How AI Improves Software Development Process: 11 Powerful Insights from JetRuby Agency

5 min read

05 How JetRuby Uses AI 
to Improve Software Development Process cover development

As an IT company, we constantly explore ways to make our work productive and free from repetitive tasks. In line with this vision, developers must be liberated from mundane, routine tasks to contribute their best to the code creation process. This approach prevents developer burnout and yields the best results for the development process.

It’s natural to think of AI technology as a straightforward way to achieve this goal. After the launch and rapid adoption of ChatGPT, the potential of artificial intelligence to enhance productivity and reduce operational costs became even more apparent.

This article discusses the key benefits of incorporating AI technology into the software development life cycle. We examine how our software developers leverage different AI techniques to streamline workflows, automate repetitive tasks, and build high-quality software products more efficiently.

What benefits does AI technology bring to software engineering?

AI will have a profound impact on the software development industry. Many developers think AI will impact their work or cause them to become irrelevant. By delegating accurately specified, well-defined tasks to artificial intelligence, we free ourselves from routine and dedicate more quality time to architectural planning. To benefit from the vast capabilities of Large Language Models (LLMs) and to couple them with human creativity and intuition, we must first develop specific prompts detailing our requirements from AI at every stage. Only then can we leverage AI’s problem-solving abilities to help us create optimal code and architecture.

Let’s begin by reviewing AI’s potential in software development tasks, then examine specific AI utilities and use cases.

Here is how Artificial intelligence assists development teams nowadays:

  1. Automation of repetitive tasks

Automating repetitive tasks is the most essential function AI tools can help. For instance, our developers use them to:

  • Generate complete code snippets or programming functions based on users’ text prompts.
  • Modify existing code. For example, if a code snippet or programming routine suffers from security vulnerabilities or performance issues. AI-powered tools, such as OpenAI Codex,  scan it automatically and suggest improvements to make it more reliable and error-free.
  1. Predictive analysis and improved software reliability 

AI tools automatically detect and prioritize software problems based on their impact on business processes and customers. This allows our software developers to:

  • Avoid wasting time checking all security and performance issues alerts. AI tools will investigate these alerts and prioritize the most important ones that require developer attention.
  • Simulate users’ actions on the system before launching it. For example, the technology can simulate users’ actions while using the system to discover performance and security issues before they get caught by users.
  • Predictive failure. AI tools can analyze large volumes of past log data of system performance and usage and previous incidents to predict when an incident is likely to happen.

Examples of predictive analytics software are Amazon QuickSight and SAP Analytics. 

  1. Speeding up the development cycle and testing

AI tools speed up the software development cycle in JetRuby in different ways:

  • Generate code automatically from developers’ written requirements.
  • Accelerate development time by introducing an intelligent code autocomplete function.
  • Enhance project building and compiling compared with manual processes.
  • Review code immediately to discover defects and performance issues early in the development life cycle.
  • Facilitate software deployment by automating the required configurations (e.g., software and infrastructure tuning) in the deployed environment. This dramatically reduces deployment time.

In the testing area, AI tools help to:

  • Discover and prioritize software bugs so developers can focus on fixing the most critical ones.
  • AI automates the process of generating test cases covering all aspects of the software solution. This enhances its reliability.
  1. Reducing software maintenance costs

AI tools significantly reduce our software maintenance costs through:

  • Reducing debugging time by discovering bugs in the early design phases
  • Facilitating predictive maintenance, which analyzes previous software program usage to predict maintenance needs.
  • Automatically recommending code refactoring leads to better code that does not require immediate maintenance after launch and prevents costly outages.
  1. Maintaining the human element

We believe that AI will not replace human developers; instead, it will aid them in creating better software because AI facilitates the following tasks done by our team:

  • Helping developers write better code by analyzing their work and giving recommendations
  • Explaining recommendations provided by AI tools themselves. This allows our developers to understand why AI suggests a particular action or code and trust to adopt it at work.
  • Performing repetitive tasks and reducing the time needed to execute them. This enables our developers to focus on creative aspects of the work and leave repetitive duties to AI tools.

How does JetRuby apply AI in each phase of the software engineering process?

05 How JetRuby Uses AI 
to Improve Software Development Process img development

AI technology transforms how software projects within JetRuby are built and delivered. It’s no exaggeration to say that it has brought numerous benefits across the different stages of our software development lifecycle:

1. Help in prototyping

AI tools analyze users’ requirements and suggest recommendations based on the criteria supplied by the user. For instance, AI tools suggest recommendations for layout, color schemes, functionalities, typography, and user interface elements.

We employ Uizard and FigJam as AI prototyping tools to quickly create visually appealing designs. Uizard is a user-friendly design program that simplifies transforming ideas into interactive digital prototypes. It’s preferred by non-designers and professionals in our team, offering a simple drag-and-drop interface and intelligent features. Uizard is suited for quick brainstorming sessions or detailed project development.

2. Debugging  

Let’s look at the examples of AI-backed debugging tools we use. 

Code Debugging Pro is an intuitive tool that simplifies finding and correcting code errors. It caters to both novice programmers and seasoned developers. Real-time error analysis and comprehensive debugging capabilities make Code Debugging Pro helpful for both small projects and large-scale applications.

