The role of synthetic data in software testing and development

To say that synthetic data has emerged as a core solution for software testing and development is not an overstatement. 

The current context sees testing projects having to strike a difficult balance: the opportunity to employ data for enriching pre-production contexts, while also complying with increasingly strict data privacy regulations. A balance that represents a major challenge for dataset management, which, without the right tools and processes, can quickly become costly and unworkable for its intended purposes.

This is where synthetic test data comes in: a key data strategy to overcome data challenges in testing environments and which is increasingly accessible for companies via specific tools.

But what exactly is synthetic data, how does synthetic data generation take place, what are its advantages and what is the right tool to access its potential? Keep reading to find out in this in-depth guide.

What is synthetic data?

Definition and purpose of synthetic data

Synthetic data is data that has been generated artificially so that it mimics real-world data. As such, it retains the original data’s characteristics, but without corresponding to any actual real-world information. 

This allows synthetic data to fulfill its main purpose: to generate datasets that are representative of original information but don’t include any sensitive data. Thus, synthetic data generation allows for extracting value from data while also complying with privacy regulations and protecting citizens’ personal data.

As such, synthetic data has mainly proven its value across various sectors where using real data is either restricted or challenging, from healthcare to finance and public authorities.

In the field of software testing and development processes specifically, synthetic data opens the door to protocols that are safer and more comprehensive. As such, the European Data Protection Supervisor highlights how ‘synthetic data is gaining traction within the machine learning domain’, providing help in training algorithms, as well as software testing and quality assurance contexts. 

In fact, research company Gartner foresees 75% of businesses will use synthetic data created via generative AI by 2026. For what purpose? Gartner predicts the data will unlock pathways to ‘simulate environments and identify new product development opportunities, especially in highly regulated industries… [enabling] fast prototyping of software, digital and hybrid experiences.’

However, it’s important to note that synthetic data is not without its challenges. Ensuring that artificially generated data is truly representative of real-world scenarios can be highly complex — particularly in sensitive fields like healthcare and clinical trials. As Gartner points out, a single inaccurately calculated parameter in a synthetic dataset could pose serious risks to patient safety. In such contexts, perfect precision in representing the original data is critical. These limitations and challenges will be addressed in detail in the following sections.

What is synthetic data vs. real data? The key differences

How synthetic data and real data are created

• Real-world data is collected from actual contexts (for instance, patient medical records or customer purchase behaviour).
• Synthetic data is generated artificially via different techniques, from statistical models to today’s advanced methods that rely on machine learning algorithms. We explore some of these below in this article.

The accuracy of synthetic data and real data

• Real-world data represents accurate information from true events.
• Synthetic data mimics the characteristics of real-world data, but doesn’t present its authentic information.

Compliance with data privacy regulations

• Real-world datasets can contain PII (Personally Identifiable Information) markers, which are considered sensitive information. This limits these datasets’ use following regulations such as the GDPR, the HIPAA or CPRA.
• Synthetic data can be created to eliminate PPI markers, so that it’s fully compliant with data privacy regulations.

Availability and scalability of synthetic data and real data

• In certain contexts, comprehensive real-world data can be difficult to obtain or scale. The reasons are many: from access restrictions to potential privacy concerns or issues related to inaccurate or incomplete data collection. As such, scalability is limited to the actual availability of data.
• Synthetic data can be generated on demand to extend datasets, address gaps or generate diverse datasets where wide scopes or edge cases are incorporated.

Main uses of synthetic data and real data

• Real-world data is crucial for some production contexts where actual information must be leveraged, or for processes that require data on actual conditions and practices (for instance, regulatory reports for compliance).
• Synthetic data stands out in processes such as testing, model training, or prototyping. It also facilitates dataset sharing with third-parties, enabling collaborative processes.

You might be interested: Real-World vs. Synthetic data: The key to better testing

Why is synthetic test data generation important for testing? 

Ensuring data privacy and compliance 

Synthetic data doesn’t include sensitive information, and is thus key for guaranteeing data privacy compliance.

