VENDORiQ: Open Source StarCoder Goes Supernova on Technical Debt

Why it’s Important

The release of StarCoder introduces an open access model to the landscape of AI coding services, distinguishing itself primarily through its development and licensing approach. Many existing AI coding services, often integrated into commercial development environments, operate under proprietary models and closed ecosystems. In contrast, StarCoder’s open access nature allows for broader scrutiny, adaptation, and integration by development teams without the restrictive licensing typically associated with commercial offerings.

IBRS positions the model’s openness as a key metric for evaluating trust in AI. As a result, Starcoder scores highly as a trusted AI¹.

This difference in accessibility has several implications. For organisations, leveraging an open access LLM like StarCoder can reduce vendor lock-in and provide greater control over the model’s customisation and deployment within their specific technological stacks. The stated open science and open governance principles suggest a collaborative development environment where external researchers and developers can improve the model and identify potential biases or security vulnerabilities. This transparency contrasts with the opaque nature of some proprietary solutions, where the underlying training data and model architecture remain undisclosed.

StarCoder’s training across over 80 programming languages aims for broad applicability, enabling it to assist with diverse coding tasks, from generating new code to modifying existing functions based on natural language prompts. This ability to translate ‘legacy code’ to modern code is significant, as it is a challenge that contributes to—if not defines—enterprise technical departments.

While deciphering, documenting and translating old code is a vital part of an application modernisation effort, it still needs human input to modernise processes and interfaces. However, the ability to use AI coding tools to prototype changes after the initial code base conversions is another capability that needs to be embedded into programs to reduce legacy debt.

While proprietary services may also offer multi-language support and similar functionalities, the ability to run StarCoder as a standalone tool or integrate it via plugins into common development environments offers flexibility. The approach could facilitate its adoption in organisations that prioritise open source technologies or wish to avoid dependency on a single commercial provider.

Who’s Impacted?

• Chief Technology Officers (CTOs) and Chief Information Officers (CIOs): Identify the potential for AI code tools to modernise legacy applications.
• Development Team Leads: Evaluate and integrate open access AI tools into their development workflows, assessing their impact on productivity and code quality.
• Software Architects: For understanding the technical specifications and deployment considerations of open access LLMs in enterprise environments.

Next Steps

• Thoroughly assess the licensing terms of StarCoder to ensure compatibility with internal open source policies and intellectual property considerations.
• Conduct pilot programmes within development teams to evaluate StarCoder’s performance on internal codebases and compare its efficiency and accuracy against existing commercial or proprietary coding services.
• Investigate the community support and governance structures of the BigCode Project to understand the long-term viability and update mechanisms for the StarCoder model.
• Evaluate the resource requirements (compute, data storage) for deploying and operating StarCoder internally versus consuming a managed service from a commercial provider.

1. “The IBRS AI Trust Scorecard Framework 2025” IBRS ↩︎