AI and LLMs Support Developer and DevOps Productivity

Look For a Software Development Partner That Uses AI and LLMs

A recent Copilot study revealed an interesting fact about the use of AI and Large Language Models (LLM) in the software development process. The study included developers from Microsoft, Accenture, and a Fortune 100 electronics firm and reported a 26% boost in productivity, increasing output from the usual eight hour workday to what amounts to ten hours of traditional output. This improved output increased even more for less experienced developers.

By leveraging Artificial Intelligence (AI) and Large Language Models (LLM), the DevOPS organization can greatly improve output, code quality, developer productivity and consistency. As businesses embrace the collaborative and team-oriented concepts of DevOps, the use of AI and LLMs can be utilized across the organization, and forward-thinking organizations are looking at the set of practices in DevOps (software development IT operations) to automate processes and accelerate the software development lifecycle.

Where software vendors employ these techniques, clients, customers and end-users can benefit from this approach. The development team can work more quickly and efficiently to satisfy requirements, design, develop and test and deploy, so business projects can be completed more rapidly and dependably.

If a business is considering a vendor or a software product for implementation within the walls of the enterprise, it is worth asking the prospective vendor and service provider how they are currently using cutting-edge technology to improve their development process and lifecycle.

Elements and Aspects of AI in Software Development

The Use of AI and Large Language Models (LLM) Improves the Development Process

Prompt Engineering uses natural language interfaces to study interactions with and the programing of LLM computation systems to enable complex problem solving, looking for patterns and focusing on reusable solutions. Infrastructure-as-Code (IaC), Code-as-Data and CodeQL LLMs support developers by exploring the code, studying requirements and documentation and analyzing infrastructure to find issues and inconsistencies.

Automated Code Generation allows the development team to optimize testing and deployment. Developers can use AI code review tools like Codiga and testing tools like DiffBlue Cover to review and analyze code and find issues, and AI-based code generators like GitHub and Copilot.

Generative AI (GenAI) leverages LLMs to streamline the steps in the development process, including analysis of requirements, coding and testing.

Natural Language Processing (NLP) enables code generation with machine learning and produces suggestions to develop or complete code, thereby reducing the occurrence of human error and allowing developers to focus on other, more complex aspects of code and development.

Testing and Debugging can be automated to detect and address bugs, inefficiencies and vulnerabilities in the code. These tools can be used to generate unit tests, create test cases and increase the effectiveness of the testing phase to improve overall quality.

Translation Tools enable translation of other programming languages for projects where the team must migrate code to other programming languages. The process uses large language models to complete the translation, leaving developers free to focus on architecture.

Documentation Support includes development of documents for code comments, regulatory requirements etc. Prompt Engineering generates summaries and answers questions and provides examples so developers who review the code for later upgrades have appropriate documentation to support the software evolution.

Project Management for all of DevOps is supported by automated routines and integration of information and documentation throughout the process, monitoring system performance, analyzing test results and optimizing implementation. The ongoing analysis of test planning, data migration, compliance documentation and architecture supports the entire DevOps team.

If your business wishes to improve productivity, timelines, budgets and dependability of in-house applications, you will want to find a vendor and service provider who appropriately employs AI and LLMs to support its development model. If you are planning to engage an IT expert to augment your own software product or solution, it is wise to look for this capability when you interview prospective partners. Contact Us to find out how to integrate AI And LLM capabilities into your software project, website, analytics initiative or other project. Explore our free White Papers: ‘What Is AI And How Can It Help My Business,’ and ‘The Practical Use Of GenAI In BI And Analytics Tools.’

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