The General Data Protection Regulation (GDPR) was a key milestone in bringing greater attention to synthetic data, as it emphasized the need to protect all sensitive information and addressed techniques such as anonymization or synthetic data for such purposes.

This was further confirmed by the EU AI Act in 2023, which directly references synthetic data as a valid strategy for guaranteeing data privacy and securing systems’ technical resilience.

With similar regulations emerging across the world (including HIPAA, CCPA or CPRA), comprehensive synthetic data generation emerges as a key compliance strategy for software testing and development.

Generating diverse and edge-case test scenarios

Synthetic data allows for enhanced control over datasets, as it opens the door to building ad hoc datasets for comprehensive, granular testing.

As such, synthetic data can be designed to include extreme conditions and edge-cases that may not be present in real-world datasets but that add depth and robustness to software testing scenarios. Ultimately, this opens the door to better quality outputs in software developments. 

As such, synthetic data can be designed to include extreme conditions and edge-cases that may not be present in real-world datasets. While these datasets must be carefully designed to reflect plausible scenarios, their use can bring valuable depth and robustness to the software validation process. Ultimately, this opens the door to better quality outputs in software developments.

Additionally, well-designed synthetic data generation is a useful strategy for addressing the potential biases in real-world data (specially when data is scarce), amplifying its variety and balance whenever needed.

Reducing dependency on production data for testing

Synthetic data generation can reduce data provision times to minutes, a significant decrease compared to production data, where times can scale up to days or weeks. Additionally, it can be scaled easily, augmenting available test data while also ensuring new data maintains uniformity, is labeled and standardized.This on-demand, self-service approach to dataset generation then cascades into faster workflows, bypassing conventional restrictions related to production data that would slow down processes in testing.

Synthetic data generation techniques

Rule-based synthetic data generation

This is synthetic data that is created based on predefined rules created by humans, so that datasets are generated that resemble certain expected scenarios.

For instance, the testing processes for a medical app could develop a synthetic dataset that includes the following basic layout of rules:

• Patient names that are randomly selected from a list of common first and last names.
• Ages determined between 60-70
• Heart rates between 60–100 bpm
• Diagnosis is selected from a list of common conditions (for example, diabetes or hypertension).
• Medications must be in accordance with diagnosis.

AI and machine learning-driven synthetic data

The evolution of AI and machine learning models (and, specially, generative models) are unleashing new opportunities in synthetic data generation. In this case, the models are trained on real data and learn their underlying characteristics in order to then create artificial datasets that mimic it.

There are several techniques available within this approach:

• Language models such as GPT (Generative Pre-trained Transformer)
• GANs (Generative Adversarial Networks)
• VAEs (Variational Autoencoders)

Statistical methods for generating synthetic datasets

These methods rely on representing the original data’s distribution via mathematical and statistical models.

Examples include parametric models, random sampling or bayesian networks.

Choosing the right Synthetic Data solution

Key factors when selecting a synthetic data generation tool

While this article has covered some of the main advantages of synthetic data, it’s important to understand the choice of data generation tool will largely determine whether testers can access such benefits or not.

In fact, inadequate tools can foster a series of limitations that are inherent to poor synthetic data, such as: 

• A lack of representation of temporal changes in data or their evolution over time.
• A limited ability to replicate the intricate relationships and dependencies found in real-world data.
• Data that fails to capture edge cases and rare scenarios.
• Does not account for outdated systems or legacy issues.
• Data is unrealistic or has reduced richness because it misses the irregularities and diversity typically seen in real-world environments.
• Data that fails to accurately model the true data distribution and patterns observed in real data.

These gaps could make synthetic data approaches unreliable for testing environments that aim to mimic production conditions accurately.

As such, here’s a look at some of the essential things to consider when choosing the right tool for synthetic data generation.

Understanding synthetic test data quality parameters

From an overall perspective, the right tool will largely be the one that allows for quality synthetic data generation. 

