AI in software testing

"Artificial Intelligence" is the latest buzzword that has captured the attention of people working in many diverse fields in the past year. While the current wave of AI can be credited to the popularity of ChatGPT, the concepts and methods of artificial intelligence have existed long in the software industry in both development and testing areas. A popular example of this is self-healing test automation tools that can detect UI changes automatically and make appropriate changes in the test cases. AI is today implemented in almost all engineering fields and beyond that to help engineers, users, and make important business decisions for the future.

The software industry is no exception in this regard and this is the area we are most concerned with in this post. AI in software testing and development not only provides data-related outcomes but shares the responsibilities of a tester and a developer to accomplish a part of their tasks. This helps in early software release and quicker feedback cycles. However, discussing this area would mean jumping ahead and missing critical information about AI, its evolution, and its association with software testing.

What is artificial intelligence (AI) - A brief introduction

The most simple definition of AI is possessing an intelligence but artificial in nature i.e. not natural. By natural intelligence, we refer to the intelligence developed by humans and animals on their own with their evolution. We incorporate this intelligence into a machine and depending on the type of AI involved, the machine can provide outputs based on inputs just like a human would but in a highly restricted way (at least currently).

The field of artificial intelligence has been in development since the time of Alan Turing and as you might assume, it started with extremely basic actions. For instance, providing output for checkers and speaking the written sentence in English. For almost fifty years, the focus on artificial intelligence remained in mathematics until in 2010 it shifted towards creating intelligent machines (which was the initial aim when AI was created).A farmer monitoring his crop using AI on a mobile device

In recent years, artificial intelligence can be seen applied in a very vast variety of domains. A few of the examples are as follows:

• Crop monitoring - Agriculture field.
• Hospital staff management and environment controlling - Healthcare field.
• Risk identification and mitigation - Construction and Civil Engineering field.
• Content curation and Personalised learning experience - Education field.
• Fraud detection and prevention - Finance (Banking) field.

With these wide applications, AI seems to have seeped into every corner and making its mark. In the list above, we have left applications in the software industry intentionally as from the next section onwards, that will solely be our point of focus.

AI in the software industry

The most powerful impact AI had in any industry is the software industry. Not only in the area of end-applications (which is definitely a big chunk) but also in adopting it in their own software. The most recent example is Microsoft's Edge browser integrating ChatGPT directly into their search engine to provide search results in a conversing format. When we talk about AI in terms of software, we combine multiple technologies under a single umbrella-Machine learning and deep learning are not new terms either but if we are applying any of the two in our application, we are essentially dealing with AI in software development. Today, the software industry has an outreach of a wide range of domains that include mobile applications, web applications, IoT devices, Unix-based machine devices, and a lot more. It is surprising to see that AI has been able to penetrate all of these domains so easily in a very short period of time. Filtering out the most used areas (from an end user's perspective), you can witness the power of artificial intelligence in any of the following applications with just a Google search:

• Chatbots: A bot conversing with the user, taking inputs from them, and providing necessary outputs is a very common feature on web applications today. The usage and work of the chatbot depend on the type of application in which it is installed. Some chatbots may be planted just to provide information while some can register requests, raise queries and resolve issues without ever concerning the support team.
• Predictions: There are numerous applications appearing in the market that works on the foundation of "prediction". The type of prediction depends on the nature of the application. For example, stock market prediction applications can predict a stock price in the near future depending on past performance.
• Suggestions and Recommendations: Suggesting what could be the most "probable" choice for the user and recommending them on their profile based on their inputs (interactions or past purchases, etc) is a common element of websites especially those working in eCommerce and online streaming department. This is fairly visible while shopping on Amazon or scrolling around on Netflix (the most popular example).
• Voice assistants: Speech recognition and generating output based on speech is another popular application of artificial intelligence commonly used today. In this application, the user can provide audio inputs and interact with the voice assistant using speech. A popular example is Alexa which can converse as well as perform various tasks all by analyzing what the user is saying.
• Natural Language Processing: Detecting the input language, understanding grammar, and replying based on text is done using AI. Currently, NLP-based algorithms have moved a step ahead and can recognize two mixed languages, handwriting, and Romanized forms of languages as well.

Apart from these common applications of AI in software, there are numerous other examples and the list is increasing every day. Why do we spend so much time and effort to implement algorithms that can detect, analyze, respond, and whatnot! It's definitely user-friendly but does it bring any benefits that lure engineers and business owners to adopt it in their model as well?

Benefits of having an AI-powered software development cycle

Setting aside the applications of AI in software, there are numerous benefits that help the actual software development process and the business as a complete unit:

• Data Analysis: AI applications are extremely efficient in gathering data for the organization. Getting a recommendation automatically would help the algorithm explore your interests more clearly than you as a user searching for that product (which in a lot of cases you would not have thought to order!). This data can be gathered automatically and analyzed for making future decisions.
• Takeover development activities: AI can be implemented into actual development activities to fix minor issues without the engineers. This can also be done using predictive algorithms to predict any bugs before merging the code to the main base.
• Cost saver: AI in the applications saves a lot of costs as the business needs minimum hiring as possible. The work of hundreds of support engineers can be done by AI applications in minimum time.
• Helps in business growth: AI algorithms can predict based on past and current trends of the application and user behavior towards it. With this data, we can make future decisions, create new strategies and implement them to grow the business.
• Software testing enhancements: Apart from development, software testing has seen an equal implementation of AI into their process trying to make the tester's work easier and eliminating scripted programming for the most part. Software testing can now be done by manual testers and sometimes even business analysts as AI can take care of most of the work.

In the complete software development lifecycle, software testing has made marvelous progress toward implementing AI for the benefit of testers in a new way to shape the complete testing phase from a new start. This has helped change the priorities of an organization for the betterment and optimize their testing phase to reduce stress and effort than before.