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How to Increase Developer Productivity with GenAI – We’ve Done It

GenAI is revolutionizing software development by optimizing key phases of the SDLC. Learn how tools like GitHub Copilot can streamline coding, enhance productivity, and drive faster time to market. 

GenAI is transforming industries across the board, and software development is no exception — offering the potential to boost developer productivity by 30-40%. 

In a previous blog, we explored how enterprises can leverage GenAI for various business use cases. 

Today, we’re focusing on how GenAI can enhance developer productivity throughout the software development life cycle (SDLC), enabling faster time to market. 

In this post, we’ll break down: 

  • The stages of the SDLC where GenAI can help 
  • How GenAI tools can optimize workflows and coding efficiency 
  • How to introduce AI-driven coding assistants like GitHub Copilot to your development team 

How GenAI Helps with Code Creation 

When people hear that GenAI can help with software development, they immediately think of coding, so let’s start there. In the coding stage of the SDLC, GenAI brings substantial improvements across several key areas: 

  • Code Generation – AI can suggest code snippets, entire functions, or even complete classes based on the context, reducing the time spent on repetitive tasks. 
  • Code Understanding By analyzing existing code, GenAI can help developers grasp complex logic and identify potential issues. 
  • Code Debugging AI can assist in pinpointing errors and suggesting solutions, accelerating the debugging process. 
  • Code Refactoring GenAI can recommend improvements to code quality, making it more readable, maintainable, and efficient. 
  • Unit Test Generation Automated generation of unit tests can ensure code reliability and reduce manual effort. 

But if you’re only using GenAI for these coding-related tasks, the productivity boost is limited. Research shows that developers spend just 20% of their day coding. 

That means if you only apply GenAI to coding tasks, you’re only tapping into a fraction of its potential — a 20% productivity increase at most. 

That’s why we should all look into implementing GenAI across all stages of the SDLC to enhance developer productivity even further. 

How GenAI Can be Applied Across the SDLC 

GenAI’s impact goes far beyond coding. From Requirement Analysis to Maintenance, GenAI can streamline processes, improve accuracy, and boost productivity throughout the entire SDLC.  

Table Overview: GenAI’s Role in Each Phase of the SDLC  

Phase 

Activities 

Requirement Analysis 

Extracting and summarizing information from large sets of textual data related to user needs and preferences. 

Assisting in generating initial drafts of requirements based on high-level input or user queries. 

Assisting in defining user stories with clear acceptance criteria. 

Planning 

Identifying potential risks and suggesting mitigation strategies. 

Design 

Generating design documentation based on requirements. 

Recommending suitable patterns for specific functionalities. 

Implementation 

Providing suggestions, auto-completing code, and identifying potential issues. 

Conducting code reviews to analyze code quality and adherence to standards. 

Measuring code quality through metrics like code coverage, cyclomatic complexity, and maintainability index. 

Leveraging coding best practices from organizations. 

Recommending common libraries. 

Recommending existing libraries for similar functionalities. 

Recommending database schemas based on requirements. 

Recommending data access patterns. 

Testing 

Generating test scenarios based on requirements and acceptance criteria. 

Generating test methods based on test scenarios. 

Generating security test cases. 

Identifying and generating performance test scenarios. 

Automating performance testing. 

Deployment 

Automating deployment processes to ensure consistency and reduce deployment errors. 

Monitoring 

Analyzing system compatibility with target environments and suggesting optimizations. 

Documentation 

Detecting and notifying about issues proactively. 

Responding immediately to user queries and issues. 

Maintenance 

Identifying breaking changes in code and data schemas. 

Phase 

Requirement Analysis 

Planning 

Design 

Implementation 

Testing 

Deployment 

Monitoring 

Documentation 

Maintenance 

Activities 

Extracting and summarizing information from large sets of textual data related to user needs and preferences. 

Assisting in generating initial drafts of requirements based on high-level input or user queries. 

Assisting in defining user stories with clear acceptance criteria. 

Identifying potential risks and suggesting mitigation strategies. 

Generating design documentation based on requirements. 

Recommending suitable patterns for specific functionalities. 

Providing suggestions, auto-completing code, and identifying potential issues. 

Conducting code reviews to analyze code quality and adherence to standards. 

