How AI is used in the Software Development Life Cycle (SDLC)

AI-generated code might save you time, but it’s only scratching the surface of the software development process. Stick around, because understanding the bigger picture separates good developers from great ones.

When searching for ways to streamline software creation, you probably ask yourself: How much can AI help in the Software Development Life Cycle (SDLC)? Can it replace human input, or is it just a supplementary tool? What are the hidden challenges of relying too much on AI for coding? As Steve Jobs once said, “Technology alone is not enough.”

The problem with AI in code generation is that while it automates certain tasks, it overlooks the complexity of real-world software development. According to recent studies, including the IEEE and ACM research, the true bottlenecks in software development lie in planning, architecture, integration, and ongoing maintenance – areas where AI still needs to catch up.

Why trust this article? With over 15 years of experience in software engineering, I have led projects that range from enterprise-level applications to innovative AI-driven solutions. With a proven track record of delivering successful software and staying deeply involved in the latest industry trends, the insights I share here come directly from my hands-on experience.

By reading this article, you’ll understand the critical gaps in AI-driven code generation and how to address them effectively. You’ll learn how to leverage AI as a powerful tool while recognizing its limitations, ensuring that you’re prepared for every stage of the SDLC.

Tools can write code. Is that software creation?

With the advent of AI-driven tools capable of generating code from plain language prompts, it’s easy to see why there’s excitement in the tech community. These tools can quickly produce HTML, React components, and other code snippets, offering a glimpse into a future where parts of software development might be automated. However, while this innovation is notable, it represents only a fraction of what software creation truly involves.

Code generation is just one step in the Software Development Life Cycle (SDLC), and in the grand scheme of things, it accounts for only a small percentage of the overall process – approximately 5%. The SDLC is a complex sequence of stages, including planning, design, testing, integration, deployment, and ongoing maintenance. These stages require not only technical skills but also strategic thinking, problem-solving, and continuous collaboration – areas where AI, at least in its current form, falls short.

The tools available today can generate code, but they still need to possess the capability to integrate seamlessly into the full spectrum of SDLC activities. The gaps are significant, and the challenges of connecting these tools to the broader development process remain unresolved. Thus, while AI tools are impressive in their ability to produce code, true software creation encompasses much more than that. It requires a holistic approach combining automated and human-driven processes to deliver a fully functional, maintainable, and scalable software product.

Software Development Lifecycle (SDLC) Overview

When it comes to developing software, there’s a reason professionals rely on a standardized process known as the Software Development Lifecycle (SDLC). This framework has been honed over decades, ensuring that software is developed in a systematic, efficient, and reliable manner. Suppose you’re familiar with the construction industry. In that case, you’ll find that software development is quite similar – building software is like constructing a house, where each phase of the project must be carefully planned and executed to ensure a solid final product.

The SDLC is divided into several key phases:

1. Analysis. Understand the problem or challenge the software needs to address. This involves gathering requirements and creating detailed specifications to guide the development process.
2. Design and Prototyping. Lay out the architecture of the software and create initial prototypes to visualize how the software will function. This phase is crucial for identifying potential issues before development begins.
3. Development. Write the actual code, creating a complex system of interconnected components that make up the software. This is where the bulk of the technical work occurs.
4. Testing. Rigorously evaluate the software to ensure it functions as intended and is free of critical bugs. This phase provides the quality and reliability of the software.
5. Deployment. Make the software available to end-users. This phase involves setting up the environment where the software will operate and ensuring it’s ready for use.
6. Maintenance. Perform ongoing updates and improvements to keep the software functioning smoothly over time. This phase addresses any issues that arise post-deployment and adapts the software to changing needs.

Understanding and following the SDLC is crucial for creating high-quality software. Just like in construction, skipping steps can lead to structural failures – in software, this can result in unreliable, difficult-to-maintain, or unfit-for-purpose products.

AI’s Role in Each SDLC Phase

As AI continues to evolve, its influence on the Software Development Lifecycle (SDLC) is becoming more evident. While we often hear about AI’s capabilities in generating code, its potential impact extends to various phases of the SDLC. However, this impact is uneven—AI is highly effective in some areas but still limited or emerging in others. This section provides an overview of AI’s current and potential roles across each phase of the SDLC, offering insights into where AI stands today and what might be expected in the future.

