A guide to AI application testing strategy

Software development continues to accelerate at blistering speeds. As of 2025, the global application development market is valued at $138.41 billion. A rise in AI applications fuels a part of this surge.

Businesses are demanding even more end-to-end automation and increased decision-making as part of their workflows. The demand for increasingly complex capabilities and problem-solving is driving demand.

However, this flood of AI applications has driven a need for increased software testing. In 2025 alone, the software testing market surpassed $45 billion. As the world digitizes, the need for stable, secure, AI-enabled, and seamless software will only continue to grow. However, there is a challenge when it comes to AI applications.

Unlike traditional software, AI applications operate on probabilistic outcomes, making the behavior hard to predict.

In this post, we’ll explore AI application testing, why it’s important, challenges, and how you can build an effective AI application testing strategy for your product.

An AI application is any piece of software that uses artificial intelligence techniques or machine learning algorithms to perform tasks that would normally be done by a human

What is AI application testing?

To explain AI application testing, we need to first understand what an AI application is. An AI application is any piece of software that uses artificial intelligence techniques or machine learning algorithms to perform tasks that would normally be done by a human.

Below are a couple of examples:

1. Quality control: Paint thickness at a car factory can be monitored and actively controlled via machine learning algorithms. In the case of a detected defect, the AI system can reject the painted surface.

2. Automated lead generation via voice agents: Potential leads are called by a machine voice agent. A set of predefined questions is asked. Based on the tone and response of the lead, the agent customizes its own response.

What does it mean to test an AI application?

You might ask, how does testing AI differ from traditional software testing? Some fundamental differences exist. For conventional applications, the output is deterministic. This means that for a certain given input, the output will remain unchanged. Meanwhile, AI models provide outputs that vary under different conditions.

Secondly, AI applications create outputs based on the training data. If the data is of poor quality, inaccurate, or biased, the outputs will reflect it. This is especially a challenge today. The reason? A bulk of the data available to us has bias. For instance, this study demonstrated how large language models were prone to generating racist decisions, for certain people, based on their dialect.

AI applications evolve over time. As more data inputs are provided, the AI algorithm continuously learns. This means that the model keeps getting retrained, and as a result, needs to be retested as well. For conventional applications, on the other hand, once testing is done, you don’t need to retest unless core logic changes are made.

Why is AI application testing important?

AI application testing has evolved into a critical subject for technologists around the world. All for good reason, too. Let’s explore the importance:

• Ensuring reliability: AI applications make life-and-death decisions on behalf of humans every day. A self-driving truck should be able to react appropriately during both normal scenarios and edge cases. One failed decision can end up putting multiple lives at stake. Testers should perform stringent safety checks and work with developers to embed fail-safes, where needed.
• Preventing harmful bias: As we learned earlier, we need to ensure software is as neutral as possible. We don’t want software to be discriminatory or adversely impact decisions for certain groups. Developers can, in part, achieve this by ensuring that the test data being used is neutral and diverse. Testers should vet the data, look for patterns that suggest bias, and use metrics that can help ascertain outcome fairness. Timnit Gebru, founder and executive director at The Distributed AI Research Institute, cautions, “There’s a real danger of systematizing the discrimination we have in society [through AI technologies]. What I think we need to do – as we’re moving into this world full of invisible algorithms everywhere – is that we have to be very explicit, or have a disclaimer, about what our error rates are like.”
• Maintaining compliance: As an AI application evolves, it might need to be refactored for compliance. Testers and product owners should keep themselves updated on new regulations. Product teams should ensure their codebase complies with regional laws and organization policies.

Types of AI application testing

AI application testing incorporates performance, security, reliability, and compliance. To understand better, let’s take the example of AI-enabled supply chain software that provides data and insights to demand planners.

Functional testing

Here, we ensure that the AI model is performing the intended tasks correctly. In our case, demand planners will check if the software’s data output is accurate and as expected.

Performance testing

Used to check if the software’s speed, scalability, and resource usage are appropriate. For our example, testers will check if the supply chain software supports ten concurrent users (demand planners) and if the system slows down.

Bias testing

Ensures that the AI doesn’t discriminate. Let’s say our software has the capability to place orders with suppliers based on an algorithm. It must be ensured that the AI system does not have any bias. The algorithm should not be inclined toward any particular supplier’s ethnicity or gender.

Robustness testing

This is to see if the supply chain software can handle out-of-bound inputs, such as inventory limits, gracefully. For example, testers will input abnormally large values into the input field (such as “2 trillion pallets”) to see if the software breaks or is able to handle the error as expected.

High level, an AI test strategy is just a structured approach to validate if the AI application/system is functioning correctly

Strategies for testing AI applications

What is an AI testing strategy, and why is it important? Let’s dig deeper. High level, an AI test strategy is just a structured approach to validate if the AI application/system is functioning correctly. Developer teams employ a range of key strategies to ensure robust AI application testing:

Data-centric testing

You might have heard the phrase “it’s not the gun, but the man behind the gun that counts.” The same is true for AI models—they are only as effective as their training data. We should cater to a couple of factors here:

Representation

Ask yourself, is the data representative of all expected scenarios? Take an example of a simple AI application that observes a human face and guesses the age. The AI needs to be trained on different demographics, skin conditions, lighting conditions, and angles. The system should cater to a diverse user base under varying conditions.

Bias

We discussed earlier how bias can be a critical issue. We need to actively identify any skewed datasets to avoid unfair outcomes. Let’s say we have an application that inputs various financial KPIs of an individual, like credit history, occupation, age, gender, etc., to gauge if the bank should approve a loan or not. If the training dataset comprises an overwhelming majority of men, we can expect bias. The scenario would cause the model to unfairly reject female applicants.

