Preliminary Analysis of the Effects of Pair Programming and Test-Driven Development on the External Code Quality. - Lech Madeyski (Software Engineering: Evolution and Emerging Technologies, 2005)

The Impact of Pair Programming and Test-Driven Development on Package Dependencies in Object-Oriented Design - An Experiment. - Lech Madeyski (PROFES, 2006)

Is External Code Quality Correlated with Programming Experience or Feelgood Factor? - Lech Madeyski (XP, 2006)

The Impact of Test-Driven Development on Software Development Productivity - An Empirical Study. - Lech Madeyski; Lukasz Szala (EuroSPI, 2007)

Capable Leader and Skilled and Motivated Team Practices to Introduce eXtreme Programming. - Lech Madeyski; Wojciech Biela (CEE-SET, 2007)

On the Effects of Pair Programming on Thoroughness and Fault-Finding Effectiveness of Unit Tests. - Lech Madeyski (PROFES, 2007)

Empirical Evidence Principle and Joint Engagement Practice to Introduce XP. - Lech Madeyski; Wojciech Biela (XP, 2007)

Empirical Evaluation of Novel Approaches to Software Engineering. - Zbigniew Huzar; Lech Madeyski (e Informatica Softw. Eng. J., 2007)

Impact of aspect-oriented programming on software development efficiency and design quality: an empirical study. - Lech Madeyski; Lukasz Szala (IET Softw., 2007)

Impact of pair programming on thoroughness and fault detection effectiveness of unit test suites. - Lech Madeyski (Softw. Process. Improv. Pract., 2008)

Test-Driven Development - An Empirical Evaluation of Agile Practice. - Lech Madeyski (Springer, 2010)

Towards identifying software project clusters with regard to defect prediction. - Marian Jureczko; Lech Madeyski (PROMISE, 2010)

Editorial. - Zbigniew Huzar; Lech Madeyski (e Informatica Softw. Eng. J., 2010)

Judy - a mutation testing tool for Java. - Lech Madeyski; N. Radyk (IET Softw., 2010)

The impact of Test-First programming on branch coverage and mutation score indicator of unit tests: An experiment. - Lech Madeyski (Inf. Softw. Technol., 2010)

Continuous Test-Driven Development - A Novel Agile Software Development Practice and Supporting Tool. - Lech Madeyski; Marcin Kawalerowicz (ENASE, 2013)

Problems of Mutation Testing and Higher Order Mutation Testing. - Quang Vu Nguyen 001; Lech Madeyski (ICCSAMA, 2014)

Overcoming the Equivalent Mutant Problem: A Systematic Literature Review and a Comparative Experiment of Second Order Mutation. - Lech Madeyski; Wojciech Orzeszyna; Richard Torkar; Mariusz Jozala (IEEE Trans. Software Eng., 2014)

Searching for Strongly Subsuming Higher Order Mutants by Applying Multi-objective Optimization Algorithm. - Quang Vu Nguyen 001; Lech Madeyski (ICCSAMA, 2015)

Cross-Project Defect Prediction With Respect To Code Ownership Model: An Empirical Study. - Marian Jureczko; Lech Madeyski (e Informatica Softw. Eng. J., 2015)

Which process metrics can significantly improve defect prediction models? An empirical study. - Lech Madeyski; Marian Jureczko (Softw. Qual. J., 2015)

Higher Order Mutation Testing to Drive Development of New Test Cases: An Empirical Comparison of Three Strategies. - Quang Vu Nguyen 001; Lech Madeyski (ACIIDS (1), 2016)

On the Relationship Between the Order of Mutation Testing and the Properties of Generated Higher Order Mutants. - Quang Vu Nguyen 001; Lech Madeyski (ACIIDS (1), 2016)

Assessment of the Software Defect Prediction Cost Effectiveness in an Industrial Project. - Jaroslaw Hryszko; Lech Madeyski (KKIO Software Engineering Conference, 2016)

Software Engineering Needs Agile Experimentation: A New Practice and Supporting Tool. - Lech Madeyski; Marcin Kawalerowicz (KKIO Software Engineering Conference, 2016)

Empirical Evaluation of Multiobjective Optimization Algorithms Searching for Higher Order Mutants. - Quang Vu Nguyen 001; Lech Madeyski (Cybern. Syst., 2016)

Continuous Test-Driven Development: A Preliminary Empirical Evaluation Using Agile Experimentation in Industrial Settings. - Lech Madeyski; Marcin Kawalerowicz (KKIO Software Engineering Conference, 2017)

Continuous defect prediction: the idea and a related dataset. - Lech Madeyski; Marcin Kawalerowicz (MSR, 2017)

Defect prediction with bad smells in code. - Jaroslaw Hryszko; Lech Madeyski; Marta Dabrowska; Piotr Konopka (CoRR, 2017)

