TeleSWEBench: A Commit-Driven Benchmark for Evaluating LLM-Powered Software Engineering in Telecommunications

2026-04-16 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Expert, extended

Summary

TeleSWEBench is the first commit-driven benchmark designed to evaluate Large Language Model (LLM)-powered Automated Software Engineering (ASE) frameworks in the telecommunications domain. It addresses a critical evaluation gap by mining 734 real developer commits from the srsRAN 5G repository, distilling them into structured test cases across Easy, Medium, and Difficult tiers. The benchmark employs a two-stage evaluation pipeline, decoupling localization accuracy from functional correctness, and introduces TeleJudge, a hierarchical LLM-as-a-Judge framework. Initial evaluations of AIDER, OpenHands, and ClaudeCode, powered by LLMs like Qwen3, GPT OSS, Gemma 4, Kimi, and Qwencoder 2.5, show that even the strongest ASE tools achieve only up to 25% shippable changes, highlighting significant capability gaps in handling complex wireless stacks.

Key takeaway

For AI Engineers developing or deploying LLM-powered software agents for specialized domains like telecommunications, this research underscores the need for domain-specific benchmarks. You should prioritize agents capable of precise multi-file localization and robust functional correctness, as general-purpose tools currently struggle with complex C++ wireless stacks, achieving only up to 25% shippable changes. Consider integrating hierarchical LLM-as-a-Judge frameworks to capture nuanced code quality beyond basic unit tests.

Key insights

TeleSWEBench evaluates LLM-powered software agents in telecom by using real srsRAN 5G commits and a two-stage, judge-augmented assessment.

Principles

• Telecom software requires multi-file, stateful, C++ modifications.
• LLM-as-a-Judge can overcome rigid unit test limitations.
• Larger LLMs exhibit timidity in complex codebases.

Method

TeleSWEBench generates 734 questions from srsRAN 5G commits, categorized into three difficulty tiers (Easy, Medium, Difficult). It uses a two-stage evaluation: first, task localization, then functional correctness via unit tests and a hierarchical LLM-as-a-Judge (TeleJudge).

In practice

• Use TeleSWEBench to benchmark LLM agents on telecom-specific code.
• Implement a two-stage evaluation for localization and correctness.
• Consider LLM-as-a-Judge for nuanced code assessment.

Topics

• Telecommunications
• LLM-Powered Software Engineering
• Software Agents
• srsRAN 5G
• Code Generation Benchmarking
• LLM-as-a-Judge

Code references

• prnshv/TeleSWEBench
• gsma-labs/evals
• srsran/srsRAN_Project
• Aider-AI/aider
• ollama/ollama

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.