Navigating AI for Testing: Insights on Context and Evaluation with Sourcegraph

The AI Native Dev episode focuses on AI testing, discussing context for models, evaluation of generated code, and timing and automation of AI tests. Rashab Merra from Sourcecraft is introduced, explaining how Sourcecraft tackles the big code problem by using tools like Cody to improve developer productivity with features like autocomplete and code suggestions.

Rashab explains his experience in AI since 2009, witnessing the evolution from traditional NLP to large language models (LLMs). He describes how AI, like Cody, abstracts complexity to enhance developer productivity, comparing this progression to his own experience transitioning from coding in C to using advanced frameworks.

The discussion shifts to Spotify and Netflix's recommendation systems, using various machine learning models to enhance user experience. Rashab parallels this to Cody's multifaceted approach, including features beyond code suggestions like chat, code edits, and unit test generation, each requiring different evaluations, models, and latencies.

Different features in coding assistants demand varying latency and quality levels. For instance, autocomplete needs low latency, whereas code edit and chat can tolerate more delay. Rashab explains the trade-offs between latency and model size, highlighting the benefits of fine-tuning models for specific tasks like Rust language auto-completion.

The conversation delves into efficient code and unit test generation balancing user trust in automated systems and developers' cognitive load reductions. As complexity in code suggestions increases, so does the trust issue, stressing the importance of effective evaluation systems and guardrails to prevent introducing errors through automation.

Evaluation is emphasized as crucial for development and successful adoption of AI-driven tools. Rashab highlights the importance of developing accurate evaluation metrics that mirror real-world usage over standard benchmarks. This ensures improvements in AI features truly enhance user experience.

Rashab describes the challenges of heterogeneity in coding tasks across different industries, identifying opportunities where pre-trained models excel and where fine-tuning can provide significant benefits by focusing on underserved languages or complex tasks often found in enterprise environments.

Discussion highlights the adversarial nature of good unit tests as guardrails against bad code. Effective testing prevents the introduction of errors, especially in automated settings where AI-generated code volume increases. The conversation underscores the need for unit testing to evolve alongside increased AI integration.

Different testing levels (e.g., unit, integration) and their role in ensuring code quality are explored. Rashab stresses the need for automated systems that continuously improve through feedback loops, suggesting an integrated process where human oversight complements machine-driven testing.