Requirements Analysis

AI’s role in the requirements analysis phase is still emerging. While there aren’t many widely used tools specifically for automating the gathering of requirements, AI, like GPT, has the potential to assist by drafting initial specifications based on conversations or documents. Think of it as having a smart assistant that helps organize and clarify your thoughts into a coherent specification. However, such tools are not yet mainstream. The primary outcome of this stage is the Requirements Specification Document, which serves as a blueprint for the next phase. In Flatlogic, we started to generate basic software specifications from user conversations, covering standard components like Business Requirements, Functional Requirements, Non-functional Requirements, Use Cases/User Stories, and Stakeholder Requirements.

Design and Prototyping

AI’s involvement in the design and prototyping phase is limited but shows promise. Some tools help with basic layout design or graphics, but fully automated prototype generation from specifications isn’t yet mainstream. AI has the potential to make significant contributions in this phase, but it hasn’t fully taken on that role yet. The deliverables from this phase typically include several key documents and artifacts:

• System Architecture Document: Outlines the overall structure of the system, including major components, modules, interfaces, and data flow.
• Prototypes or Wireframes: Visual representations of the user interface and user experience, ranging from static images to functional prototypes.
• Database Design Document: Details the database schema, including tables, relationships, and constraints.
• Technical Specifications: Descriptions of the technologies, platforms, and tools to be used for implementation.

AI’s role in design and prototyping is rapidly evolving, with tools like Figma integrating AI to streamline layout generation, suggest design variations, and optimize component placements, allowing designers to focus on more complex decisions. Other tools, such as Uizard and Sketch2Code, can even convert hand-drawn sketches into functional UI code, pushing the boundaries of AI’s capabilities. While AI assists in creating wireframes, refining layouts, and automating repetitive tasks, it is not yet capable of handling fully realized, complex prototypes without human oversight. 

Development

In the development phase, AI tools like GitHub Copilot and OpenAI’s Codex assist by generating code snippets, automating repetitive coding tasks, and even suggesting solutions for common programming problems. These tools are particularly useful for routine or standardized tasks, such as writing boilerplate code, creating unit tests, or scripting individual files. However, AI cannot still handle more complex architectural needs or independently build entire systems. Despite these limitations, AI can significantly speed up development time by reducing the manual effort required for basic coding tasks. Key deliverables from this phase include executable or deployable software, source code, technical documentation, and build scripts.

Testing

AI is making a substantial impact in the testing phase, particularly through tools like Testim and Mabl, which leverage AI to generate automated test cases, detect patterns in test failures, and even predict where bugs are likely to appear based on code analysis. These tools can identify potential test scenarios, reducing the time and effort needed for manual testing. While AI is effective in automating regression testing and other routine tests, its ability to find unique or edge-case bugs still requires improvement. As AI matures, its role in testing could expand to cover more comprehensive and intelligent test strategies, potentially leading to faster and more reliable software releases.

Deployment

In the deployment phase, AI’s involvement is currently minimal, but tools like Harness and Spinnaker offer some automation for deployment pipelines. These platforms can automate the release process, manage continuous integration/continuous delivery (CI/CD) workflows, and monitor deployments in real time. While AI is not yet a major player in deployment, future advancements could see AI optimizing resource allocation, scaling infrastructure based on predicted traffic, and automatically resolving deployment issues. For now, the deployment process still relies heavily on human oversight to ensure successful releases.

Maintenance and Support

AI shows more potential in the maintenance and support phase than in many others. AI can analyze error logs, detect patterns, and identify anomalies that might indicate issues. Although full automation of maintenance processes isn’t yet widespread, AI is gradually making inroads in this area, suggesting that it could become increasingly important as tools develop further.

Throughout each of these phases, AI is starting to make its mark, but its role is far from fully realized. The future may hold more advanced AI-driven tools that could further integrate and enhance the SDLC, but for now, AI’s contributions are still developing and somewhat fragmented. As technology progresses, we can expect AI’s involvement to grow, potentially reshaping how software is created, tested, and maintained.

