Testing of AI Systems Need Not Be Complicated

Testing is an extremely important aspect of software design. Without adequate testing, any implementation is likely to land you in a minefield of issues. A well-defined and thorough testing process is important to ensure reliability of the solution and to keep support and maintenance costs in check. Over the years, testing has emerged as an important part of the development process rather than being viewed as an afterthought.

In any standard software testing process, the Quality Assurance (QA) team’s job typically includes testing basic functionality, reviewing and analyzing the code, testing each unit and finally, testing for a single user.

It isn’t as simple when it comes to Artificial Intelligence (AI). Given that AI involves much greater complexity compared to regular software development, even the testing process becomes more challenging.

Therefore, organizations need to envision a different approach when it comes to testing their AI frameworks and systems to ensure that these meet the desired goals. For instance, QA departments must clearly define the test strategy by considering the various challenges and failure points across all stages.

We recently put out a paper titled ‘The Right Testing Strategy for AI Systems,’ which examines some key failure points in AI frameworks. It also outlines how these failures can be avoided using four main use cases that are critical to ensuring a well-functioning AI system.

AI frameworks typically follow five stages and each stage has specific failure points:

Data Sources and Quality

Since AI trains itself via multiple dynamic and static data sources, there can be several issues related to the quality of input data. The data could be incorrect or incomplete. Poor data quality or formatting issues can also pose a challenge. In case of dynamic data, its variety and velocity could induce errors.

Input Data Conditioning

AI systems typically draw data from big data stores and data lakes. If the rules for data loading are flawed or if there is data duplication, then it can cause errors. There could also be data node partition failure or truncated data or even data drops.

Machine Learning and Analytics

AI uses cognitive learning algorithms to enable machine learning and analytics. Success sometimes depends on how data is split for training and testing. Also, sometimes, if the data behaves differently as compared to previous data sets, it can throw up out-of-sample errors. Understanding the relationships between entities and tables can also be tricky.


Visualization is an important aspect of AI systems and generally lies on custom apps, connected devices, the web, and bots. Sometimes, incorrectly coded rules in custom applications can result in data issues. Formatting and data reconciliation issues between reports and the back-end can bring in errors. Communication failure in middleware systems/APIs can result in disconnected data communication and visualization.


In AI systems, feedback comes in from sensors, devices, apps, and systems. If the rules in custom applications are incorrectly coded, it can cause data issues. Incorrect predictions can also result from the propagation of false positives at the feedback stage.

Each of these failure points can be identified using the right testing technique. Some of the important testing use cases to be considered are testing of standalone cognitive features, AI platforms, ML-based analytical models, and AI powered solutions. Only a comprehensive testing strategy will help organizations streamline their AI frameworks and minimize failures, thereby improving output quality and accuracy.