![]() ![]() DataFactory can be integrated with Maven.ĭata Factory is an open source Java API that can be used to generate random data. It was primarily written for populating database for development or test environments by providing values for names, addresses, email addresses, phone numbers, text, and dates. Benerator has also a plugin system that allows for instance to use it with Eclipe or Maven.ĭataFactory is an open source test data generator tool that allows you to easily generate test data. It supports many database systems (Oracle, IBM DB2, MS SQL Server, MySQL, PostgreSQL, …), XML, XML Schema, CSV, Flat Files and Excel. This test data generation tool works on Windows and Unix systems. The products currently included in this article are: Benerator, DataFactory, Data Factory, DataGenerator, generatedata, MockNeat, MySQL Random Data Generator, pydbgen, Spawner, SQLfuzz, Synth, test-data-generatorĪdded MockNeat, MySQL Random Data Generator, pydbgenīenerator is a framework released under both open source and commercial licenses that can be used to generate high-volume test data. Do not hesitate to contact us to include any tool that that is not yet listed in this article. This article presents some open source test data generators. For more information about test data generation, you can visit the Wikipedia discussing this topic on Their goal is to use a predefined data structure to produce the data need for test in a specific format that could range from a spreadsheet file to SQL insert instructions. Test data generators can work in different mode: from the random approach to a more focused or intelligent way. Test data generators are tools that can help you in this task with the automatic generation of hundreds or thousands of customers, products or accounts items with different attributes for their id, email, name, etc. Sometimes you can rely on a small sample, but if you want to perform some load testing or if you want to test a feature that needs to produce a multipage invoice, then you start to need more than just two or three occurrences. I’m hoping its functionality will be continually extended in the future, in particular more complicated transformations being allowed on the SIMPREP command I would like to see.In most of your software testing activities, you need data. ![]() This is certainly just scratching the surface of what the simulation builder can do though. Using the simulation builder is probably overkill for generating data for troubleshooting problems to the list-serve, although if you use its capabilities to mimic the current dataset it can be a quick way to generate fake data that you can upload to a public site without divulging confidential info. Otherwise you need to use DATASET DECLARE and the specify that file name on the OUTFILE command (or I presume save to a file). Note in this code snippet I save the simulated data to the active dataset, which was a feature added as of V22. Once you have the simulation plan created, you can then run the SIMRUN command to generate the data. The PLAN subcommand then specifies a file to save the simulation plan to.The STOPCRITERIA subcommand then specifies that only 50,000 cases are generated.The CORRELATIONS subcommand then lets you specify a set of approximate bivariate correlations for each of the variables.The second specifies variables input2 and input3 as being normally distributed with a mean of zero and a standard deviation of 1. I then have two separate SIMINPUT subcommands, the first specifies input1 as being distributed as Poisson with a mean of 1.2.Note numeric formats on this command are a little different than typical, they are not of the form F3.0, but just take an input format type and then if you want decimals would be F,2 for two decimals. On the SIMINPUT subcommand, you can specify a variable to create, its format and its marginal distribution.The SIMPLAN function is quite complex, but here is a quick rundown of what is going on in this particular statement: Below is an example set of syntax that creates a simulation plan using the SIMPLAN function.įILE HANDLE save /NAME = "!your handle here!".ĭISTRIBUTION = NORMAL(MEAN = 0 STDDEV = 1)įirst I create a file handle named save that ends up being where I save the splan file. You can also use it to create data from scratch, with unique distributions for each variable and have it conform to either an approximate correlation matrix or a contingency table. This is useful for conducting your own simulations, or as I mentioned in the prior post it is often useful when posting questions to discussion lists to have example data that demonstrates your problem.Īs of SPSS version 21, it includes a new simulation procedure in which you can take a predictive model and simulate new outcomes (it is in base, so everyone has it if you have a version of SPSS 21 or later). A while ago I had a post about generating fake data in SPSS. ![]()
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