arXiv:2506.13932v3 Announce Type: replace-cross Abstract: The rise of large language models (LLMs) has led to dramatic improvements across a wide range of natural language tasks. Their performance on certain tasks can be further enhanced by incorporating test-time reasoning techniques. These inference-time advances have been adopted into the code domain, enabling complex software engineering (SWE) tasks such as code generation, test generation and issue resolution. However, the impact of different reasoning techniques on code-centric SWE tasks has not been systematically explored. In this work
The proliferation of large language models (LLMs) has reached a point where their application to complex software engineering tasks demands systematic exploration and optimization of reasoning techniques.
Understanding and refining reasoning techniques for LLMs in software engineering can significantly accelerate development cycles, improve code quality, and automate traditionally human-intensive tasks.
The explicit focus on code reasoning techniques suggests a move beyond basic LLM code generation towards more sophisticated, robust, and autonomous software development processes.
- · Software developers
- · AI software companies
- · DevOps platforms
- · Large language model developers
- · Manual low-level coders
- · Legacy software development methodologies
Increased efficiency and automation in software development through AI-driven code reasoning.
A potential reduction in the demand for certain types of entry-level programming roles as AI handles more routine tasks.
The emergence of entirely new paradigms for software creation and maintenance, fundamentally altering the software engineering landscape.
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Read at arXiv cs.AI