As much as Machine Learning and Artificial Intelligence have been a hot topic throughout 2019, so is the discussion of whether AI will have a large-scale impact on software testing. In this article, we will cover some of the main concerns when it comes to the way AI will affect the quality assurance process as a part of development.
Artificial Intelligence and Machine Learning
Big leaps have been taken in the last decades as far as AI and ML is concerned. As a part of a software development outreaching company that provides testing and quality assurance services, keeping tabs on all new testing tendencies is key. And the influence of AI and ML is becoming more and more prominent. Before we dig into that we should clear up what the differences between the two are and also why they are so great working together.
- Artificial Intelligence (AI) A system that collects data that is based on human behaviour. Such patterns may include speech, face recognition, voice and more. As a result, the system learns and evolves over time, becoming more accurate with time. Currently, AI can perform specialized activities that have been established based on the input data. AI is also far from being able to mimic and recreate human behaviour.
- Machine Learning (ML)
AI and ML have infused in today’s technology to both matches the demand yet also provide a more intuitive service. One example of this is, in fact, the search engines we use every day. The data collected, based on the principles of pattern recognition of AI, in addition to using ML to create a learned behaviour is what makes search engines so good at suggesting results that are so close to the ones you were needing. The combination of AI and ML makes it possible not only suggest the correct answer to you but also create a prediction on what you may be searching for based on your previous input.
And while it is very well implemented in search engines and social media algorithms it has much deeper applications than that. AI and ML can process and store complex and large amounts of data in additions to finding patterns in them. For example, it can analyze how much time it takes you to perform a task and help with the planning processes, based on previous information.
Machine learning and its three basic applications in testing
Easily, the biggest goals of Machine learning is to allow systems to learn without having to be explicitly programmed by an engineer. By utilizing predictive pattern analytics, the data use can make more “informed” prediction organically. For example, Deep learning – a method in ML – is based on neural networks in the human body. There are other ML algorithms, but the three main ones used widely in technology are:
- Supervised Learning – By input/output of data combinations for the algorithm to learn from.
- Unsupervised learning – Inputting vast amounts of data and analyzing
- Reinforced learning – Based on the idea of the reward function. Rewarding Good/Bad behaviour and developing the algorithm to learn from it.
There are many ways to nurture a Machine Learning algorithm to generate results, however, we will focus mainly on the ones used as a part of the quality testing and QA processes.
How AI will affect software testing mechanisms?
It has been established that the use of AI and ML will have an impact on quality testing, but how will it achieve that? What makes the use of this technology better than the “traditional” methods that have been used so far?
Faster manual testing
Manual testing has many repetitive processes that are being implemented and used. However, it is far from being foolproof. Testing based on the programmer to think to check something is obviously flawed, the risk to miss a bug is high, and so is the risk to take a really long time, to deem a product relatively bug-safe. And it is also a fairly expensive way as it is highly time-consuming and the developer needs to put in days, weeks, maybe even months to make sure the product is viable.
Using AI allows to write scripts and analyze vast amounts of data, while taking significantly less time, and has the potential to be unbiased about the returned results. This adds another layer of correctness when it comes to evaluating the possible outcomes. This gives the QA engineers a much more complete view of what they need to do to resolve the issues within the code.
Predicting functionality and user requirements
Easily one of the strongest features of ML is the ability to use the collected data and make predictions on what is needed and also, assisting to target the functionality and user functionality specifically.
AI algorithms are also used to analyze client feedback and the information provided by them in a way that allows making predictions on what would be demanded next. Essentially giving the company an edge against competitors by being able to focus their efforts on what is needed, and what would provide the most value to the users of the product.
This easily changes the direction of the development process and also helps create and test a product that is reliable, and up to date with what is needed on the market.
Process and testing reliability
Manual QA testing is not expected to be replaced but rather enhanced by the use of Artificial Intelligence and Machine Learning. While this will likely change the way that the process is carried out, such practices will make it much more reliable, bu minimizing the chance for a human error. In addition to getting rid of the occasion where it is necessary to go over the same parts of the code multiple times. Once it has been confirmed that a certain part of it is clear, it is no longer a subject of testing, unless the QA decides to change it due to compatibility issues with the other features.
Programmers vs AI testing
Whether using AI in software testing will be the next big thing or not, and also whether it is currently reliable enough to replace manual programming, is very much up for debate. Some think that the Artificial Intelligence age is upon us and before long it will replace manual developers in certain processes in development – like testing. Others think it is not ready yet to take over and will inevitably miss a lot that an experienced developer would not.
And not to mention that there is always the point to be made that testing using AI should be used to assist the testers, rather than replace them over time.
Where do you stand on whether Artificial Intelligence testing is a viable option to perform those actions in 2020? How much of an impact do you think the introduction of ML and AI will have on a large scale? Make sure to comment below!