9 ways to increase developer productivity with generative AI

Software development is one field that is already seeing a significant impact from generative AI tools. The benefits are many, and businesses that adopt these tools today can see significant productivity gains. A McKinsey study found that software developers can complete coding tasks up to two times faster using generative AI.

According to a study by a consulting firm, complex coding tasks are not significantly affected by the use of generative AI, so concerns about AI replacing developers can be put to rest. However, there are “low-hanging fruit” use cases where AI can significantly increase team productivity and improve the developer experience.

But before we look at what impact generative AI tools can have, let’s talk more generally about how to improve developer productivity through methodologies, frameworks, and best practices. Generative AI is just one tool in your tool belt.

Measure and improve developer productivity

Measuring developer productivity, a subset of employee productivity, presents a multifaceted challenge. Traditional metrics, such as lines of code written or time worked, often fall short of capturing the complexity of complex workflows. This may not adequately reflect the quality or broader impact of the developer’s work, and may require the incorporation of external factors such as customer satisfaction to be properly assessed. It’s important to recognize that developer productivity extends beyond simple code generation. This includes delivering high-quality deliverables that consistently satisfy customers while mitigating the risk of burnout. A burned-out developer is usually an unproductive developer.

DevOps Research and Assessment metrics (DORA), which include metrics such as deployment frequency, lead time, and mean time to recovery, serve as a measure of software delivery efficiency. These developer productivity metrics help engineering managers and chief technology officers (CTOs) accurately measure individual and team performance.

Widely adopted project management tools like Jira help you track progress, manage tasks, and facilitate contribution analysis. Implementing the SPACE framework (Software Engineering, Productivity, Analytics, Collaboration, and Efficiency) provides a holistic approach to software development. Key performance indicators (KPIs), such as story points and real-time productivity tools, serve as benchmarks to continuously measure and improve software developer productivity.

Diversifying productivity measures beyond individual performance requires a comprehensive understanding of team dynamics. Collaboration platforms like GitHub act as a catalyst for a culture of open communication, collaborative code reviews, and easily facilitated pull requests. These platforms provide a collective space for team members to learn from each other as well as improve their skills. Strategic introduction of new features and consistent delivery of high-quality code not only strengthens the competitiveness of the product, but also significantly contributes to end-user satisfaction.

DevOps has emerged as an innovative methodology to optimize the efficiency of the software development lifecycle by seamlessly integrating development and operations practices. By fostering collaboration between developers and operations teams, DevOps aims to streamline processes, minimize lead times, and increase deployment frequency. This creates an environment conducive to continuous innovation and improvement. DevOps helps you resolve bottlenecks and proactively manage technical debt, creating a work environment that keeps your developers happy and engaged.

Engineering managers can create an environment conducive to developer productivity by conducting regular contribution analysis and using this information to integrate new tools and address employee experience issues. The adoption of the Your Engineering Success (YES) model highlights the importance of fostering a positive and supportive culture within the team and fostering an atmosphere that encourages innovation and creativity. This holistic approach allows you to measure and optimize developer productivity in a way that not only improves individual and team performance, but also fosters the overall well-being of your development workforce.

How generative AI can help

There are many ways AI can streamline your development workflow. Here are some more common use cases:

Eliminate repetitive tasks

Coding often involves simple and sometimes tedious tasks, and this is where generative AI tools tend to shine. Repetitive, routine tasks, such as typing standard functions, can be handled quickly with autocomplete. Tools like OpenAI’s Codex can suggest lines of code or entire functions based on natural language descriptions. Speed ​​up code documentation by helping developers automatically adhere to specific documentation formats.

natural language interface

Generative AI can facilitate natural language interfaces for software development tools. Developers can use natural language commands to interact with the development environment, debugging, and version control systems, making it more accessible to those without extensive programming expertise.

code suggestions

Generative AI can also help beginners as they write code by providing context-aware suggestions, explanations, and guidance. This accelerates the learning curve for new developers and democratizes access to software development.

