Improved Bayesian Networks for Software Project Risk Assessment Using Dynamic Discretisation. - Norman E. Fenton; Lukasz Radlinski; Martin Neil (SET, 2006)

Project Data Incorporating Qualitative Factors for Improved Software Defect Prediction. - Norman E. Fenton; Martin Neil; William Marsh 001; Peter Stewart Hearty; Lukasz Radlinski; Paul Krause (PROMISE@ICSE, 2007)

On the effectiveness of early life cycle defect prediction with Bayesian Nets. - Norman E. Fenton; Martin Neil; William Marsh 001; Peter Stewart Hearty; Lukasz Radlinski; Paul Krause (Empir. Softw. Eng., 2008)

Improved Software Project Risk Assessment Using Bayesian Nets. - Lukasz Radlinski (2008)

Defect cost flow model: a Bayesian network for predicting defect correction effort. - Thomas Schulz; Lukasz Radlinski; Thomas Gorges; Wolfgang Rosenstiel (PROMISE, 2010)

Software Development Effort and Quality Prediction Using Bayesian Nets and small Local Qualitative Data. - Lukasz Radlinski (SEKE, 2010)

A Framework for Integrated Software Quality Prediction Using Bayesian Nets. - Lukasz Radlinski (ICCSA (5), 2011)

A conceptual Bayesian net model for integrated software quality prediction. - Lukasz Radlinski (Ann. UMCS Informatica, 2011)

Empirical Analysis of the Impact of Requirements Engineering on Software Quality. - Lukasz Radlinski (REFSQ, 2012)

Predicting the Flow of Defect Correction Effort using a Bayesian Network Model. - Thomas Schulz; Lukasz Radlinski; Thomas Gorges; Wolfgang Rosenstiel (Empir. Softw. Eng., 2013)

From complex questionnaire and interviewing data to intelligent Bayesian network models for medical decision support. - Anthony Costa Constantinou; Norman E. Fenton; William Marsh 001; Lukasz Radlinski (Artif. Intell. Medicine, 2016)

Predicting User Satisfaction in Software Projects using Machine Learning Techniques. - Lukasz Radlinski (ENASE, 2020)

Stability of user satisfaction prediction in software projects. - Lukasz Radlinski (KES, 2020)

Analysis of factors of software development effort and productivity. - Lukasz Radlinski (KES, 2021)

CLARIN-Emo: Training Emotion Recognition Models Using Human Annotation and ChatGPT. - Bartlomiej Koptyra; Anh Ngo; Lukasz Radlinski; Jan Kocon (ICCS (1), 2023)

ChatGPT: Jack of all trades, master of none. - Jan Kocon; Igor Cichecki; Oliwier Kaszyca; Mateusz Kochanek; Dominika Szydlo; Joanna Baran; Julita Bielaniewicz; Marcin Gruza; Arkadiusz Janz; Kamil Kanclerz; Anna Kocon; Bartlomiej Koptyra; et al. (Inf. Fusion, 2023)

The Trade-off Between Data Volume and Quality in Predicting User Satisfaction in Software Projects. - Lukasz Radlinski (SEAA, 2024)

Backtranslation and Paraphrasing in the LLM Era? Comparing Data Augmentation Methods for Emotion Classification. - Lukasz Radlinski; Mateusz Gusciora; Jan Kocon (ICCS (1), 2025)

Leveraging Positional Bias of LLM In-Context Learning with Class-Few-Shot and Maj-Min Alternating Ordering. - Aleksander Szczesny; Maciej Markiewicz; Lukasz Radlinski; Przemyslaw Kazienko (ICCS (4), 2025)