Another AI debugging tool we use, Explain An Error, was designed to demystify programming errors for developers of all skill levels. It analyzes error messages and provides clear, concise explanations and suggestions for fixing the issue. It helps us in those moments of confusion when working with code. Explain An Error supports a wide range of programming languages, making it a versatile assistant in the debugging process. Its user-friendly approach ensures that developers can quickly understand the root cause of an error and how to address it, streamlining the development process and enhancing productivity.

3. Code explanation

Having the project code documented – Project code management is essential to facilitate maintenance after launch and allow different team members to work on the same code file. AI tools automate the process of generating code explanations or comments. For instance, an AI reads and explains the code to our engineers in a natural language, summarizing its main functions and logic.

When considering examples of tools that explain code, Code-GPT is indispensable. It interprets and explains code snippets by translating complex programming concepts into understandable language. It takes a piece of code as the input, provides a detailed explanation of what the code does and how it works, and, in some cases, offers suggestions for optimization or debugging. This tool is handy for our developers seeking to understand unfamiliar codebases, learn new programming languages, or clarify specific functionalities within a code segment.

4. Code refactoring

AI-powered code refactoring tools help us automatically detect bugs, performance issues, and abnormal patterns in their code. Its refactoring tools identify potential problems and recommend optimization by analyzing code syntax and structure. This enhances programmer productivity in our teams by reducing the time required for manually reviewing and improving code quality across the different components of a software project.

OpenRefactor is an example of a code refactoring program we use. It streamlines refactoring, making it quicker and easier for developers. OpenRefactor automates code reorganization to enhance readability, simplify complexity, and improve maintainability without altering the code’s functionality. AI-enabled code refactoring is a reliable, yet often underestimated, method to reduce development costs by approximately 15%.

5. Predictive analysis

AI tools help in different software predictive analysis areas, especially defect, failure, and performance analysis. In particular, they streamline the following processes:

  • Analyzing previous defects to identify areas within the software solution that can generate defects in the future.
  • Identifying high-risk software components that are likely to suffer from a defect in the future.
  • Generating test use cases of the software solution before launching it to predict performance issues (e.g., bottlenecks and slowdown) and work to solve them before they cause problems. 

6. AI for Continuous Integration and Continuous Deployment (CI/CD)

    We use powered DevOps AI tools to automate many tasks through the CI/CD workflows, including:

    • Building code
    • Code integration
    • Continuous testing
    • Deployment to production environments

    For instance, GitLab CI/CD Guru is often used as a Continuous Integration and Deployment tool. 

    7. Automated testing

    Testing is a critical aspect of any software project. A few decades ago, software testing began manually, moved to agile testing, and then continuous testing. After the introduction of AI for software engineering, testing became known as AI autonomous testing. By leveraging AI-powered testing tools, our QA engineers enable faster, more accurate bug detection, significantly saving development time and cost. On the other hand, these testing tools can automatically generate test cases, analyze results, identify potential defects early, and recommend solutions.

    We employ Pythagora and QA Synthetics as efficient automated testing tools.

    8. Autonomous software maintenance

    Several ways are enabling us to benefit from the AI tool’s software maintenance capacity:

    • Automated bug fixing – Identify bugs and suggest recommendations for fixing them.
    • Predictive maintenance – predicts performance issues before they occur and solves them.
    • Automated testing – testing software solutions under different conditions and use cases.
    • Dependency management: Software projects are composed of many third-party components. Artificial intelligence can help discover and update these dependencies automatically to avoid security vulnerabilities and performance issues.
    • Configuration management – AI can automatically configure hardware and software used to run the application, making them more optimized.

    DocuWriter.ai and DocumentationLab are the autonomous software maintenance tools we use.

    9. Documentation

    AI tools read code files, understand their functions, and generate documentation to help our developers understand them.

    10. Enhancing code quality with Copilot, ChatGPT

    JetRuby actively utilizes Copilot and ChatGPT in its development practices and internal processes. This allows the creation of high-quality software products that do not sacrifice human developers’ innovation and imagination.

    In particular, using Copilot and ChatGPT helps us improve code quality and accelerate development by:

    • Reducing error – by recommending contextually relevant code snippets.
    • Enhancing consistency – recommend consistent code that follows established patterns.
    • Encouraging best practices –Suggest using code snippets that use secure coding and follow proper performance best practices.
    • Save time – Allow our developers to focus on performing complex tasks while leaving the routine activities to AI tools. This amplifies developer productivity and allows us to create high-quality software solutions at competitive prices.

    11. Improving the accuracy of estimations

    Traditional methods for estimating the costs of complex web development projects were time-consuming and inaccurate. This leads to budget overruns. To solve this problem, our development team utilizes ChatGPT and Copilot to streamline the project cost estimation and development process.

    This allows our company to speed up the time to market by 30% while still maintaining high quality of delivery and achieving profits.

    AI technology advances dramatically

    We’ve been keen on tracking development metrics as it’s our bread and butter. Our team did its best to shift from traditional methodologies to those enhanced by Artificial Intelligence. We’ve noticed that the average time to complete tasks has improved by 15%, and this improvement is consistently ongoing.

    Integrating the right AI tools into our toolchain made our software development team much more efficient! In particular, this integration:

    • reduces the overload of developers and prevents their burnout. As a result, we allocate more time for complex design tasks and innovation.
    • minimizes errors and improves the CI/CD process,
    • generates test use cases,
    • simplifies finding and correcting code errors,
    • enhances code quality, and reduces costs associated with code refactoring. 

    The list is not exhaustive: as AI technology advances dramatically, we’re excited to harness its full potential to develop groundbreaking software solutions.

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