With the onset of Artificial Intelligence (AI) in every possible domain of technology, software development is definitely leading the race. Though we knew that AI was a result of commendable coding by developers, today it’s vice-versa. Software developers are using AI to write and review codes, detect bugs, and test software efficiently. Companies can now deploy new software in the process of software development and execute their ideas with much ease and coordination.
The process of software development is highly organized with precise procedures and defined steps called the Software Development Life Cycle or SDLC. Follow this article to see how enabling Artificial Intelligence and Machine Learning in the SDLC process has helped developers save time, money, and a lot of resources for the companies.
Process of SDLC & Impact of Artificial Intelligence
- AI in Brainstorming & Planning
The process of creating great software always starts with a great idea. An idea that is carefully thought over, wild, and the most innovative. The best of the ideas are the ones that come from well-executed brainstorming sessions that allow a free flow thinking and ideation process. AI can help smoothen this process with electronic brainstorming. With the help of an intelligence moderator, individuals can brainstorm virtually by using various artificial intelligence functions, like natural language processing, machine learning, and reasoning.
2. AI in Requirements and Feasibility Analysis
After the idea is generated, the second step to analyze the feasibility of the idea where we gauge how economical the project is, what will be our technological, administrative, and human resource requirements, how much time will be required, and finally what would be the ROI rate towards the successful implementation of the idea. Requirement analysis, on the other hand, mitigates the risks involved in the process, where the project is plotted on paper with a detailed report of goals and objectives to bring every member of the team on the same page. It is in cases where companies make folly decisions in this process, that the projects overflow the pre-decided budget and deadlines. Hence deep expertise is needed along with context understanding to create accurate budgets for effective planning. Machine learning helps in this process to use the data from past projects like features, estimates, user stories, etc. for predicting the effort and the budget required.
3. AI in Designing
The third process in the software development cycle is designing. It is the visual modelling of everything in the project starting from the feasibility of the project to the software and hardware requirements in the future that require specialized learning and experience. Developers need to ensure that each process of designing is error-free, and goes with the original idea of their client’s requirements. Automating complex processes help resolve the issue of redundancy that is being done by AI. The AI design assistant is basically a virtual system that helps developers anticipate possible errors and take corrective measures today. It helps designers, developers, and product owners register key AI challenges including data rights and use, privacy, security, explainability, fairness, bias, and robustness in the designing process to make the software design foolproof and ready for development.
4. AI in Development & Coding
Development and coding are the backbones of the software development process. It involves writing codes and converting the designs and blueprints into actual software that meet the software requirement specifications and conforms to the stakeholders’ requirements. Now, this process of writing codes from scratch for every new project is time-consuming and can turn out to be a costly affair for a software development service provider. AI-based assistance reduces these loads to some extent by automating the code generation and detects the bugs from the code at an early stage. Take for instance a project where your idea in your natural language and your system can understand it by converting it into executable code.
During the crucial stages of software development, AI-enabled software helps developers to decide which tasks have to be prioritized by gaining experience from the past projects and assess the performance of the existing applications. This helps reduce efforts and saves time that’s usually wasted in performing redundant processes.
5. AI in Integration & Testing
In between the stages where the software is built and the one where we release the software to the company, there are several testing stages along with continuous integration that has to be executed to ensure product quality. The series of tests includes functionality testing, systems integration, and interoperability as well as user acceptance testing, etc. to ensure the code is clean and business goals of the solution are met ending with verification and validation. There is a wide range of software testing tools to employ AI for creating test-cases and performing regression testing. All of these AI tools help to automate the testing services to ensure error-free testing. For instance, Testim.io, Functionalize, and Appvance is a few of the artificial intelligence and machine learning-oriented testing platforms. Machine learning can help analyze bugs and errors in coding successfully. Hence it can be enabled with software to change the error response without any future intervention at all.
6. AI in Implementation & Deployment
This stage is the installation of the software to the client’s system. In the case of complex projects, the software is released in stages whereas in the case of a simple straightforward project the release is smooth with one step handover where analysts and end-users can actually start using the product. Now, there are times when a software defect pops out only when the users start to use it, i.e. at the stage of deployment which comes with a huge rectification cost. AI-powered tools today are helping to predict such deployment failure at the development stage by examining statistics from prior code releases and application logs. Machine-learning tools can analyze numerous potential application runtime settings and automatically deploys optimal environment configurations. This helped them halve cloud costs and more than double application performance.
7. AI in Operations & Maintenance
Last but definitely not the least part of software development is maintenance and regular updates. This is the most crucial stage in the entire SDLC because this stage defines how well a new software or an application will be accepted in the market and for how long will it stay relevant to its users. It is in this stage that the software is polished, upgraded, enhanced, and fine-tuned. You can refer to this stage as time-consuming and costly to the company because it involves a lot of redundant features. To identify these redundancies, a developer needs to correlate data with multiple sources. AI to rescue developers can automate the marking of unessential features and associated codes that saves time and energy.
The role of AI in software development is explained well in the above pointers. The world is picking pace every day and with it, the need to upgrade technology is becoming imperative for a software development company. AI is a blessing since it simplifies a variety of tasks to save time and money.