Challenges in Adopting AI Driven Testing

Artificial Intelligence has taken the world by storm. With rapid advancements, AI is revolutionizing industries and business processes and reshaping our everyday lives.

In software testing too, the emergence of AI testing has changed the game and unlocked new ways to conduct and ensure thorough software testing and test case management.

What is AI?

Artificial Intelligence (AI) means training machines through various techniques and approaches to mimic human intelligence: performing tasks and making decisions like humans. There are various subfields in artificial intelligence, such as:

Machine Learning: Consists of algorithms that analyze large datasets and make decisions and predictions based on existing patterns. An example of this is personal recommendations where platforms like Netflix use machine learning models to recommend new shows based on past views and preferences.

Computer Vision: Analyzes images, videos, and other visual cues. Ever wondered how your phone can recognize your face and unlock itself? It is because of computer vision!

Natural Language Processing: NLP enables machines to understand human language and respond in the same manner. An example is virtual assistants who recognize voice commands and return results to user queries in the same language.

Robotics: Design and creation of intelligent robots that can replace humans and perform various tasks in their stead. For example, surgical robots are designed to perform accurate surgeries.

What is AI Driven Testing?

Now that we have a basic understanding of Artificial Intelligence, the question arises, what use is it in software testing?

AI driven testing refers to using Artificial Intelligence techniques and technologies to enhance the software testing process. AI testing can be incorporated throughout the various testing stages, along with test management tools for test case management.

Let us consider that you are testing a new software. How can AI testing be done here? Firstly, the AI will analyze the code and product requirements to automatically generate test cases. NLP can be employed to better understand the user and product requirements and turn them into test cases. Machine learning analyzes historical data, including past test results and common issues in similar software, to prioritize testing on critical areas prone to defects. To start your testing, the AI will generate relevant test data that you can use in your software testing process. Then, robotics and other AI driven test automation frameworks will be utilized to execute the test. Tests will be monitored continuously and in real-time, quickly identifying bugs and providing results. Furthermore, when the software is complete, AI can be used to stimulate real users for usability testing and also employ sentiment analysis to understand and analyze actual user feedback.

AI testing, combined with well-thought-out test case management and premier test management tools, can help take your QA processes to the next level.

Computer screen showing brain icon with interconnected links to represent AI and AI testing

Challenges in AI Testing

AI testing can enhance software testing processes and make them more efficient. So why are more organizations not adopting it? Well, this is because Artificial Intelligence and AI testing is still in its nascent stages. Many organizations are still struggling to develop and find talent and resources that can utilize AI efficiently, and the field of AI itself has vast areas of discovery and development that are yet to be explored. Hence, there are several challenges in implementing AI testing that should be kept in mind. Let us look at the challenges in AI testing in more detail.

Lack of Quality Data

Consider the act of cooking. To cook a delicious meal, you need high-quality ingredients. Without using good ingredients, you cannot produce good food. For Artificial Intelligence, the ingredients are the datasets. AI works by learning from existing data. This means that without quality data, AI cannot be used to drive good decisions. Here are some of the characteristics of good data:

  • • Should be representative and diverse to mitigate bias. Training and testing data should encompass a wide range of demographics and characteristics from various audience groups.

  • • Should be accurate and obtained from a reliable source.

  • • Should be current and up-to-date.

  • • Should be complete and comprehensive.

  • • Should be accurately labeled and sorted to help AI train better and make more accurate predictions.

Unfortunately, one of the major challenges in AI testing is to find data that meets this criterion. A sufficient amount of data is not always available, especially for testing unique or new software or scenarios.

Ethical Issues

The use of artificial intelligence can lead to various ethical issues. These may be related to privacy concerns, the need for ethical compliance, or the potential of AI to reinforce harmful biases. How do these ethical issues present challenges in AI testing? Let us see.

Firstly, data privacy and security are a major concern for organizations and individuals alike. When tied in with software testing, it is essential that testing data is kept secure. Such data might be confidential and contain personal information of users or clients. In this case, it is the responsibility of the organization to prevent the data from leaking and make sure no one has unauthorized access to it. If such data is being used in AI testing, proper controls, and security measures need to be in place to protect it.

Secondly, it is important to be aware that decisions made by AI are susceptible to bias. This is because AI makes decisions based on data. Availability and quality of data impact the decisions it makes. If past data is biased or incomplete, decisions made by AI will reflect that. Human intervention is required to correct such imbalances. AI cannot consider such ethical factors when making decisions, it just uses the information that it is provided.

