arXiv:2501.11086v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have shown tremendous promise in automated software engineering. In this paper, we investigate LLMs for just-in-time regression test generation for programs, like parsers, interpreters, or compilers, that take highly structured, human-readable inputs. When a bug fix or code change is committed, the repository (as part of CI/CD) runs an LLM for a few minutes to generate regression tests that exercise the changed code and potentially trigger bugs. We frame LLM-based regression test generation as a machine tran
The rapid advancements in large language models (LLMs) continue to push their capabilities into complex software engineering tasks, making evaluation of their practical applications a current priority.
Sophisticated readers should care as this indicates an acceleration in the automation of crucial software development processes, potentially reducing human effort and improving code quality, impacting workforce dynamics and development cycles.
The conventional manual or semi-automated regression test generation in software development may be significantly augmented or replaced by AI-driven methods, changing resource allocation and skill requirements.
- · Software companies
- · DevOps teams
- · LLM developers
- · AI-driven testing platforms
- · Manual software testers
- · Traditional test automation tool vendors (if they fail to adapt)
Increased efficiency and speed in software development and deployment due to automated regression testing.
A potential reduction in software bugs reaching production, leading to more stable and reliable applications.
The development of more complex and robust software systems, as testing becomes less of a bottleneck, further accelerating innovation in software sectors.
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Read at arXiv cs.AI