This statement directly links with the question on how to evaluate synthetic data quality. In this quest, the following are key criteria can be considered, according to each project’s characteristics:

• Fidelity to real-life data. Fidelity is important because it determines whether models trained on a synthetic dataset will produce accurate insights and avoid potential flaws. In this regard, an example involves looking at whether synthetic data has retained the original statistical patterns or not.
• Dataset size. This can be key in scenarios such as machine learning model training, which requires large data volumes. Generally speaking, a big dataset size enables training on diverse scenarios and thus improves the model’s robustness. 
• Consistency. Some complex projects where integrated testing takes place will require data consistency, so that data remains coherent across all systems and applications where testing takes place.
• Dataset diversity. This quality aims at minimizing data bias, so that a range of different scenarios (relevant to the project) is represented in data.
• Data privacy and regulation compliance. Testing data mustn’t contain sensitive information and must be in line with privacy standards.

All in all, quality synthetic data should be useful and relevant to the project, maintain high fidelity when compared to real data and ensure compliance with data regulations.

Establishing the project’s requirements and finding the tool to match them

Once the key data quality requirements have been taken into account, it’s time to look at what the exact requirements for the project are. This means establishing:

• Types of data needed (structured data, unstructured, sequential…).
• Relevant privacy requirements. 
• The synthetic data generation techniques that will be employed, according to the project’s needs. 

The goal here is to ensure the choice of tool will match the project’s requirements. For instance, if the dataset must be compliant with GDPR requirements, the selected tool for synthetic data generation should be designed accordingly.

Other criteria for choosing the right synthetic data tool

Some desirable features in synthetic data platforms include:

• The data generation platform offers customizability and advanced control. This is key for introducing edge cases or simulating specific conditions, which can elevate testing procedures and output software.
• An on-demand, self-service approach to data generation that speeds up processes.
• The right tool should integrate with ease in the existing digital ecosystem where it’s going to operate, including the test automation and CI/CD tools and databases.
• It should provide testers with capacities not only to operate large datasets, but also for scaling up operations when needed.
• The right platform will offer security guarantees to prevent leaks and other issues.

Why icaria Technology provides the best synthetic test data approach

At icaria Technology we’ve developed a model-based synthetic data approach that encourages the production of realistic, secure, and scalable datasets for high-quality testing environments. 

Our platform aims at helping testers overcome the key data challenge in test environments, while also bypassing the potential limitations of synthetic data outlined above. As such, it offers on-demand high-quality synthetic data generation that mimics real-world data with security, compliance and performance as priorities.

Our platform helps testers strike a balance between extracting value from data while also complying with data privacy regulations. In order to do so, the tool is able to:

• Replicate the structure, patterns and complexity of real data with no privacy risks. Data is generated from pre-existing models. This approach  creates realistic test scenarios where relationships, distributions and behaviours are maintained, without any privacy concerns and guaranteeing compliance.
• Seamless transitioning between testing stages. The tool preserves differential integrity and data relationships, so that consistency across development, staging, and production phases is achieved.
• Tailored datasets. The tool offers flexibility and total control on dataset parameters. Rare scenarios, edge cases or new features that require testing can all be included, all while ensuring testers ease when introducing commands.
• Scalability. The platform is ready to incorporate large volumes of test data for extensive performance and scalability tests.
• Automation. It minimizes manual efforts and rework costs thanks to automating a number of processes during the testing lifecycle.

The tool builds up from a foundation made of a coherent data structure that has the necessary structural integrity and is relevant for the tests. Then, synthetic data generation rules come in to modify the attributes, so that the resulting synthetic data can coexist with the original data in the same testing environment.

The end goal aims high-quality testing data that guarantees data consistency even in highly complex testing, such as integrated testing procedures.

icaria Technology offers these synthetic data generation tools as part of a more comprehensive TDM platform. Through icaria TDM, testers can combine real-world data with synthetic data generation, laying the groundwork for accuracy and compliance in test data management.

Want to learn more about icaria TDM and our approach to synthetic data? Get in touch with us and talk to our experts about your project’s needs.