To provide a clear and concise overview of AI’s role in each phase of the Software Development Lifecycle (SDLC), we’ve created a table that summarizes the application and current state of AI tools. This table serves as a quick reference for understanding where AI is making an impact and where it still has room to grow.

Table: AI in the Software Development Lifecycle

SDLC Phase	Outcome/Deliverables	Can AI Be Applied?	Are Widespread Solutions Available?
Requirements Analysis	Requirements Specification	Yes	No
Design and Prototyping	System Architecture, Prototypes	Yes	Some, but limited
Software Development (Coding)	Executable Software, Source Code	Yes	Yes, but with limitations
Testing	Test Cases, Bug Reports	Yes	Partially, especially in automated testing
Deployment	Deployed Software	Potentially for optimization	Not significantly
Maintenance and Support	Updates, Error Reports	Yes	Yes, but not fully comprehensive

This table offers a snapshot of AI’s involvement in the SDLC, but it’s important to understand the nuances behind these entries. In the Requirements Analysis phase, AI has the potential to assist in drafting initial specifications, but tools that fully automate this process are not yet widespread. Design and prototyping are other areas where AI can contribute, particularly in generating basic layouts or graphics, though fully automated tools are still limited.

When it comes to Software Development (Coding), AI is more established, helping with routine coding tasks, though it falls short of managing complex system architecture. Testing is one of the more mature areas for AI, with significant strides made in automated testing, although AI is still not fully adept at identifying unique or edge-case bugs.

In the Deployment phase, AI’s role is currently minimal, with potential mainly in optimization tasks that could be developed in the future. Maintenance and Support is an area where AI shows promise, particularly in analyzing error logs and detecting anomalies, though comprehensive automation is still in development.

By accompanying this table with a brief narrative, readers can gain a better understanding of where AI currently stands within the SDLC and where there is potential for future growth. This combination of summary and detail ensures that the complexities of AI’s role in software development are communicated.

We are Still Far from the Ability to Create Entire Software with AI

After reviewing the table summarizing AI’s current role across the Software Development Lifecycle (SDLC), it becomes evident that while AI can assist in writing code, this is just a small piece of the overall software development puzzle. Presently, only about 5% of the SDLC is automated, with AI’s influence mostly limited to the coding phase. The majority of the SDLC, including the integration of these tools and their application within the broader context of software development, remains largely untouched by AI.

This starkly highlights a significant gap between the capabilities of current AI tools and the comprehensive needs of the software development process. While AI tools are available, they are not yet integrated in a way that automates the entire software creation process. The reality is that we are still far from a point where AI can manage the full spectrum of tasks required to create software from start to finish.

To further illustrate this, consider the following horizontal bar chart, which estimates the percentage of AI automation in each phase of the SDLC (this is just our estimation! :). The chart visually contrasts the limited scope of AI’s current application with the full extent of each phase, highlighting just how much of the SDLC remains reliant on traditional, human-driven processes.

Chart Explanation:

• Gray Bars: Represent the standard way of handling each phase without AI.
• Blue Bars: Indicate the portion that, as per our current understanding, is currently automated with AI.

To add more depth to this discussion, it’s crucial to consider the specific challenges preventing AI from expanding its role in the SDLC. These include the complexity of software architecture, the need for creative problem-solving, and the intricate nature of tasks like system integration, user experience design, and long-term maintenance. Overcoming these barriers will require significant advancements in AI technology and a deeper understanding of how AI can work in harmony with human developers to enhance, rather than replace, the entire software creation process.

Possible Solution: Applying AI in Every Phase and Chaining Results to Create Software 

The current limitations of AI in the Software Development Lifecycle (SDLC) suggest that a more holistic and integrated approach could greatly enhance its effectiveness in software creation. The key lies in chaining together each phase of the SDLC, starting from the initial stages and utilizing AI at every step. This approach envisions a seamless interface where users can interact with AI in a conversational mode to draft the application’s specifications. By combining human intuition with AI’s analytical power, this process ensures that the initial requirements are both thorough and aligned with the project’s overall vision.

