CN103995775A - Testing data generating method based on neural network - Google Patents
Testing data generating method based on neural network Download PDFInfo
- Publication number
- CN103995775A CN103995775A CN201410214122.0A CN201410214122A CN103995775A CN 103995775 A CN103995775 A CN 103995775A CN 201410214122 A CN201410214122 A CN 201410214122A CN 103995775 A CN103995775 A CN 103995775A
- Authority
- CN
- China
- Prior art keywords
- test data
- neural network
- testing data
- evaluator
- method based
- 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.)
- Pending
Links
Landscapes
- Debugging And Monitoring (AREA)
Abstract
The invention provides a testing data generating method based on a neural network. The method comprises the steps that testing data generated by a testing data generator are subjected to measurement analysis, testing data indexes are extracted, and in addition, fault categories revealed by the testing data are obtained through an evaluator; a series of testing data indexes and the corresponding fault categories form a learning sample of the neural network, a learning algorithm is used for carrying out neural network training, and after training, a trained neural network is obtained; and testing data are generated through the evaluator, testing data indexes are extracted through measurement analysis and are input the trained neural network, the error revealing degree of the testing data is obtained, and accordingly the testing data are subjected to accepting-rejecting processing. Compared with the prior art, high-quality testing data can be generated, the testing data total number is lowered as much as possible, accordingly, software development cost is lowered, practicability is high, and promotion is easy.
Description
Technical field
The present invention relates to field of computer technology, one creates and loads webpage dynamically specifically, realizes the test data generating method based on neural network of real-time update function.
Background technology
Kore to the definition of test data Generating Problems is: a given program element, find the input of a program, and make it can carry out this program element.Generate the path-oriented that the method for test data has, have towards non-standard road warp.Method has three classes: the test data generating method of random device, object-oriented and path-oriented, each class methods has can be divided into static state or dynamic test data generating method.
One, Static and dynamic test data generating method.Static test data generate, and the input data based on program but the symbol of the program of employing are not carried out and the method for clearing up and converting of expression formula.Dynamic test data generate, and are to utilize the actual input data of program to carry out the method for executive routine.
Two, random test data creation method.Random device, is the simplest test data generating method, can generate the value of arbitrary type input variable, but it is lower to find out the wrong probability of program, and generally conventional random device is as the comparison other of other test data generating methods.
Three, object-oriented test data generating method.A non-standard road warp in preset sequence control flow graph, goal-oriented method generates test data and travels through this road warp.Therefore the method is in fact to generate test data to travel through all paths through this non-standard path, and its effect is more much better than than random device.
In software development process, software test occupies very important status.In general sense, software test is the process of carrying out in order to find mistake.Wherein, designing and generate test data is effectively one of key factor determining software testing quality.Test data generates can be understood to a sampling process, according to corresponding Test coverage standard, adopt certain method, in test data complete or collected works, sample, select a collection of wrong responsive test data, they have the possibility of higher discovery software error.An effective test data method for designing can generate high-quality test data, and reduces as much as possible test data sum, thereby reduces software development cost.So, in order to shorten the construction cycle, reduction expense, researchers are studying the automatic Generating Problems of software test data.Because this problem is extremely complicated, conventionally belong to combinatorial optimization problem, solve and meet difficulty by conventional method, therefore the Intelligent generation problem of software test data becomes the study hotspot of software test.Artificial neural network has the feature of self-adaptation, self-organization and real-time learning, and comparatively active in the research of software test data Intelligent generation, technology also reaches its maturity.Therefore, the test data based on neural network generates a kind of method that has obviously become research test data Intelligent generation problem.
Artificial neural network (ANN) is a kind of operational model, by a large amount of node (or claim neuron) and between be coupled to each other formation.Each node represents a kind of specific output function, is called excitation function.Every two internodal connections all represent that one is referred to as weight for by the weighted value of this connection signal, and this is equivalent to the memory of artificial neural network.The output of network is according to the connected mode of network, the difference of weighted value and excitation function and difference.And network self is all to the approaching of certain algorithm of nature or function conventionally, may be also the expression to a kind of logic strategy.Artificial neural network is parallel and distributed, adopt and Traditional Man intelligence and the diverse mechanism of the information processing technology, the artificial intelligence that has overcome traditional logic-based symbol, in the defect of processing aspect intuition, unstructured information, has the feature of self-adaptation, self-organization and real-time learning.
Study is an important content of neural network research, and its adaptability realizes by study.According to the variation of environment, weights are adjusted, improve the behavior of system according to academic environment difference, the mode of learning of neural network can be divided into supervised learning and unsupervised learning.In supervised learning, the data of training sample are added to network input end, corresponding desired output is compared with network output simultaneously, obtain error signal, the adjustment of controlling weights strength of joint with this converges to definite weights after repeatedly training.In the time that sample situation changes, can revise weights to adapt to new environment through study.When unsupervised learning, not given master sample, is directly placed in network among environment in advance, and learning phase and working stage become one.Now, the Evolution Equation that connects weights is obeyed in the variation of learning law.
