9 papers · 1 filter
Beyond Blind Spots: Analytic Hints for Mitigating LLM-Based Evaluation Pitfalls
Ora Nova Fandina, Eitan Farchi, Shmulik Froimovich +4
Large Language Models are increasingly deployed as judges (LaaJ) in code generation pipelines. While attractive for scalability, LaaJs tend to overlook domain specific issues raisi…
Enhancing Formal Software Specification with Artificial Intelligence
Antonio Abu Nassar, Eitan Farchi
Formal software specification is known to enable early error detection and explicit invariants, yet it has seen limited industrial adoption due to its high notation overhead and th…
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 (…
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…
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…