Welcome to our blog page on test data generation techniques.
Software testing has a lot to do with data. Data is very important in ensuring the functionality of software turns out as expected.
It is also very important in enabling testers to test and validate that the software meets user requirements.
In this blog post, we discuss the basics of test data generation techniques and how they enable the success of software testing.
Let us get to it, shall we?
What is Test Data and Test Data Generation?
Test data in simple terms, refers to any documented form that is useful in testing the functions of software programs. According to Testbytes, test data has to do with the collection of data that either affects or is affected by certain implementations. To this effect, Testbytes identifies two broad categories of test data; negative and positive test data.
Negative test data are useful when it has to do with validations. It can be with a specific input of a function that requires certain results. Negative test data on the other hand, is very important for testing a software’s capacity and how it handles unusual/unexpected inputs.
Test data generation is very important in achieving success in software testing practices. The process, as you may imagine, involves the creation of datasets for testing software apps.
The Entry Journal Books says generated data may be “the actual data that has been taken from previous operations or artificial data created for this purpose”. Similarly, Testbyte agrees that test data generation may involve either actual or artificially sourced data.
However the technique and source of data generated, it must be in sync with the test case with which it will be used.
Test Data Generation Techniques
Let us look at the most common test data generation tools
Manual Test Data Generation Technique
This technique follows the manual process and requires human input. It is a very simple and straightforward way of generating test data. The data generated with the manual technique is important and plays crucial roles in testing various scenarios of the software.
Some Common Types of Manual Test Data
Some of the most commonly manually generated test data include; Null, Valid and the Invalid data set. These are especially useful during performance and standard tests.
Benefits of Manual Test Data Generation Technique
- Allows testers to explore their testing skills
- Allows human testers to gain more knowledge from the testing experience
- It requires human input and not any other resources
Disadvantages of Manual Test Data Generation Technique
- It may be very slow
- It comes with human errors
- It Is time consuming
- It is a complex process
- The tester needs a domain knowledge for the success of the test
Automated Test Data Generation Technique
The automated technique explores the use of test generation tools which results in faster and more accurate results. It is also reliable when using higher data volume.
The most common automation test data generation tool is the Selenium/Lean FT and Web Services APIs.
Benefits of Automation Test Data Generation
- It is fast as it takes less time to complete
- It is accurate
- It can handle large data volumes
- It is easier to add new data to existing data during testing
Disadvantages of Automation Data Generation
- It is costly to implement
- It is difficult to have a comprehensive understanding of how the system works
- It can only be handled by skilled testers
Back-end Data Injection Test Data Generation Technique
This is one of the data test generation techniques that takes a different twist; it uses the back-end servers like SQL injection queries. Using this method requires a human tester to write the test query and inject it into the database.
The data injection works to populate the data sets in respect to the test cases.
It is important to note that this data generation technique allows the tester to easily update the database, thereby increasing the volume of data in use.
Advantages of Back-end Data injection
- It is easier to increase data volume and update the database
- Back-end data injection requires little technical skills from the tester
Disadvantages of Back-end Data injection Technique
- You can only use the back-end
- It is technical, more technical than the manual process
- Tester needs to have knowledge of domain for success
- Corrupt database will lead to disastrous results (ensure you have a proper database backup)
Third-Party Tools as a Test Data Generation Technique
You can easily get the tools from the market and use them in creating and injecting your data for your tests.
Third-party tools are intelligent in nature because they first try to understand everything that surrounds your test.
They use the insights from the study to generate datasets so as to meet the requirement for the test.
Using third party tools allows you to access diverse but very useful data which are also available in high volumes.
Major Benefits of this Technique
- Accuracy of data
- The third-party tool can automatically study and understand your data and domain
- They can handle and take care of backdated data fill
- No need for testers to have detailed experience or be an expert in testing
Disadvantages of third-party tools
- Attracts huge costs
- Limitation of use
PathWise Test Data Generation Technique
This is one of the best test data generation techniques. It offers testers a single path to follow rather than several. This allows for a reduction in cases of confusion and paves way for effectiveness and efficiency.
The technique is easily predictable and it allows testers to expand their testing knowledge in a lot of ways.
With the path-wise test data generation technique, users need to enter the program they intend testing. They also need to enter the test criteria (the path and the coverage).
There are many path wise methods of test data generation. It is up for the testers to make their picks in relation to their requirements and software.
Many software development companies who have properly explored this test data generation technique end up happy.
Top Five Test Data Generation Tools
Test Sigma
Test Sigma is a test automation platform that has one of the most powerful test data generation functionalities. Expert testers love this tool because it enables them to generate high quality data.