Based on this, now provide one can generate high-quality test data, reduce the total test data generating method based on neural network that also can reduce software development cost of test data.
Summary of the invention
Technical assignment of the present invention is to solve the deficiencies in the prior art, and a kind of high-quality test data, test data generating method based on neural network of generating is provided.
Technical scheme of the present invention realizes in the following manner, this kind of test data generating method based on neural network, and its specific implementation process is as follows:
1) training stage:
The test data that test data generating generates is extracted test data index by metric analysis on the one hand, obtains the fault category that test data discloses on the other hand by evaluator; Formed the learning sample of neural network by a series of test data indexes and corresponding fault category, adopt learning algorithm to implement neural metwork training, train the complete neural network that obtains training;
2) forecast period:
Generate test data by evaluator, extract test data index through metric analysis, be entered into the neural network training, obtain the wrong grade of taking off of test data, and accordingly test data is accepted or rejected to processing.
Described test data is that test data generating uses random approach to generate, and this test data obtains corresponding Output rusults after being input to tested software, the learning sample described in a large amount of input and output result composition step 1).
Described evaluator is for correctly judging whether a test data causes tested software to break down, and belongs to the fault of which kind of classification if break down, and this evaluator realizes by software fault pattern analysis, structure test data fault model storehouse.
Described learning algorithm is BP algorithm.
The beneficial effect that the present invention compared with prior art produced is:
A kind of test data generating method based on neural network of the present invention, by after training and testing, can generate high-quality test data, and reduce as much as possible test data sum, thereby reduces software development cost, practical, is easy to promote.
Brief description of the drawings
Accompanying drawing 1 is training stage process flow diagram of the present invention.
Accompanying drawing 2 is forecast period process flow diagrams of the present invention.
Embodiment
Below in conjunction with accompanying drawing, a kind of test data generating method based on neural network of the present invention is described in detail below.
As shown in accompanying drawing 1, Fig. 2, a kind of test data generating method based on neural network is now provided, artificial neural network in the method represents with english abbreviation ANN, predicts that obtaining the wrong ability of taking off of test data selects test data, to reduce test data quantity by ANN.
Its specific implementation process is as follows:
One, the training stage.
The test data that test data generating generates is extracted test data index by metric analysis on the one hand, obtains the fault category that test data discloses on the other hand by evaluator.Formed the learning sample of ANN by a series of test data indexes and corresponding fault category, adopt learning algorithm to implement ANN training, train the complete ANN that obtains training.The learning algorithm here refers to BP algorithm.
Forecast period.
Generate test data by evaluator, extract test data index through metric analysis, be entered into the ANN training, obtain the wrong grade of taking off of test data, and accordingly test data is accepted or rejected to processing.
Training stage is that the study of evaluator is summarized, and forecast period is the popularization to evaluator.Evaluator is the key of ANN prediction test data.Evaluator can correctly judge that whether a test data causes tested software to break down, and belongs to the fault of which kind of classification if break down.Evaluator can be passed through software fault pattern analysis, builds test data fault model storehouse and realizes.
In said method, by using random approach to generate a large amount of test datas, be input to tested software and obtain corresponding Output rusults.By these a large amount of input and output results composition learning samples, by ANN being built, trains, reduction and rule extraction, the influencing each other of the input and output of extracting out, the test data set that obtains simplifying.Need constantly operation system under test (SUT) if generate test data.When unit testing, the running and comparing of driver element is feasible, but to complicated system testing, and constantly operational objective system exists that efficiency is low, cost is high, even infeasible problem.For addressing this problem, can adopt ANN constructing system model, substitute goal systems, evaluation test data.The functional test data that learning sample collection generates by random approach and output thereof obtain.
The application of ANN in software test is mainly reflected in aspect two:
(1) using ANN as sorter, predict the wrong ability of taking off of test data, select accordingly, simplify test data set.
(2) ANN, as system approaches device, replaces real system under test (SUT) to implement software test.No matter ANN is as sorter, or as approaching device, all need to prepare a large amount of learning samples, meticulous evaluator design, network structure are chosen and complicated learning training, only has the test data set that just can obtain simplifying by training, generate high-quality test data set, the automatic generation of test data is obviously very important to software test, is also in close relations equally to whole software development process.
The foregoing is only embodiments of the invention, within the spirit and principles in the present invention all, any amendment of doing, be equal to replacement, improvement etc., within all should being included in protection scope of the present invention.
