Testing Applications with Machine Learning
Software development lifecycles go through a set of common steps that range from the process of ideation to the final deployment of the product or service. The processes that are undertaken in the software development lifecycle help systems automatically regulate and enhance user experiences without manually programming them. It is important to make sure that quality assurance (QA) processes are taken forward so that the program behaves as expected and the features of the product are made sure.
Software testing helps to pinpoint defects and flaws throughout the development process, and in the context of using machine learning application testing, the programmer usually inputs the data and the logic is further computed by the machine.
It might be interesting to read about 4 Common Machine Learning Mistakes And How To Fix Them!
Let’s see what the different kinds of machine learning are, and how to further polish machine learning testing processes:
Types of Machine Learning
There are three main categories that machine learning falls under:
The first step of supervised learning is to determine the type of training dataset. After this, the training data is collected and split into datasets – test datasets, and validation datasets. The next step includes determining the input features for the training dataset, and this needs to have ample information so that the model can correctly predict the output. A suitable algorithm is picked and executed on the dataset where there is a necessity to sometimes keep validation sets as the parameters of control. The very last step includes matching the model to the test set. If the model predicts the right output, it means that the model is accurate.
There are two algorithms used in supervised learning – regression algorithms and classification algorithms. The former deals with the prediction of continuously occurring variables such as weather forecasts, market/stock trends, etc. The latter kind of classification algorithm is used when the output variable is categorical in nature. Some examples of where classification algorithms can be used are in yes-no, male-female, and true-false scenarios.
Supervised learning can therefore help the model predict the output on the basis of previous experiences. The developer has an exact idea about the classes of objects that are available to work with. It helps with solving various real-world issues such as spam filtering and fraud detection.
In this kind of machine learning, the model is not supervised using training datasets, rather the model automatically finds hidden insights and patterns from the ingested data. This process happens by cluttering image datasets together and understanding their similarities in groups. It is helpful to find insights from any given data as it is very similar to how the human brain operates- because in the real world, we do not always have the input data to correspond with the output.
There are two methods of unsupervised machine learning that are worth noting: clustering and association. Clustering is a process wherein similar objects are sorted into clusters. Cluster analysis helps in determining the similarities between objects of data and labels them according to how common they are in the datasets.
Some use cases where unsupervised machine learning can be put to use include data exploration, targeted marketing campaigns, data visualization, etc.
This class of machine learning makes it so that the machine learns to make a succession of decisions in uncertain and complex data environments. The algorithm basically uses a trial-and-error method. Along with a reward system attached to it, the AI either gets rewards or penalties according to how the algorithm performs.
One of the main challenges with reinforcement learning is setting up the right simulation environment. Scaling or tweaking the neural network that controls the whole AI also presents itself as a challenge.
The model figures out how to perform tasks and maximize the reward potential, starting with completely randomized trials, leading up to more advanced strategies and sophisticated skills.
A few examples where reinforcement learning can come into play include autonomous vehicle technologies and advanced robotics. Here, the AI is required to learn how to recognize walking patterns and imitate the same.
Best Practices while Testing Machine Learning Applications
Artificial Intelligence-based solutions use data as code. To have a well-oiled system, it needs to be made certain that the input data are accurate and optimal to achieve any solution. When creating an AI-based solution, there are a few things to keep in mind:
Clocking Test Findings: Machine learning algorithms validate data based on range-based precision, rather than what the expected outcome can be. For this reason, the test results need to be recorded and put across in statistical terms. For each new development, there needs to be a new set of confidence criteria to make sure that the inputs in the given range are used to analyse.
Constructing Data Sets: Test data sets must be carefully curated so as to test all possible combinations and permutations in order to evaluate the effectiveness of the model. As the number of inputs and iterations scale-up and the richness of the data increases, the module must also be constantly polished throughout the training process.
Building System Validation Test Suites: These test suites are made with the help of algorithms and the test data sets that are used. An example can be that of a test scenario set to predict the outcomes of patients based on diagnostic data and pathology. There needs to be certain mitigative profiling done for the patient that includes the disease under question, the patient’s demography, and other relevant patient information from the past.
Creating Comprehensive and Semi-Automated Training Data Sets: These kinds of training data sets include a combination of both input data and the required output. Static data dependency analysis is required to be taken forward in order to interpret both data sources and data features. This is an integral part of the migration and deletion of data sets.
Smarter than AI
A number of critical aspects come into play while testing AI systems. These include various processes that include but are not restricted to data curation & validation, algorithm testing, performance, and security testing & smart interaction testing. AI systems are usually created to work in conjunction with other systems to tackle very specific challenges. Many factors must be thoroughly considered when testing AI/ML models.
Businesses that use AI on an everyday basis are building systems and applications that lead to newer testing methods and approaches. The procedures are only set to evolve in the coming years, as traditional testing methods are slowly taking a backseat.