Role of Generative AI in the Modernization of Software Development Process

In recent years, software engineering has witnessed a significant shift towards enhanced automation and simplification of the development process. There is a lot of buzz around the adoption of Generative AI as a strategic enabler in modernization initiatives. The Natural Language Processing enables machines to understand user requirements and automatically deliver high-quality software models. 

Integrating distinctive capabilities of AI can aid developers in each stage of the Software Development Lifecycle (SDLC) – from business requirements analysis and creating agile user stories to software design, coding, testing, deployment, monitoring and maintenance. Here’s where organizations can optimize with the use of Generative AI. 

Efficient SDLC Prototyping and Planning

In the conceptual phase of Planning, ‘Requirements Management’ in SDLC involves maximum human intervention in order to align development with the vision. AI algorithms can analyze large amounts of data such as customer reviews, market research and industry best practices to identify patterns in user needs and preferences. AI tools better equip project teams to interpret client requirements, enabling faster development of new software prototypes and feedback gathering in the early development process. Predictive analytics adds another layer of advantage with the ability to forecast cost, time and effort.

Accelerate Code Creation and Review

Generative AI tools can be used by developers to outline and draft codes based on context via input code or natural language. Tools like GitHub Copilot can automatically generate accurate codes at a faster pace with reduced friction while enabling automatic translations. AWS CodeWhisperer can have impact across many routine developer tasks, including refactoring existing code. Most recently, there have also been niche tools designed, such as Deep Code, to identify potential flaws in the code.  

Streamline User Story Creation and Automated Test Case Generation 

Writing user stories can be a time-consuming and tedious process. With Generative AI, software teams can rapidly create a set of baseline requirements to cover epics, user stories and tasks for engineers to follow. It autonomically generates tests that reflect end users’ behavior as part of software testing. TestRigor is an executable specification engine with the ability to build, maintain and understand tests created and executed in English language. Additionally, AI systems can report the test results, reducing time and manual efforts. Tools such as AutonomIQ and AcceIQ provide AI assistance to streamline testing. 

Automate Regression Testing and Threat Identification

Generative AI can be used in quality assurance to automate regression testing, which involves testing changes made to the software and identifying new bugs or issues to help prevent downtime. By simulating user interactions with the software, Generative AI can highlight potential gaps in user interface or user experience. PentestGPT is a penetration testing tool that automate the penetration testing process, operating in an interactive mode.

Optimize Software Deployment and Reliability 

As part of the workflow or process automation, post testing and de-bugging, the application software developed can be deployed using Generative AI. The Generative AI tools can optimize workload placement to maximize resource utilization, minimize response time and improve overall system efficiency by analyzing system performance in real time. It can be used to create knowledge documentation for reference should there be a case of service disruptions. 

In an IT support scenario, there are several use cases that can augment the teams’ capability for a more reliant system and user experience. The prominent ones are self-help features for business, automated ticketing, ticket routing and resolution of routine tickets, assistant for support engineer, and multilingual support.  

As Generative AI evolves and seamlessly integrates within tools across SDLC, it is expected to further accelerate quality of delivery and drastically improve productivity. But implementing Generative AI and managing the economies of scale will require a responsible-first approach, which ensures uncompromising ethics, trust, privacy, security, and compliance, while amplifying the potential of humans, enterprises, and communities when tapping into the next generation of opportunities and creating value from unparalleled innovations, connected ecosystems, and pervasive efficiencies. 

The article has been written by Suresh HP, Chief Delivery Officer, Sonata Software