Claims (4)
1. the test data generating method based on neural network, is characterized in that its specific implementation process is as follows:
1) training stage:
The test data that test data generating generates is extracted test data index by metric analysis on the one hand, obtains the fault category that test data discloses on the other hand by evaluator; Formed the learning sample of neural network by a series of test data indexes and corresponding fault category, adopt learning algorithm to implement neural metwork training, train the complete neural network that obtains training;
2) forecast period:
Generate test data by evaluator, extract test data index through metric analysis, be entered into the neural network training, obtain the wrong grade of taking off of test data, and accordingly test data is accepted or rejected to processing.
2. a kind of test data generating method based on neural network according to claim 1, it is characterized in that: described test data is that test data generating uses random approach to generate, this test data obtains corresponding Output rusults after being input to tested software, the learning sample described in a large amount of input and output result composition step 1).
3. a kind of test data generating method based on neural network according to claim 1, it is characterized in that: described evaluator is for correctly judging whether a test data causes tested software to break down, belong to the fault of which kind of classification if break down, this evaluator realizes by software fault pattern analysis, structure test data fault model storehouse.
4. a kind of test data generating method based on neural network according to claim 1, is characterized in that: described learning algorithm is BP algorithm.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410214122.0A CN103995775A (en) | 2014-05-20 | 2014-05-20 | Testing data generating method based on neural network |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410214122.0A CN103995775A (en) | 2014-05-20 | 2014-05-20 | Testing data generating method based on neural network |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN103995775A true CN103995775A (en) | 2014-08-20 |
Family
ID=51309947
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201410214122.0A Pending CN103995775A (en) | 2014-05-20 | 2014-05-20 | Testing data generating method based on neural network |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN103995775A (en) |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104504441A (en) * | 2014-12-09 | 2015-04-08 | 河海大学 | Method and device for constructing MADALINE neural network based on sensitivity |
| CN104634706A (en) * | 2015-01-23 | 2015-05-20 | 国家电网公司 | Neural network-based soft measurement method for pulverized coal fineness |
| CN109933526A (en) * | 2019-03-06 | 2019-06-25 | 颐保医疗科技(上海)有限公司 | The picture test method that Chinese medicine AI identifies |
| CN110134108A (en) * | 2019-05-14 | 2019-08-16 | 内蒙古电力(集团)有限责任公司内蒙古电力科学研究院分公司 | A kind of aacode defect test method and device |
| CN110967036A (en) * | 2018-09-29 | 2020-04-07 | 北京四维图新科技股份有限公司 | Test method and device for navigation product |
| CN111026664A (en) * | 2019-12-09 | 2020-04-17 | 遵义职业技术学院 | Program detection method and detection system based on ANN and application |
| WO2020191627A1 (en) * | 2019-03-26 | 2020-10-01 | 西门子股份公司 | Method, apparatus, and system for evaluating code design quality |
| CN114816963A (en) * | 2022-06-28 | 2022-07-29 | 南昌航空大学 | Embedded software quality evaluation method, system, computer and readable storage medium |
| US11556870B2 (en) | 2020-07-29 | 2023-01-17 | Oracle International Corporation | System and method for validating a candidate recommendation model |
| US11645467B2 (en) | 2018-08-06 | 2023-05-09 | Functionize, Inc. | Training a system to perform a task with multiple specific steps given a general natural language command |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| TW200929566A (en) * | 2007-12-17 | 2009-07-01 | Nat Univ Chin Yi Technology | Method for fault diagnosis of photovoltaic power generating system |
| CN102411106A (en) * | 2011-11-18 | 2012-04-11 | 广东电网公司广州供电局 | Fault monitoring method and device for power transformer |
-
2014
- 2014-05-20 CN CN201410214122.0A patent/CN103995775A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| TW200929566A (en) * | 2007-12-17 | 2009-07-01 | Nat Univ Chin Yi Technology | Method for fault diagnosis of photovoltaic power generating system |
| CN102411106A (en) * | 2011-11-18 | 2012-04-11 | 广东电网公司广州供电局 | Fault monitoring method and device for power transformer |
Non-Patent Citations (1)
| Title |
|---|
| 傅博: "《软件测试数据智能化生成的研究》", 《软件测试数据智能化生成的研究》 * |
Cited By (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104504441A (en) * | 2014-12-09 | 2015-04-08 | 河海大学 | Method and device for constructing MADALINE neural network based on sensitivity |
| CN104634706A (en) * | 2015-01-23 | 2015-05-20 | 国家电网公司 | Neural network-based soft measurement method for pulverized coal fineness |