Explainability of the model

AI models are often treated as a “black box.” Users should be able to understand and explain the reasoning that goes into each of the outputs. Testers should ensure that the AI model can explain the reasoning behind their decisions. Not only is this important for transparency purposes, but it’s an important vector for user trust and compliance.

Security and safety

Just like conventional applications, AI applications are also prone to malicious attacks. During security testing, testers scan the codebase for exploits, identify gaps, and then rank them according to their potential impact. The vulnerabilities are then addressed as part of the development sprints. AI applications are increasingly integrated with the physical world in the form of self-driving vehicles, industrial machinery, and health devices. One miscalculation can translate into injury or fatality.

For instance, an application that regulates insulin levels might malfunction, causing the patient to receive an overdose, resulting in a hypoglycemic state, leading to loss of consciousness and even death. This is why the associated risks and safety of the AI models should be evaluated regularly and rigorously.

Now that you’re aware of the key components of an effective AI testing strategy, let’s explore more.

Steps for testing AI applications

While the exact steps taken vary from organization to organization, a basic test regimen is outlined below:

Bring together all relevant stakeholders across dev, product, and business teams

Step 1: Formulate a testing plan

Bring together all relevant stakeholders across dev, product, and business teams. Begin by defining the objective. Ask questions such as: What is our success criteria going to be? (Such as “>97% accuracy, <3% bias for your AI application.”)

Next, identify test scenarios. What scope of normal, edge, and adversarial cases will you cover? Make your test cases detailed and ensure you align them with your team.

Step 2: Execute

Here you will implement all or a selection of tests covered in the earlier section: functional, performance, bias, and robustness testing. Start with validating your datasets for quality and bias. Run accuracy, robustness, and explainability checks. Testers typically perform A/B tests to evaluate model outcomes over varying inputs. It’s important to validate your workflows end-to-end to ensure your AI application works well, all the way from data ingestion to model serving.

Step 3: Evaluate the results

In this phase, compare your models’ metrics against predefined success criteria determined during Step 1. Verify that your model is fair for all user groups. In case there is an issue, conduct appropriate root-cause analysis and apply fixes.

Step 4: Deployment

Once the application is ready, development teams should follow their organization’s CI/CD process to deploy the application to production.

Best practices for AI application testing

By now, it should be evident why AI applications need to be thoroughly tested. Let’s cover some good practices:

Testers should aim to test as early as possible to prevent defects from “snowballing” into unmanageable product backlogs

Test early and frequently

Testing should be integrated as part of the application’s CI/CD pipeline to ensure that no code snippet ever misses its testing cycle. Testers should aim to test as early as possible to prevent defects from “snowballing” into unmanageable product backlogs.

Automate testing where possible

Use automation testing tools and platforms to drive productivity. This does not mean that we eliminate humans from the equation. It’s equally important to retain appropriate human oversight for more complex problem-solving.

Monitor continuously

Employ the right toolchains to monitor your AI application. The tools should be able to detect drift and model degradation, and generate alerts upon reaching the right thresholds.

Maintain documentation

Test cases, logs, results, and model versioning should be documented in detail. Not only does this help during audits, but it also helps engineers backtrack their steps and root cause problem statements more easily.

Challenges in AI application testing

AI applications are undoubtedly a more complex beast to tame compared to their conventional counterparts. The challenge arises in various shapes and forms:

Non-deterministic outcomes

We discussed how AI models can generate random outputs each time they run. Testers need to become comfortable with this uncertainty and build up their experience and knowledge to arrive at the right conclusions: Is the outcome within acceptable bounds, or is it a complete anomaly?

Higher costs

Due to the need for increased testing, more computational resources and effort are spent. This can be optimized in a number of ways: by using off-peak computation resources, rightsizing frequency for non-critical systems, etc.

Skill gap

The need for testing AI apps grows; however, the number of QA engineers with the right skillset has yet to catch up.

Future trends in AI application testing

AI application testing is still in its evolutionary phase. Major changes can be expected in the time to come:

Increased use of AI in application testing

Not surprisingly, AI will be increasingly leveraged to drive efficiency and scale. More refined AI testing models will continue to be introduced to the market. These AI tools will be able to create test cases, anticipate defects, and automatically run repetitive parts of the testing process.

Increased legislature

As concerns around AI continue to grow, we can expect stricter laws around AI application testing. New testing frameworks will spawn, and we will see the creation of new industry standards. Increased reporting and auditing will be required for higher-risk AI models.

New roles

Due to specialized testing requirements and the technical knowledge required, it would not be a surprise if new roles and responsibilities are created in the process.

Breakthroughs across AI applications in medicine, engineering, manufacturing, and beyond are picking up pace

Heading into the future

AI has taken center stage and will continue to do so in the years to come. We can already start to feel the ripples of change. Breakthroughs across AI applications in medicine, engineering, manufacturing, and beyond are picking up pace. Amidst the commotion, testing is and will become even more mission-critical for AI systems.

Testing is no longer just limited to “checking for bugs.” In the new paradigm, it’s a means of ensuring that the system behaves reliably, fairly, and transparently in an unpredictable world. Engineers need to make deliberate efforts to upskill and ensure that they are able to keep up.

Looking to learn more? Visit the Tricentis learning portal today and start your journey via in-depth articles.

This post was written by Ali Mannan Tirmizi. Ali is a senior DevOps manager and specializes in SaaS copywriting. He holds a degree in electrical engineering and physics and has held several leadership positions in the manufacturing IT, DevOps, and social impact domains.