With the test data generation tool, testers can cover different scenarios which turns out to be very helpful in software testing
The test data generation tool also boasts of an intuitive interface. It is designed in such a way that allows testers to easily achieve their goals.
The tool can store, manage and even use data efficiently for testing.
Features include
- Intuitive User-Friendly Interface
- Data Generation Options
- Customization
- Data Security
- Seamless Integration
Mostly AI
This one is very innovative and, of course, it is powered by AI technology. This also means it leverages the benefits of machine learning for enhanced performance. With these exceptional features, AI can create realistic and private synthetic data.
Also, the test data generation tool harnesses AI for generating diverse data sets that are close to real world data. What is very interesting about this tool is that while it offers so much, it maintains data privacy protection.
Features include
- Synthetic Data Generation
- Customization
- Privacy Preservation
- Scalability
- Integration
DatProf
This is yet another amazing test data generation tool. It comes with simplicity and streamlining abilities which complements its high quality and representative data creation.
Its user-friendly interface is worth commenting on and the way it equips testers, especially in creating diverse datasets is amazing.
Features Include
- Rule-Based Generation
- Pattern-Based Generation
- Random Generation
- Bulk Data Generation
- Data Masking
- Data Validation
EMS Data Generator
If you are looking for one of the most impressive test data generation tools, this is it. The tool is both powerful and versatile in its offerings. Design is simple and so does it simply the whole process for you.
Using the tool, testers can generate both realistic and customisable data in large volumes.
The EMS data generating tool offers strong support for different database types/platforms. With the tool, testers can easily define their rules of engagement as well as design a template.
The Features Include
- Multi-Platform Support
- Customizable Data Generation
- Data Randomization
- Data Masking
- SQL Script Generation
- Performance and Scalability
RedGate SQL Data Generator
The RedGate SQL Data Generator is among our top five most powerful tools because of its general abilities. It simplifies and automates, and it offers comprehensive features and several useful functionalities.
Its features include
- Database-Aware Generation
- Diverse Data Generation
- Customization and Constraints
- Data Masking
- Performance and Scalability
- Integration
Top Challenges of Test Data Generation Techniques
Test Data Generation can be complex. This is why It often involves dealing with various challenges. In this field, sometimes the code used in different scenarios may not truly mirror the kind of code employed in the real-world.
Let’s delve into the specific issues encountered when implementing test data generation techniques for industry-standard code.
Arrays and Pointers
Arrays and pointers share similar constructs and present common challenges. When it comes to symbolic execution, these data types introduce complications as their values are usually unknown.
Generating input for arrays and pointers poses multiple problems, such as determining the array index or structuring input for pointers. This complexity is further compounded by the potential dynamic allocation of arrays and pointers.
Objects
Objects, owing to their dynamic nature, pose difficulties in test data generation techniques. This challenge is amplified when dealing with other object-oriented features.
The unpredictable runtime behaviour of object-oriented code makes it challenging to ascertain which code will be executed. Attempts have been made to address this issue through techniques like mutation.
Loops
Loops that exhibit varying behaviour based on input variables can be problematic. Predicting the exact path they might take is challenging.
However, if the loop’s behaviour remains consistent for a given input, it doesn’t pose a problem.
Some techniques have been proposed to mitigate potential issues with such loops.
Modules
A typical program consists of modules, which in turn contain functions. Generating test data for these functions can be approached in two ways:
- Brute Force Solution
This involves inlining the called functions into the target code.
- Analysing the Called Functions
An alternative is to analyse the called functions first and generate path predicates for them.
However, it’s worth noting that the source code of modules is often inaccessible, making complete static analysis challenging.
Infeasible Paths
Generating test data to traverse a specific path requires solving a system of equations. If no solutions exist, the path is deemed infeasible.
Unfortunately, this process is limited by the undecidable nature of these equations. Typically, a maximum number of iterations is set before declaring a path as infeasible.
Constraint Satisfaction
Constraint satisfaction involves finding a solution that adheres to a set of constraints imposed on variables.
This solution comprises a set of variables that satisfy all constraints. Solving constraint satisfaction problems is inherently challenging, and proper implementation is often lacking.
Various methods, such as iterative relaxation and genetic algorithms, have been employed to address constraints in programs.
Our Conclusion on Test Data Generation Techniques
Test data generation techniques are a vital part of ensuring software reliability.
From manual methods to automated tools and innovative approaches, there are various ways to generate test data.
While challenges exist, the field continues to evolve, promising better solutions for software testers.
Embracing these techniques and tools is essential for ensuring the quality of software in an ever-changing tech landscape.