Measuring code quality through metrics like code coverage, cyclomatic complexity, and maintainability index. 

Leveraging coding best practices from organizations. 

Recommending common libraries. 

Recommending existing libraries for similar functionalities. 

Recommending database schemas based on requirements. 

Recommending data access patterns. 

Generating test scenarios based on requirements and acceptance criteria. 

Generating test methods based on test scenarios. 

Generating security test cases. 

Identifying and generating performance test scenarios. 

Automating performance testing. 

Automating deployment processes to ensure consistency and reduce deployment errors. 

Analyzing system compatibility with target environments and suggesting optimizations. 

Detecting and notifying about issues proactively. 

Responding immediately to user queries and issues. 

Identifying breaking changes in code and data schemas. 


How Much More Productive Can This Make Your Developers? 

Based on a small pilot project with our engineers, we project a potential productivity increase of 30%40%.

This figure represents our initial findings and reflects Relevantz’s specific experiences with GenAI.  

While our pilot project was small and the duration limited, showing a 30%40% productivity increase, other studies have indicated potential gains even in the 50% range. This suggests that GenAI could significantly transform software development, perhaps more than we’ve observed so far. 

How Can You Measure That Productivity? 

To quantify these productivity gains, we can turn to GitHub’s SPACE framework, which classifies productivity into five key factors: 

  • S – Satisfaction & Well-being: Ensuring engineers are content and mentally healthy, as dissatisfaction can significantly decrease productivity. 
  • P – Performance: The ability to quickly release new features to the market. 
  • A – Activity: Monitoring the level of coding and testing activity. 
  • C – Collaboration & Communication: Assessing how effectively the team communicates and collaborates on projects. 
  • E – Efficiency & Flow: Evaluating the efficiency of the code and the smoothness of development processes. 

For instance, to assess Satisfaction, metrics such as team health surveys and retention rates can be utilized, while Performance can be evaluated by measuring the speed of feature deployment and levels of customer satisfaction. 

Now that you know what to measure, remember, applying these metrics isn’t an exact science — yet. It’s up to you to tailor how you measure these factors to best fit your team and projects. 

How Developer Support Has Evolved: From Peer Consultations to GenAI Tools 

Over the years, the methods developers use to seek help have evolved significantly. Here’s a look at the typical timeline: 

  1. Historically: Consulting senior engineers or delving into official product documentation. 
  2. More Recently: Searching for answers on Google and Stack Overflow. 
  3. Current Trend: Embracing advanced tools like ChatGPT and GitHub Copilot.  
Choosing Your Modernization Path: The Simplified 6 R's Framework


Developers are increasingly using AI tools, making it essential for all of us to embrace these advancements in software development. As GenAI technologies become more common, embracing them is crucial.
  

How You Can Embrace GenAI for Software Development 

While GenAI holds immense potential, it’s important to implement it thoughtfully. Here are some tips to get started: 

  • Provide prompt training: Ensure developers are not only familiar but fully comfortable using GenAI tools. This foundational step is crucial for maximizing efficiency with these tools, making it the essential first step. 
  • Start small: Kick off with a pilot project using tools like GitHub Copilot, Amazon Q, or Google Gemini to gauge both the benefits and challenges. 
  • Set clear expectations: Clearly define what you aim to achieve with GenAI and how you’ll measure success. 
  • Continuously learn: Stay updated on the latest advancements in GenAI. 
  • Prioritize security and privacy: Implement robust data handling and privacy policies to protect sensitive information and ensure compliance with industry standards. 
  • Protect proprietary code: Ensure that proprietary code isn’t leaked or shared inadvertently, and verify that no external or unauthorized code is being used. 

By effectively leveraging GenAI, developers can streamline their workflows, improve code quality, and ultimately increase productivity. 

What Relevantz Can Do for You 

When used correctly, GenAI unlocks numerous possibilities for your enterprise, from automating routine tasks to crafting innovative solutions for complex problems. 

At Relevantz, we excel in crafting custom business solutions powered by GenAI platforms. Our expertise ensures effective adoption and integration of GenAI, propelling your business toward innovation and growth. 

Have any GenAI questions? Our experts are here to assist with everything from quick inquiries to building custom GenAI solutions tailored to your business.