of classifying and ordering the population P is to divide the population P into a plurality of mutually disjoint sub-populations, and the basis of classifying and ordering the individuals is a Pareto domination relationship; n is the scale of the evolution population P, M is the size of the archive set Q, and T is a preset evolution algebra;
initialization: generating an initial population P 0 While making the archive set Q 0 Is null, t is 0;
fitness distribution: calculating P t And Q t Fitness of all individuals in the population;
selecting an environment: will P t And Q t All non-dominant individuals in (1) are saved to Q t+1 In, if Q t+1 If the size of the tree exceeds M, the size of the tree is reduced by utilizing a pruning process; if Q t+1 Is smaller than M, then P is selected t And Q t Selected dominant individual filling Q t+1 ；
And (4) finishing conditions: if T is more than or equal to T or other termination conditions are met, Q is added t+1 All non-dominant individuals in (1) are saved in the NDSet as a return result;
pairing selection: to Q t+1 Performing a tournament selection;
and (3) evolution operation: to Q t+1 Performing crossover, mutation operations and saving the results to Q t+1 Wherein t is t + 1;
(42) setting parameters of a selection operator, a crossover operator and a mutation operator according to the three optimization targets; selecting a test suite as an input, and performing test simulation in a simulation simulator; operating an operator and outputting a result;
(43) and combining the simplified test cases into a minimized test suite.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030126586A1 (en) * | 2001-12-27 | 2003-07-03 | International Business Machines Corporation | Organization of test cases |
| JP2011257947A (en) * | 2010-06-08 | 2011-12-22 | Clarion Co Ltd | Method, apparatus and program for preparing test plan |
| US20150261657A1 (en) * | 2014-03-13 | 2015-09-17 | Infosys Limited | Methods for generating test suites and devices thereof |
| US20180095867A1 (en) * | 2016-10-04 | 2018-04-05 | Sap Se | Software testing with minimized test suite |
| CN109034479A (en) * | 2018-07-28 | 2018-12-18 | 河南工业大学 | A kind of Multiobjective Scheduling method and device based on differential evolution algorithm |
| CN110084369A (en) * | 2019-04-08 | 2019-08-02 | 西北工业大学 | Mutation testing variant reduction method based on multiple-objection optimization |
| US20200065234A1 (en) * | 2018-08-27 | 2020-02-27 | Synopsys, Inc. | Test case selection and ordering with covert minimum set cover for functional qualification |
-
2022
- 2022-03-02 CN CN202210201699.2A patent/CN114816982A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030126586A1 (en) * | 2001-12-27 | 2003-07-03 | International Business Machines Corporation | Organization of test cases |
| JP2011257947A (en) * | 2010-06-08 | 2011-12-22 | Clarion Co Ltd | Method, apparatus and program for preparing test plan |
| US20150261657A1 (en) * | 2014-03-13 | 2015-09-17 | Infosys Limited | Methods for generating test suites and devices thereof |
| US20180095867A1 (en) * | 2016-10-04 | 2018-04-05 | Sap Se | Software testing with minimized test suite |
| CN109034479A (en) * | 2018-07-28 | 2018-12-18 | 河南工业大学 | A kind of Multiobjective Scheduling method and device based on differential evolution algorithm |
| US20200065234A1 (en) * | 2018-08-27 | 2020-02-27 | Synopsys, Inc. | Test case selection and ordering with covert minimum set cover for functional qualification |
| CN110084369A (en) * | 2019-04-08 | 2019-08-02 | 西北工业大学 | Mutation testing variant reduction method based on multiple-objection optimization |
Non-Patent Citations (3)
| Title |
|---|
| DIPESH PRADHAN: "CBGA-ES+: A Cluster-Based Genetic Algorithm with Non-Dominated Elitist Selection for Supporting Multi-Objective Test Optimization", 《 IEEE TRANSACTIONS ON SOFTWARE ENGINEERING ( VOLUME: 47, ISSUE: 1, 01 JANUARY 2021)》, 18 November 2018 (2018-11-18), pages 86, XP011830463, DOI: 10.1109/TSE.2018.2882176 * |
| ZITZLER E: "pSPEA2: Optimization Fitness and Distance Calculations for Improving Strength Pareto Evolutionary Algorithm 2 (SPEA2)", 《2016 INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY SYSTEMS AND INNOVATION (ICITSI)》, 20 February 2017 (2017-02-20), pages 1 - 5 * |
| 科研小海绵: "多目标优化 MOP （二）：遗传算法 SPEA2 2001", 《HTTPS://BLOG.CSDN.NET/QQ_32182397/ARTICLE/DETAILS/105875419》, 1 May 2020 (2020-05-01), pages 1 - 6 * |
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