--- title: "Software testing: how it changes in the era of AI" description: "Software testing and AI: how to evaluate AI-based applications through probabilistic models, metrics, advanced testing, and complexity governance" image: https://newsroom.kireygroup.com/hubfs/SoftwareDevelopment_9.jpg --- [kireygroup.com](https://www.kireygroup.com/) [![](https://newsroom.kireygroup.com/hubfs/item-2020-dicembre/logo-Blog-v2.svg)](https://newsroom.kireygroup.com/en/news) - [Ita](https://newsroom.kireygroup.com/news/software-testing-come-cambia-nell-era-dell-ai?hsLang=it) - Eng - [Esp](https://newsroom.kireygroup.com/es/news/testing-de-software-como-cambia-en-la-era-de-la-ia?hsLang=es) [Kireygroup.com](https://www.kireygroup.com/) # Get your daily dose of tech! ## We Shape Your Knowledge # [Welfare: from compliance to competitiveness, digital services and technologies are key to pension funds’ future](https://newsroom.kireygroup.com/en/news/welfare-digital-services-and-technologies-are-key-to-pension-funds-future?hsLang=en) By Michela Sertorio, Business Line Manager - BU Pension Funds atKirey. 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What changes is the way software is validated and governed: AI-based applications introduce new complexities that push testing beyond simple functional verification, opening challenges that deserve careful analysis.  ## What changes in software testing for AI-based applications In traditional software, based on deterministic logic, testing relies on the assumption that system behavior is predictable and repeatable. Given the same input, the system must produce the same output, and the different dimensions of software quality (functionality, performance, security, UX, accessibility, and reliability) can be validated against clearly defined requirements and thresholds. Validation is therefore mainly binary: the behavior either complies with the requirements or it does not. AI-based applications do not overturn the principles of traditional software testing, but they expand its scope. Even when AI models are involved, many established activities remain valid, such as verifying interface behavior and identifying functional (bug) and integration defects. However, the software behavior is no longer fully predictable. In other words, system decisions depend on statistical models trained on data that may produce different results even with the same input. As a result, software testing must also evaluate whether the application’s behavior falls within acceptable thresholds consistent with its usage context: response accuracy, consistency between similar outputs, stability of behavior over time, and continuous alignment with business objectives. ## Why AI testing cannot follow rigid rules Testing AI-based applications cannot be rigidly codified because there is not just one type of AI nor a single purpose. Models exhibit varying levels of predictability, autonomy, and risk and therefore require **specific validation approaches**. From a technological perspective, two representative categories are machine learning and generative AI.  1. **Machine Learning** If the application uses machine learning algorithms (e.g., fraud detection systems), testing begins with evaluating established metrics such as precision, accuracy, and F1 score. However, it is also necessary to test the model’s ability to work with new data while avoiding *overfitting* — which occurs when the model performs well only on training data — and *underfitting,* when the model is too simple to capture the complexity of the problem. 2. **GenAI and agentic AI**   With generative and agentic AI, complexity increases because output validation cannot rely on a **direct comparison with a single expected and formalizable result.** Given the same input, the system may produce different responses, all potentially correct from an informational perspective but not necessarily equivalent in terms of quality, relevance, or contextual appropriateness. Automatic metrics exist to compare outputs with known references, but human involvement (HITL, Human-in-the-Loop) becomes central. When the system combines deterministic logic and probabilistic models within the same architecture (a typical phenomenon in agentic AI), testing becomes even more complex. It is necessary to verify both **the quality of the individual nodes of the agentic architecture and the interactions between them.**   ## Testing AI-based systems: a 5-phase methodology In the AI era, there is no validation method that works in all cases. Applications introduce variables related to data, context, and model behavior, making rigid testing frameworks ineffective. **The correct approach is therefore strategic**: it is necessary to understand what system you are dealing with, what decisions it makes, its level of autonomy, and the risks it introduces. These factors determine the choice of the most appropriate validation methodologies, which may also coexist within the same project. ### Data validation The first step is usually *data validation,* meaning the verification of the quality, consistency, and impartiality of datasets. Identifying statistical bias or anomalies in training data is the first defense against distorted or discriminatory outcomes in the future.   ### Model robustness verification Once data quality is ensured, attention shifts to model validation (model testing). In addition to fundamental metrics such as accuracy and precision, it is also essential to test robustness, meaning the model’s ability to provide reliable responses even when faced with degraded or noisy inputs. For example, so-called *invariance tests* verify that small irrelevant perturbations do not change model predictions, while other tests verify that the model has learned the correct relationships from past interactions. In a credit-scoring system, for instance, if all other factors remain the same, increasing income should improve the score; if this does not happen, something is wrong with the logic learned by the model. Alongside this, more advanced testing practices are used, such as **analyzing biases present in responses and red-teaming methodologies**, which adopt an adversarial approach to expose logical vulnerabilities and unexpected behaviors. ### System integration and security The system and integration testing phase verifies how the model integrates into the overall architecture of the application. This includes monitoring API latency and ensuring that model outputs are correctly interpreted by user interfaces. **Security also becomes crucial** at this stage: testing an AI application means protecting it from specific threats such as prompt injection or data poisoning. ### Human factor and dynamic validation The final frontier of testing is experiential validation, often defined as Human-in-the-Loop. Due to the non-deterministic nature of AI, human judgment remains essential to evaluate contextual relevance and the ethical aspects of responses. Finally, testing does not end with release: through continuous feedback cycles and real-time monitoring, **the system is constantly refined based on real-world usage**, transforming testing into a living and iterative process. ## Our approach: quality, testing, and AI for valuable solutions When supporting companies in their digital transformation journeys, we start from a clear premise: the value of innovation depends on the ability to generate tangible results, even when solutions are not governed by strictly deterministic logic. For this reason, alongside [software development](https://www.kireygroup.com/en/competencies/software-development) skills and **full application lifecycle management,** where testing plays a central role, we combine strong capabilities in **designing and delivering** [AI-based solutions](https://www.kireygroup.com/en/competencies/data-ai) oriented toward concrete use cases. In these projects, we integrate data science, software engineering, and application governance expertise to build solutions that deliver measurable and sustainable benefits over time. [Contact us](https://www.kireygroup.com/en/contact) to discover how we can support your journey toward becoming an increasingly data-centric company. - [Previous post](https://newsroom.kireygroup.com/en/news/giovanni-ciarlariello-is-the-new-ceo-of-kirey?hsLang=en) - [Read all posts](https://newsroom.kireygroup.com/en/news?hsLang=en) - [Next post](https://newsroom.kireygroup.com/en/news/data-strategy-a-practical-guide-to-aligning-data-business-and-it-architecture?hsLang=en) ## Related posts: #### [Legacy modernization in banking: discover the true...](https://newsroom.kireygroup.com/en/news/legacy-modernization-in-banking-discover-the-true-cost-of-legacy-systems?hsLang=en) The banking sector is one of the world’s largest investors in digital transformation and innovation,... 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