| US11645467B2 (en) | 2018-08-06 | 2023-05-09 | Functionize, Inc. | Training a system to perform a task with multiple specific steps given a general natural language command |
| CN110967036A (en) * | 2018-09-29 | 2020-04-07 | 北京四维图新科技股份有限公司 | Test method and device for navigation product |
| CN109933526B (en) * | 2019-03-06 | 2023-01-20 | 颐保医疗科技(上海)有限公司 | Picture testing method for AI identification of traditional Chinese medicinal materials |
| CN109933526A (en) * | 2019-03-06 | 2019-06-25 | 颐保医疗科技(上海)有限公司 | The picture test method that Chinese medicine AI identifies |
| US11860764B2 (en) | 2019-03-26 | 2024-01-02 | Siemens Aktiengesellshaft | Method, apparatus, and system for evaluating code design quality |
| WO2020191627A1 (en) * | 2019-03-26 | 2020-10-01 | 西门子股份公司 | Method, apparatus, and system for evaluating code design quality |
| CN110134108A (en) * | 2019-05-14 | 2019-08-16 | 内蒙古电力(集团)有限责任公司内蒙古电力科学研究院分公司 | A kind of aacode defect test method and device |
| CN110134108B (en) * | 2019-05-14 | 2021-10-22 | 内蒙古电力(集团)有限责任公司内蒙古电力科学研究院分公司 | Code defect testing method and device |
| WO2021115186A1 (en) * | 2019-12-09 | 2021-06-17 | 遵义职业技术学院 | Ann-based program test method and test system, and application |
| CN111026664B (en) * | 2019-12-09 | 2020-12-22 | 遵义职业技术学院 | Program detection method and detection system based on ANN and application |
| CN111026664A (en) * | 2019-12-09 | 2020-04-17 | 遵义职业技术学院 | Program detection method and detection system based on ANN and application |
| US11556870B2 (en) | 2020-07-29 | 2023-01-17 | Oracle International Corporation | System and method for validating a candidate recommendation model |
| CN114816963A (en) * | 2022-06-28 | 2022-07-29 | 南昌航空大学 | Embedded software quality evaluation method, system, computer and readable storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103995775A (en) | Testing data generating method based on neural network | |
| CN102707256B (en) | Fault diagnosis method based on BP-Ada Boost nerve network for electric energy meter | |
| CN104454785B (en) | A kind of engineering machinery hydraulic cylinder fault diagnosis system and the fault sample signal acquisition method being suitable for therewith | |
| CN108051660A (en) | A kind of transformer fault combined diagnosis method for establishing model and diagnostic method | |
| CN108460134A (en) | The text subject disaggregated model and sorting technique of transfer learning are integrated based on multi-source domain | |
| CN104463706A (en) | Method and system for detecting voltage sag event reason for power grid | |
| CN108876163A (en) | The transient rotor angle stability fast evaluation method of comprehensive causality analysis and machine learning | |
| CN109407654A (en) | A kind of non-linear causality analysis method of industrial data based on sparse depth neural network | |
| CN106485594A (en) | A kind of main distribution integration incident response decision method | |
| CN109492790A (en) | Wind turbines health control method based on neural network and data mining | |
| CN109919236A (en) | A kind of BP neural network multi-tag classification method based on label correlation | |
| CN107295537A (en) | A kind of method and system for wireless sensor network reliability of testing and assessing | |
| Wu et al. | Optimized deep learning framework for water distribution data-driven modeling | |
| CN106874963A (en) | A kind of Fault Diagnosis Method for Distribution Networks and system based on big data technology | |
| Kumar et al. | Software fault proneness prediction using genetic based machine learning techniques | |
| CN112632840A (en) | Power grid transient stability evaluation method based on adaptive differential evolution algorithm and ELM | |
| CN114118138A (en) | Bearing composite fault diagnosis method based on multi-label field self-adaptive model | |
| CN108171432B (en) | Ecological risk evaluation method based on multi-dimensional cloud model-fuzzy support vector machine | |
| CN114004155A (en) | Transient stability assessment method and device considering topological structure characteristics of power system | |
| CN116186633A (en) | Power consumption abnormality diagnosis method and system based on small sample learning | |
| CN115526258A (en) | Power system transient stability evaluation method based on Spearman correlation coefficient feature extraction | |
| Zhang et al. | Improved GWO-MCSVM algorithm based on nonlinear convergence factor and tent chaotic mapping and its application in transformer condition assessment | |
| Suntoro et al. | Software defect prediction using AWEIG+ ADACOST Bayesian algorithm for handling high dimensional data and class imbalance problem | |
| Wang et al. | Transient stability assessment with time-adaptive method based on spatial distribution | |
| CN116087692B (en) | Distribution network tree line discharge fault identification method, system, terminal and medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20140820 |
|
| WD01 | Invention patent application deemed withdrawn after publication |