ALTEN Group Case Studies Sharing

CIeNET, an ALTEN Group subsidiary specialized in technology solutions, is driving innovations in coding. CIeNET leverages AI-powered code generation tools and large language models (LLMs) to streamline tasks, improve coding quality, and enhance the developer experience.

The integration of generative AI into software development is revolutionizing how developers build and maintain applications, helping them to write and fix code faster and more efficiently. Through its work on generative AI-powered code assistants, CIeNET is providing consulting services and solutions aimed at optimizing the performance of AI tools.

Challenge: Enhance the reliability and performance of existing AI-powered coding assistants in generating accurate, high-quality code that works in diverse situations, quickly identifying and correcting mistakes, adapting to different programming styles, and responding well to instructions

Solution:

  • AI code assistant plug-ins for IDEs, aligned with developers’ evolving needs
  • Automated benchmarking systems to evaluate performance and accuracy
  • Advanced prompt engineering techniques to improve the relevance and precision of AI-generated code
  • Test cases to ensure the functional and non-functional reliability of IDE plug-ins
  • Comprehensive triage systems to manage incoming issues
  • Optimized prompts to ensure that LLMs produce precise and contextually relevant code

Benefits:

  • Improved code quality
  • Enhanced responsiveness of AI assistants
  • Increased developer productivity

 

Improved AI coding assistants

One of CIeNET’s major achievements is the Generative AI Benchmark System (GAINS). GAINS compares various LLM-based coding assistants – including OpenAI’s ChatGPT, Google Gemini, and Anthropic Claude 3 – to evaluate how they work in different scenarios. This enables CIeNET to identify areas for improvement and fine-tune the systems. An automated testing process reduces errors and speeds up the development of tools, while the fine-tuning of large language model (LLM) models improves the accuracy and quality of code generation. In this way, GAINS enhances the reliability and performance of AI-powered coding assistants already integrated into commonly used software, across real-world coding scenarios. To ensure seamless assimilation within integrated development environments (IDEs), the systems undergo comprehensive testing, triage and fine-tuning to tackle issues across user interfaces, backend services, and LLM interactions. By fixing bugs and adding new capabilities, the existing tools become more useful for developers. Improving how prompts are provided to the AI further enhances their effectiveness.

CIeNET’s toolbox

CIeNET employed diverse tools, including Python, Java, Go, C++ and JavaScript, alongside plug-ins like Gemini, Copilot, and CodeWhisperer, integrated into IDEs such as Visual Studio Code and JetBrains.

Outcomes that matter

These improvements make a real difference for developers and their clients. The new tools improve the AI’s ability to understand real-world challenges and respond to them with suggestions that are more accurate and helpful. This in turn saves time for developers, enabling them to focus on more creative and important tasks and limiting the time they have to devote to resolving repetitive problems.

CIeNET’s expertise in integrating generative AI within modern development workflows exemplifies how tailored solutions can enhance productivity and streamline software engineering processes. By addressing challenges such as LLM reliability, prompt optimization, and test automation, CIeNET’s work in generative AI paves the way for smarter technology that can help solve complex problems. The focus is on making these tools reliable and user-friendly. The solutions are potentially useful not only in coding, but in other areas as well.

 

For more information, please visit AI and Machine Learning.