While a number of enterprises are at the cusp of digital transformation a key setback that businesses still face is that most of the processes are repetitive, manual and error prone. To tackle this, businesses across various industries are adopting emerging technology such as AI to make their processes smarter, automated and efficient. By leveraging data to make machines simulate human behavior, AI solutions can optimize tasks by automating and optimizing them, thus helping businesses save on key resources of time and money.
A key area where AI can be utilized is in the software development process. Incorporating AI techniques in software development can help IT enterprises implement robust, accurate, and intelligent software applications with greater efficiency and reduced human intervention.
Let's discuss and explore different ways of incorporating artificial intelligence techniques in the software engineering process:
Software Engineering Processes and Use of AI Techniques
There are multiple software design and development methodologies and each one has its unique way of developing software applications. It started with the waterfall model and then progressed to an iterative way of designing and now Agile has taken control of it. Though there are various methodologies of designing the software, it always involves the core stages of requirement gathering, design, development, and testing.
Let's see how we can incorporate artificial intelligence in the core software development stages:
Requirement understanding is the first core stage in software development, and it must happen at the beginning of software development. It is the process of understanding and defining what services are required and identifying the constraints on these services. It assures the prospective users that the software will meet their expectations.
Requirement gathering is the conceptual stage of software development and it requires maximum human intervention since there are a lot of back and forth tasks that need to be achieved to meet design expectations. Many of these manual tasks can be automated and accelerated using various techniques available, including artificial intelligence. For example, the AI technique of natural language processing can be used to make the machine understand user requirements.
A common example is the use of chatbots and virtual assistants to interact with customers and gather necessary information before connecting them to the customer care executive. This has helped customer care companies to provide a more personalized and engaging experience to the customers while eliminating human interference to a degree.
2. DesignDesign is an important code stage in software development. Everything needs to be accurate in the design stage to accomplish user requirements. Providing an accurate design in every substage is a challenging and complex job for designers, since deciding the correct plan for each stage can be tedious, and prone to errors. Incorporating artificial intelligence techniques in this phase can help designers, simplify their job while delivering the exact design expected by the clients. The use of some AI-enabled design assistance tools can help the designer accelerate the design process with minimum rework in plans. For example, design assistance tools can create multiple options for one type of requirement by understanding the requirements from the user and thus reduce design creation time for the designer.
3. Develop
Development is a key stage in SDLC in which the actual implementation of the user's requirements happens. This is the longest duration stage in software development, wherein software developers devise and write intelligent programs to drive the software. The work in this stage is tedious, time-consuming, and labor-intensive. To overcome this, people tried the early development solution, i.e., starting the development work before the previous two stages get completed but this idea didn’t work. Another approach in place is, keeping commonly used modules ready for use and using them directly to accelerate work.
Currently, research is in progress to automate the coding process using natural language processing, such that the machine understands our requirements and converts it to executable code. However, this is still at a very nascent stage and will take some time to be achieved.
Testing is the final stage of SDLC wherein all the client requirements are validated with developed software. Testing is a very important stage and only with the approval of the testing team can the software be delivered to the client for usage.
Retesting the application each time there is a change in the underlying code can be a very repetitive, manual, and time-consuming process.
By using some automation techniques it’s possible to reduce the repetitive testing time while also improving testing capabilities. For example, AI-driven tools help collect data during each stage of the test and the same data can be used for multiple testing scenarios to get quality testing in a short time. There are multiple AI tools like (Applitools, Appvance, and Functionize) available in the market which can be used for automating the testing process.
Conclusion
Adopting AI in different stages of SDLC will improve and accelerate the software development process along with generating quality software applications. With technology advancing at a rapid pace, several new AI techniques will be discovered in the coming days and IT enterprises stand to benefit by adopting these in their software engineering process.