For example, currently, the most widely used and easily available training data is in the English language. If the AI model used is trained primarily on English data, it might not sufficiently represent or cover the viewpoints and concerns of non-English speakers when designing test cases. Hence it is necessary to use high-quality training data that is diverse and representative of a wide demographic.

Thirdly, accountability and transparency are fundamental aspects of ethical decision-making. However, in AI driven testing, it is often difficult to break down and understand how the models work or make decisions. Without transparency, there is a lack of accountability, potential for bias as well as a general mistrust of AI driven work.

Lack of Trained Resources

Another one of the challenges in AI testing is the complexity of AI systems. The correct use of AI requires specialized skills. These include a firm knowledge of data structures, programming languages, mathematics, statistics, etc., as well as familiarity with AI domains such as NLP, computer vision, machine learning, and more.

Since AI testing is a relatively new field, not many people are proficient in its correct usage. There is a need to hire people with AI expertise and also upgrade the skills of existing resources through specialized training. Resources can be trained in effective test case management and the use of test management tools to reap the greatest benefits from QA activities.

At an organizational level, QA leads should conduct a requirements analysis to develop a long-term strategy for incorporating AI in the organization. AI experts are needed to devise the best strategy to adopt AI, research how AI testing can be incorporated into the current testing frameworks of the organization, and handle complex testing scenarios related to test planning, test generation, result analysis, etc..

Lack of Knowledge

Since AI testing is a new field, there is not a lot of existing knowledge or material to learn from. This is another one of the challenges in AI testing. There are no guidelines or standard procedures regarding the types of tools, frameworks, or approaches to use.

Furthermore, not every organization is proactive in implementing and using AI testing. This can either be caused by people’s resistance to change or because the organization simply does not have the physical and human resources to implement AI/ML systems. Introducing AI driven testing might require an upgrade of the current systems and procedures the company has in place. This requires planning, expert guidance, and investment of time and money.

However, it is important to note that organizations should not shift to AI testing just for the sake of it. They must conduct a thorough audit of their current QA processes to identify areas that can benefit from AI driven testing, and plan its implementation accordingly. Conducting informational sessions, webinars, and demonstrations can help onboard QA teams and demonstrate the importance and benefits of AI in enhancing QA activities. Integration with test management tools is crucial for effective test case management during AI testing.

Need for Human Element

Technology, at least in its current stages, cannot mimic or replace human cognition and understanding. Therefore, no matter how advanced current AI systems may be, they cannot entirely replace software testers.

For example, AI models learn based on data. Hence, they have limited room for creativity. To generate useful test cases, we need human exploratory testers who can incorporate the unique viewpoint of human users, especially when designing new software for which learning data might be limited.

Humans are also necessary to review the work produced by AI and give feedback so the model can continuously improve. Additionally, not all AI models are capable of unsupervised learning. They require human intervention to provide updates and relevant new data for learning. This may include updated legal requirements and standards to ensure that testing conventions cover the specific software’s compliance requirements.

AI models may struggle to understand cultural and linguistic differences due to limited diverse and unbiased data. Human intervention is necessary to address these challenges in AI testing. AI models should consider the specific cultural and regional context of the software under test, generate test cases accordingly, and conduct tests using diverse and representative data. Effective test case management and tools can help humans enhance AI driven testing.


Artificial Intelligence is an emerging field. There is a lot yet to be discovered and developed. However, the speed of progress in AI is significant, and the challenges in AI testing will likely be resolved soon.

For organizations to remain relevant and successful in this fast-paced world, they must be proactive in adopting new solutions and technology to make their processes more efficient and accurate. This is why QA teams must explore and adopt AI testing in their software testing methodology. In the long run, adopting AI testing, along with test management tools, can help teams improve accuracy, save time and become more cost-efficient in their testing processes.

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AI driven testing means utilizing Artificial Intelligence to enhance manual and automated software testing activities and processes. This can include planning tests, creating test cases, executing and monitoring tests, identifying bugs, analyzing test results, and generating reports.
AI consists of various subfields, including computer vision, machine learning, robotics, natural language processing, and more. All of these can play a part in supporting various stages of AI driven testing to enhance software testing efficiency.
AI testing poses several challenges. Trained resources are not available and there is a lack of expertise and knowledge of the field in QA teams. Many organizations lack the infrastructure and technical prowess to properly implement and utilize AI solutions. There is insufficient high-quality data to adequately train AI models, leading to ethical issues such as bias and discrimination. There is a concern regarding privacy, data security, and transparency in decision-making that needs to be addressed for people to trust AI.