8 citations · 32 across the 31 of their papers we have counts for
7 papers · 1 filter
Technique to Baseline QE Artefact Generation Aligned to Quality Metrics
Eitan Farchi, Kiran Nayak, Papia Ghosh Majumdar +1
Large Language Models (LLMs) are transforming Quality Engineering (QE) by automating the generation of artefacts such as requirements, test cases, and Behavior Driven Development (…
An Agent-Based Framework for the Automatic Validation of Mathematical Optimization Models
Alexander Zadorojniy, Segev Wasserkrug, Eitan Farchi
Recently, using Large Language Models (LLMs) to generate optimization models from natural language descriptions has became increasingly popular. However, a major open question is h…
Vintage Code, Modern Judges: Meta-Validation in Low Data Regimes
Ora Nova Fandina, Gal Amram, Eitan Farchi +6
Application modernization in legacy languages such as COBOL, PL/I, and REXX faces an acute shortage of resources, both in expert availability and in high-quality human evaluation d…
Automated Validation of LLM-based Evaluators for Software Engineering Artifacts
Ora Nova Fandina, Eitan Farchi, Shmulik Froimovich +4
Automation in software engineering increasingly relies on large language models (LLMs) to generate, review, and assess code artifacts. However, establishing LLMs as reliable evalua…
LaajMeter: A Framework for LaaJ Evaluation
Samuel Ackerman, Gal Amram, Ora Nova Fandina +5
Large Language Models (LLMs) are increasingly used as evaluators in natural language processing tasks, a paradigm known as LLM-as-a-Judge (LaaJ). The analysis of a LaaJ software, c…
Black-Box Bug-Amplification for Multithreaded Software
Yeshayahu Weiss, Gal Amram, Achiya Elyasaf +3
Bugs, especially those in concurrent systems, are often hard to reproduce because they manifest only under rare conditions. Testers frequently encounter failures that occur only un…