How Generative AI is Transforming Application Modernization?
Introduction
According to the RedHat survey, 95% of respondents consider application modernization as a critical priority for their organization’s success. Most of the forward-thinking organizations are already reaping the benefits of scalability, improved customer experience, and competitive advantage.
However, with an increasingly complex IT landscape, high rising costs, limited skills, and execution complexities, there is a pressing need to adopt different approach and technology altogether to drive innovation. This is where Generative AI steps in, serving as a catalyst for enhancing efficiency, precision, and speed of the application modernization.
This blog delves into how the Generative AI revolutionizes the application modernization across the entire SDLC, offering a holistic and agile transformation approach, making application development more scalable and efficient than ever before.
Generative AI: A Contemporary Catalyst for Application Modernization
“According to Avasant research, enterprises and service providers are scouting ways to leverage generative AI in application modernization.”
From automating code generation to successfully providing intelligent design suggestions, Generative AI holds immense potential to revolutionize application modernization across industries and unlock new possibilities for growth and innovation. It significantly speeds up modernization, improves code quality, and enhances user experience, enabling organizations with continuous improvement capabilities. This transformative technology bridges the gap between conceptualization and creation, positioning itself as an essential tool in modern software development.
Code Refactoring
Modernizing legacy applications presents significant challenges due to their complex and outdated code structures. Manually rewriting these codes can be tedious, time-consuming, and prone to errors, often leading to frustration and delays in project timelines.
Generative AI has come to the rescue by analyzing and understanding existing codebases, offering a comprehensive assessment of current applications. Leveraging advanced machine algorithms helps generate refactored and optimized code automatically that aligns with our project requirements and coding standards, significantly improving the application performance, readability, and maintainability. As a result, businesses can modernize their applications without the need to rewrite entire codebases, significantly saving time and reducing manual intervention.
Cross-platform Compatibility
Considering the intricacies of cross-platform compatibility, GenAI can generate code that seamlessly adapt to diverse environments such as mobile, web, or desktop applications, reducing the manual intervention. It further analyses the code and suggests platform-specific adjustments, optimizing the UI elements that bridges compatibility gaps, ensuring a smooth cross-platform experience.
Integrating Microservice Design Assistance
The transition from monolithic to microservices architecture represents a significant paradigm shift, presenting challenges such as defining optimal service boundaries and ensuring effective inter-service communication across the network. Generative AI plays a crucial role in this transformation by facilitating the decomposition of monolithic applications into microservices.
It analyzes dependencies and usage patterns to identify potential issues, recommend optimal service boundaries, and establish effective communication protocols. This ensures a smooth migration to a microservices architecture, enabling organizations to harness the benefits of scalability, agility, and resilience while minimizing disruption during the transition.
Role of Generative AI in Software Development Life Cycle
Application modernization is at the forefront of enterprises’ pursuit of investment across industries. This process involves several steps, each with a specific set of deliverables that help successfully modernize legacy applications.
“79% of responding executives say using generative AI in app modernization projects will increase business agility.”
Let us explore at how GenAI is accelerating the development of each phase of SDLC.
Discovery
Generative AI significantly reduces the reliance on extensive SMEs. It excels in use cases such as code analysis, extracting insights from outdated documents, and correlating domain functionalities with code and data. This facilitates effective decomposition, event-storming, and domain-driven design. By providing teams with enhanced visibility into legacy applications and their dependencies, generative AI mitigates risks and boosts the efficiency of the discovery and design phases.
Planning and Requirements Gathering
User Story Generation: GenAI, powered by LLMs, can craft well-defined user stories that accurately reflect user expectations. GenAI can suggest potential functionalities and roadmaps by analyzing the project scope, complexities, historical data, and dependency patterns. Generative AI is a valuable tool for correlating application portfolio details with discovered dependencies, making planning exercises more informed and adaptable.
Defining Acceptance Criteria: Acceptance criteria play a pivotal role in Agile methodologies, defining the conditions under which a user story is considered complete. Generative AI can help generate accurate and measurable acceptance criteria based on the user stories and planned features on the roadmap. By analyzing similar completed projects and industry standards, it can suggest acceptance criteria, ensuring that the functionality meets user expectations.
Requirements Analysis with Gen AI: Gen AI can analyze your requirements to identify inconsistencies, missing information, and potential risks. It facilitates discussions and collaborative reviews within your team by providing a platform for discussing and refining acceptance criteria. It can aggregate feedback from various stakeholders, ensuring that the criteria aligned with user requirements, ensuring a solid foundation for the development process.
