Automated program comprehension underpins many software engineering tasks, from code summarisation to clone detection. While recent deep learning models have achieved strong results, they typically rely on source code alone, overlooking contextual signals such as version history or structural relationships. We conduct an empirical study of how enriching code representations with such signals affects model performance on four tasks: code clone detection, code classification, code summarisation, and vulnerability detection, using three datasets (SeSaMe, CodeSearchNet-Java, Vul4J). We evaluate five foundational code models (CodeBERT, GraphCodeBERT, CodeT5, PLBART, ASTNN) and seven code-specialised LLMs (CodeLlama, Qwen2.5-Coder, DeepSeek-Coder), comparing prompt-based context injection against fine-tuned use of context. Context improves classification for all five foundational models and, after fine-tuning, for most LLMs. For summarisation, the effect depends on the model family: version history improves all four foundational models on every metric, whereas prompt-level context helps two of the seven LLMs and hurts the other five. Combining context types reaches up to +21.48% macro-F1 in code classification and +15.04% F1 in clone detection. A human evaluation of CodeT5 summaries on 100 Java snippets ranks context-augmented summaries significantly higher for Accuracy and Content Adequacy. An exploratory vulnerability detection evaluation provides initial evidence of transfer to security-oriented tasks.

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