Once the specification is developed, it transitions to an AI-driven prototyping tool. This tool iteratively refines the prototype through continuous interaction between the user and AI. The objective is to evolve the prototype to precisely match the requirements and expectations set out in the initial specification.

The next phase involves the actual generation of the application, where AI takes on a central role. AI is tasked with high-level responsibilities such as selecting the appropriate system architecture, determining necessary integrations, and designing the database model. After these overarching decisions are made, AI moves to more detailed tasks, including the customization of components for specific API integrations and the generation of a database schema that can be directly used to construct the application.

This approach is designed for flexibility, allowing adjustments at various levels – from high-level architectural decisions and integration strategies to minor details like the color of a button on the user interface. The component-based method, driven by AI, enables ongoing refinements throughout the software development process.

What sets this method apart is its comprehensive use of AI, extending beyond mere code generation to encompass the entire SDLC. By chaining these AI-driven phases together, the process becomes more automated, efficient, and adaptable to the evolving needs of software development.

To illustrate, imagine a business that needs a new customer management system. Using this AI-driven approach, the team begins by discussing their requirements with the AI, which then drafts a detailed specification. Based on this specification, the AI creates a prototype, allowing the team to visualize and refine the design. Once the prototype meets the desired criteria, the AI selects the optimal architecture, integrates necessary APIs, and designs a database model tailored to the business’s specific needs. Throughout development, any required changes – whether significant or minor – can be made quickly and efficiently through AI interaction, ensuring the final product meets all business requirements.

This scenario demonstrates how integrating AI across all phases of the SDLC could revolutionize software creation, making it more streamlined, adaptive, and responsive to user needs. As AI technology continues to advance, this approach has the potential to usher in a new era of software development, where AI works in tandem with human developers to create more robust, scalable, and user-centric applications.

How Flatlogic is Addressing the SDLC Challenge with AI

Flatlogic is tackling the challenges of the Software Development Lifecycle (SDLC) by reimagining each phase to incorporate specialized AI components. This innovative approach is particularly evident in how we create software. Starting with a conversational AI interface, users can draft both the specification and prototype of the application. From there, the process transitions into an AI-driven software development phase, where AI takes on crucial roles in high-level decision-making, such as selecting system architecture, determining integrations, and designing the database model.

Once these high-level decisions are made, AI moves into more detailed tasks, including customizing components for specific API integrations and generating database schemas. These schemas are then processed through the Flatlogic generator, which creates the actual applications. This seamless integration of AI and automation tools highlights Flatlogic’s unique approach, where AI is not just an assistant but a key player in the entire software creation process.

This method offers flexibility throughout the software development process, allowing for changes at various levels—from significant architectural adjustments to minor design tweaks, such as altering the color of a button. The AI-driven, component-based approach ensures that different aspects of software development are catered to with precision and adaptability.

Moreover, Flatlogic’s current capabilities extend beyond just creating code. We can generate full applications based on simple descriptions and even handle their deployment, automating a significant portion of the SDLC. To put this into perspective, while the general industry has only automated about 5% of the SDLC with AI, Flatlogic has pushed this number up to 20%. This is achieved through the automation of deployment, change management, and specification creation processes via AI.

In conclusion, Flatlogic’s approach to integrating AI within the SDLC goes beyond the current industry standard. We’re not just focused on generating code from natural language, we’re committed to creating entire software systems through a more extensive use of AI. By automating a larger portion of the SDLC, we are driving more efficient and effective software development. At least trying to do that 🙂

Summary

To summarize – while AI adaptation in software has made significant advancements in automating certain aspects of the phases – such as code generation, testing, and even prototyping – managing the entire process still fundamentally requires human involvement.

However, the long-term goal or the direction where the current landscape is going to develop (and we, as Flatlogic, are going to help to move it to) is building a complete SDLC AI Agent. In this integrated system, AI plays a role in every step and between phases, chaining the results from one to another. This approach means developing AI that can handle everything from drafting specifications and selecting architecture to customizing integrations and refining designs, all while collaborating with human developers.

Using AI for code generation is just a fraction of the entire SDLC. We still need to integrate AI into other SDLS phases and chain the outcomes, so we still have a lot of work to do!