From the 1 of 7 linked papers with an AI index.
7 papers
ExplainBench: Evaluating Code Explanations from Agents
Zhiyuan Pan, Sungmin Kang, Imam Nur Bani Yusuf +1
The paper introduces ExplainBench, a benchmark that automatically evaluates how trustworthy the explanations generated by code‑writing LLM agents are, by checking if the explanatio…
Skills for the future software profession: beyond agentic AI!
Sungmin Kang, Baishakhi Ray, Abhik Roychoudhury
As coding agents are rapidly changing software engineering, a natural question is: what are the core skills needed by future software engineers? To identify where software engineer…
AutoCodeSherpa: Symbolic Explanations in AI Coding Agents
Sungmin Kang, Haifeng Ruan, Abhik Roychoudhury
Large language model (LLM) agents integrate external tools with one or more LLMs to accomplish specific tasks. Agents have rapidly been adopted by developers, and they are starting…
Finding the Needle in the Crash Stack: Industrial-Scale Crash Root Cause Localization with AutoCrashFL
Sungmin Kang, Sumi Yun, Jingun Hong +2
Fault Localization (FL) aims to identify root causes of program failures. FL typically targets failures observed from test executions, and as such, often involves dynamic analyses…
COSMosFL: Ensemble of Small Language Models for Fault Localisation
Hyunjoon Cho, Sungmin Kang, Gabin An +1
LLMs are rapidly being adopted to build powerful tools and agents for software engineering, but most of them rely heavily on extremely large closed-source models. This, in turn, ca…
Lachesis: Predicting LLM Inference Accuracy using Structural Properties of Reasoning Paths
Naryeong Kim, Sungmin Kang, Gabin An +1
Large Language Models are increasingly used to build agents to perform more complex tasks. As LLMs perform more complicated reasoning through longer interactions, self-consistency,…