1 citations · 1 across the 5 of their papers we have counts for
7 papers · 1 filter
Preliminary Guidelines for Using and Evaluating GenAI Tools to Support Systematic Literature Reviews
Barbara Kitchenham, Sebastián Pizard, Lech Madeyski +3
Context: Generative AI (GenAI) and Large Language Models (LLMs) are increasingly used for academic tasks in software engineering and beyond, including systematic literature reviews…
Triage: Routing Software Engineering Tasks to Cost-Effective LLM Tiers via Code Quality Signals
Lech Madeyski
Context: AI coding agents route every task to a single frontier large language model (LLM), paying premium inference cost even when many tasks are routine. Objectives: We propose T…
Test Case Prioritization: A Snowballing Literature Review and TCPFramework with Approach Combinators
Tomasz Chojnacki, Lech Madeyski
Context: Test case prioritization (TCP) is a technique widely used by software development organizations to accelerate regression testing. Objectives: We aim to systematize existin…
How Software Engineering Research Overlooks Local Industry: A Smaller Economy Perspective
Klara Borowa, Andrzej Zalewski, Lech Madeyski
The software engineering researchers from countries with smaller economies, particularly non-English speaking ones, represent valuable minorities within the software engineering co…
LLM4SCREENLIT: Recommendations on Assessing the Performance of Large Language Models for Screening Literature in Systematic Reviews
Lech Madeyski, Barbara Kitchenham, Martin Shepperd
Context: Large language models (LLMs) are increasingly used to screen literature for systematic reviews (SRs), but the standard confusion-matrix metrics used to evaluate them can m…
Defect prediction with bad smells in code
Jarosław Hryszko, Lech Madeyski, Marta Dąbrowska +1
Background: Defect prediction in software can be highly beneficial for development projects, when prediction is highly effective and defect-prone areas are predicted correctly. One…