By analyzing requirements and acceptance criteria, Generative AI can highlight potential risks and ambiguities that may affect the development process. This proactive approach allows teams to address issues early, reducing the likelihood of costly changes later in the project.
Design and Development
Modernizing UI Design
Generative AI can automatically generate new UI elements, mock-ups, styles, and layouts, prototypes, and frameworks based on user personas and modern design practices, making UI modernization user-optimized. It can build UI code, including the back-end for front-end (BFF) patterns, by analyzing images to generate code compatible with UI design. It alleviates the burden on designers and ensures a more intuitive interface for users.
Intelligent Code Generation
According to Mckinsey Study, “Generative AI allows developers to complete complex tasks up to two times faster.”
Beyond refactoring, generative AI can generate new code snippets with the natural language inputs by analyzing learning patterns from existing code. This intelligent code generation seamlessly integrates into existing infrastructure, enhances code quality and functionality, automates the creation of boilerplate code, unit tests, and API documentation, enabling developers to dedicate their expertise on complex tasks.
Additionally, GenAI can generate related artifacts such as security design points, pipeline code/configurations, and Infrastructure as Code (IAC) based on insights from legacy code or Swagger definitions. It orchestrates these outputs using predefined application reference architectures, ensuring consistency and efficiency.
Testing and Deployment
Test Case Generation
Debugging legacy codes often feels like finding a needle in a haystack due to a lack of understanding of the code’s purpose and potential hidden dependencies.
Imagine an intelligent assistant can leverage Gen AI can automatically generate comprehensive test cases and codes by analyzing code and requirements, significantly improving the quality while reducing the risk of missed bugs from the testing phase, ensuring applications remain robust and reliable throughout the SDLC.
Maintenance & Support
Unlike traditional debugging tools that rely on solely known patterns and manual inputs, Generative AI can continuously monitor the performance of deployed applications by analyzing system behavior, logs, and code to identify anomalies and suggest possible fixes, enabling testers to predict and promptly resolve issues before they escalate, ensuring smooth and secure deployments. It can also assist in reactive approaches like automated bug fixing and root cause analysis. The ability to generate inputs for configuration management and change management streamlines release processes. By drawing insights from release notes, generative AI enhances the efficiency of security review and approval processes.
Human-AI Collaboration is Key: While GenAI offers tangible benefits across SDLC, it is not a replacement for human expertise where it can only augment developers’ capabilities. Developers still need to review and refine the generated code and outputs aligned with project requirements and maintain code quality.
Potential Benefits of Generative AI for Application Modernization
As generative AI continues to advance, organizations that leverage its capabilities early will experience more significant benefits and drive exceptional growth. Generative AI is not merely a tool but a key catalyst for enhancing greater efficiencies and fostering innovations across various sectors.
Let’s explore the potential benefits of GenAI in application modernization:
Enhanced Efficiency and Productivity: McKinsey’s study reveals that “Developers using Generative AI to perform complex tasks were 25 to 30% more likely to complete those tasks within the time frame”.
GenAI automates core repetitive tasks, enabling developers to focus on complex problem-solving and strategic tasks, leading to faster development cycles.
Reduced costs and time-to-market: Generative AI automates various tasks, streamlining the development process and significantly reducing time and associated costs. This allows for faster time to market and quicker return on investment.
Dynamic Scalability Optimization: Generative AI helps optimize code for dynamic scaling by analyzing application workloads and performance data, ensuring effective resource utilization and performance of underlying architecture.
Delivering Excellence: Improved Product Quality: Generative AI reduces the likelihood of errors by generating high-quality code and identifying potential missing features and issues early on, resulting in delivering more efficient and reliable product.
Security Enhancement: GenAI can recommend secure coding practices by detecting vulnerabilities in legacy code, eventually preventing data breaches and significantly boosting application security.
Automated Documentation Maintenance: GenAI streamlines documentation and unlock new level of efficiency and agility in SDLC. It ensures the documentation is updated in real-time as the code changes throughout modernization, making the documentation correct and aligned with the evolving codebase.
Bottomline
Generative AI ushers in a new era of application modernization. While Generative AI’s role in software development is still an evolving field, the potential is enormous, unlocking innovation rather than merely addressing outdated challenges. Generative AI serves as a force multiplier, empowering teams to tackle monolithic systems, seamlessly migrate to cloud environments, and breathe new life into legacy applications, making them more agile, scalable, and efficient.
With more than two decades of experience in product engineering and digital experience services, we leverage various Gen AI-driven modernization accelerators to expedite the different phases of SDLC. This blog merely skims the surface of the possibilities that GenAI offers for application modernization. To learn more about how we can modernize your applications with GenAI, connect with us!