Code improvements

Generative AI can suggest improvements to existing code by identifying redundant or inefficiencies. This can help maintain code quality and performance over time. Problems that were once difficult to pinpoint can be discovered and resolved much faster by implementing AI-suggested solutions, and this can even be done automatically.

code translation

Generative AI can also translate code from one language to another, streamlining code conversion or app modernization projects, such as converting COBOL to Java to update legacy applications.

code testing

Generative AI can be used to automatically generate test cases. By analyzing your code and generating test inputs, you can improve test coverage and identify potential issues early in the development process.

bug detection

By analyzing large code bases, generative AI can help software development teams identify and automatically fix bugs. This results in more robust and stable software as well as faster development cycles.

Personalized development environment

Generative AI can help create customized development environments that adapt to individual developer preferences and coding styles. This increases productivity and makes the coding experience more comfortable for programmers.

Enhanced Documentation

Generative AI can help engineering teams generate documentation by summarizing code functionality, explaining algorithms, and providing context. This can be useful for maintaining clear and up-to-date project documentation.

How Generative AI for Coding Software Works

Generative AI in coding works by leveraging machine learning models trained on large code datasets. These models can understand the structure and syntax of programming languages.

Model pre-training

Generative AI models are pre-trained on large datasets containing a variety of code examples written in different programming languages. During pre-training, the model learns how to predict the next word or token in a code sequence based on the context of the previous word. This process allows the model to capture the syntax, semantics, and patterns inherent in various programming languages.

Understanding context

When presented with a coding prompt or query, a generative AI model processes the input and uses learned knowledge to understand context and intent. The model considers the relationships between various code elements such as variables, functions, and control structures to generate relevant and syntactically correct code.

Code generation

Generative AI models use learned patterns and contextual understanding to produce code snippets as output. The generated code is based on input prompts and follows the structure and style of the programming language in which the model was trained.

Adapt to user feedback

Generative AI models often have mechanisms to adjust and improve based on user feedback. Developers can provide feedback on the generated code to help refine the understanding of the model and improve future output. This iterative feedback loop contributes to the model’s ability to generate more accurate and context-sensitive code over time.

Generative AI in coding is a powerful tool, but it cannot replace the creativity, problem-solving, and domain expertise of human developers. It acts as an augmentation tool to support developers’ coding efforts, provide suggestions, and potentially accelerate certain aspects of the development process. Developers must use generative AI responsibly, thoroughly verify the generated code, and supplement the output with their own expertise and understanding.

Hypothetical use case

Imagine a programmer tasked with implementing complex functionality in a web application project. Faced with complex data manipulation and dynamic content rendering challenges, she decided to integrate generative AI into her development workflow to accelerate the coding process. She begins by carefully defining the requirements of a new feature and encapsulating its core logic and structure into coding prompts. Leveraging generative AI tools trained on diverse datasets of web development code, she inputs coding prompts and lets the model automatically generate preliminary code snippets that meet the specified requirements. This generated code includes functions for data processing, event handling, and dynamic content rendering.

She goes into an iterative process of improving and fine-tuning the generated code. Through these interactions, she ensures that the AI-generated code adheres to the project’s coding conventions and architectural nuances. Now that she is satisfied with the generated code, she integrates it into the web application’s existing codebase. Despite the development process accelerated by generative AI, she recognizes the essential role of human verification in thorough testing to ensure functionality is accurate, responsive, and reliable.

Integrating generative AI into her workflow not only accelerates the coding process, but also allows her to allocate more time to higher-level design aspects, user experience considerations, and comprehensive testing. This use case demonstrates how generative AI can serve as a valuable ally, empowering developers and contributing to the overall efficiency and quality of the software development life cycle.

Getting started

IBM watsonx Code Assistant leverages generative AI to accelerate development while keeping trust, security, and compliance principles at its core. Developers and IT operators can accelerate application modernization efforts and create automation to rapidly scale their IT environments. watsonx Code Assistant is based on IBM Granite, which includes a state-of-the-art, large-scale language model designed for code that allows IT teams to generate high-quality code using AI-generated recommendations based on natural language requests or existing sources. password.

Explore the watsonx code assistant