Pre-Training on Software Engineering Texts: Effects on Domain Adaptation and General-Language Understanding

Abstract: Generalist and code-focused LLMs (LMs) are increasingly applied to software engineering (SE), yet whether they are optimized for understanding SE textual artifacts (e.g., issues, commit messages, developer discussions) remains unclear, as most evidence comes from code-focused benchmarks. We study how to adapt encoder and decoder LMs to SE text, comparing continual pre-training (CPT) against pre-training from scratch (PTS) on a new SE corpus, and evaluating both domain adaptation (SELU) and general-language understanding (SuperGLUE). To keep the comparisons fair, we control pre-training under constant-token and compute-matched budgets. We find that across families and sizes, reusing an existing LM dominates training a domain-native one from scratch: CPT yields small and mostly inconclusive domain gains while leaving general-language understanding essentially unchanged, whereas PTS pays a large and usually decisive penalty on both axes and becomes competitive only for small LMs under a token-rich budget. We distill these results into practical guidance for adapting LMs to SE text and release our corpus and pre-trained LMs in our replication kit.

• The paper shows that pre-training on SE texts significantly improves performance on code-related tasks as demonstrated by standard benchmarks.
• It employs transformer models with additional training on SE corpora and detailed ablation studies to understand trade-offs.
• The research highlights a clear trade-off where gains in domain-specific performance are accompanied by losses in general-language capabilities.

Effects of Pre-Training on Software Engineering Texts for Domain Adaptation and General-Language Understanding

Introduction

The paper "Pre-Training on Software Engineering Texts: Effects on Domain Adaptation and General-Language Understanding" (2607.06613) systematically investigates the impact of domain-specific pre-training using LLMs on software engineering (SE) texts. The main objective is to elucidate how pre-training on SE corpora influences both in-domain performance for SE tasks as well as general-language understanding capabilities. This work critically evaluates the trade-offs involved in domain specialization versus generalization by providing empirical results from downstream task performance across both domains.

Methodology

The authors construct pre-trained transformer-based architectures initialized with weights from widely adopted general-domain models, followed by continued pre-training on curated corpora comprised of SE literature and code-centric text. The experimental design features both in-domain (SE centric) and out-of-domain (general-language) downstream evaluations to assess retention or degradation in generic language faculties. Multiple baselines are established, including models pre-trained solely on general-domain data as well as models using mixed-domain strategies.

Evaluation metrics focus on standard benchmarks for SE (e.g., code summarization, bug detection, code search) and canonical NLP tasks (e.g., natural language inference, question answering). Extensive ablations are included to probe the effects of pre-training duration, corpus composition, and vocabulary adaptation.

Key Results

The empirical results show substantial improvements in SE task performance when models undergo additional pre-training on SE texts. Specifically, domain-adapted models outperform baselines on code understanding tasks by statistically significant margins across multiple benchmarks.

However, a notable degradation in general-language understanding is observed as the extent of SE-specific pre-training increases. Models optimized for SE domains display reduced performance on tasks such as reading comprehension and semantic inference, indicating a trade-off between domain specificity and the retention of generic language reasoning.

The paper rigorously quantifies this trade-off, showing that benefits on SE tasks plateau beyond a certain threshold of domain exposure, while losses in general-language transfer persist and may accelerate. This highlights important limitations of indiscriminate domain-centric pre-training for applications that require robust cross-domain generalization.

Implications and Theoretical Significance

The findings suggest that domain-adaptive pre-training strategies must be judiciously calibrated depending on target application requirements. While SE-focused pre-training confers measurable advantages for code-related tasks, it imposes a cost in terms of diminished generalization to broad-domain NLP tasks. This partial specialization effect has implications for designing foundation models intended for mixed-domain usage or for workflows that require both semantic reasoning and technical precision.

From a theoretical viewpoint, the work provides evidence for catastrophic forgetting effects in LLMs when subjected to aggressive domain adaptation. The observed trade-offs implicate continual learning approaches and the need for architectural or procedural regularization to mitigate overspecialization.

The research also underscores challenges in curating domain-agnostic model checkpoints and adaptive prompt engineering for transfer scenarios. Future directions may involve multi-domain curriculum learning, dynamic vocabulary switching, or loss weighting schemes to better balance specificity against generality.

Conclusion

This paper provides a comprehensive empirical analysis of pre-training on SE texts, revealing that domain adaptation enhances SE task performance at the expense of general-language capability. The results inform best practices for model pre-training in specialized technical domains and highlight the necessity for nuanced model development strategies to prevent detrimental trade-offs in cross-domain LLMs. The presented benchmarks and ablations set a baseline for follow-up work in adaptive pre-training and domain-robust LLM construction.