Software Metrics in Boa Large-Scale Software Mining Infrastructure: Challenges and Solutions. - Agnieszka Patalas; Wojciech Cichowski; Michal Malinka; Wojciech Stepniak; Piotr Mackowiak; Lech Madeyski (CoRR, 2017)

Robust Statistical Methods for Empirical Software Engineering. - Barbara A. Kitchenham; Lech Madeyski; David Budgen; Jacky Keung; Pearl Brereton; Stuart M. Charters; Shirley Gibbs; Amnart Pohthong (Empir. Softw. Eng., 2017)

Would wider adoption of reproducible research be beneficial for empirical software engineering research? - Lech Madeyski; Barbara A. Kitchenham (J. Intell. Fuzzy Syst., 2017)

Addressing mutation testing problems by applying multi-objective optimization algorithms and higher order mutation. - Quang Vu Nguyen 001; Lech Madeyski (J. Intell. Fuzzy Syst., 2017)

Effect sizes and their variance for AB/BA crossover design studies. - Lech Madeyski; Barbara A. Kitchenham (Empir. Softw. Eng., 2018)

Introduction to the special section on Enhancing Credibility of Empirical Software Engineering. - Lech Madeyski; Barbara A. Kitchenham; Krzysztof Wnuk (Inf. Softw. Technol., 2018)

Problems with Statistical Practice in Human-Centric Software Engineering Experiments. - Barbara A. Kitchenham; Lech Madeyski; Pearl Brereton (EASE, 2019)

Empirical evaluation of continuous test-driven development in industrial settings. - Lech Madeyski; Marcin Kawalerowicz (J. Intell. Fuzzy Syst., 2019)

MLCQ: Industry-Relevant Code Smell Data Set. - Lech Madeyski; Tomasz Lewowski (EASE, 2020)

Technical Debt Aware Estimations in Software Engineering: A Systematic Mapping Study. - Pawel Klimczyk; Lech Madeyski (e Informatica Softw. Eng. J., 2020)

Meta-analysis for families of experiments in software engineering: a systematic review and reproducibility and validity assessment. - Barbara A. Kitchenham; Lech Madeyski; Pearl Brereton (Empir. Softw. Eng., 2020)

Code Smell Prediction Employing Machine Learning Meets Emerging Java Language Constructs. - Hanna Grodzicka; Arkadiusz Ziobrowski; Zofia Lakomiak; Michal Kawa; Lech Madeyski (Data-Centric Business and Applications, 2020)

Software Defect Prediction Using Bad Code Smells: A Systematic Literature Review. - Pawel Piotrowski; Lech Madeyski (Data-Centric Business and Applications, 2020)

Creating Evolving Project Data Sets in Software Engineering. - Tomasz Lewowski; Lech Madeyski (Integrating Research and Practice in Software Engineering, 2020)

ASH: A New Tool for Automated and Full-Text Search in Systematic Literature Reviews. - Marek Sosnicki; Lech Madeyski (ICCS (3), 2021)

Security and Scalability in Private Permissionless Blockchain: Problems and Solutions Leading to Creating Consent-as-a-Service (CaaS) Deployment. - Hanna Grodzicka; Michal Kedziora; Lech Madeyski (ICCCI (CCIS Volume), 2021)

Continuous Build Outcome Prediction: A Small-N Experiment in Settings of a Real Software Project. - Marcin Kawalerowicz; Lech Madeyski (IEA/AIE (2), 2021)

Jaskier: A Supporting Software Tool for Continuous Build Outcome Prediction Practice. - Marcin Kawalerowicz; Lech Madeyski (IEA/AIE (2), 2021)

Cloud Solutions for Private Permissionless Blockchain Deployment. - Hanna Grodzicka; Michal Kedziora; Lech Madeyski (Comput. Informatics, 2021)

How far are we from reproducible research on code smell detection? A systematic literature review. - Tomasz Lewowski; Lech Madeyski (Inf. Softw. Technol., 2022)

The Importance of the Correlation in Crossover Experiments. - Barbara A. Kitchenham; Lech Madeyski; Giuseppe Scanniello; Carmine Gravino (IEEE Trans. Software Eng., 2022)

Can we Knapsack Software Defect Prediction? Nokia 5G Case. - Szymon Stradowski; Lech Madeyski (ICSE Companion, 2023)

Bridging the Gap Between Academia and Industry in Machine Learning Software Defect Prediction: Thirteen Considerations. - Szymon Stradowski; Lech Madeyski (ASE, 2023)

Detecting code smells using industry-relevant data. - Lech Madeyski; Tomasz Lewowski (Inf. Softw. Technol., 2023)

Exploring the challenges in software testing of the 5G system at Nokia: A survey. - Szymon Stradowski; Lech Madeyski (Inf. Softw. Technol., 2023)

Machine learning in software defect prediction: A business-driven systematic mapping study. - Szymon Stradowski; Lech Madeyski (Inf. Softw. Technol., 2023)

How Should Software Engineering Secondary Studies Include Grey Material? - Barbara A. Kitchenham; Lech Madeyski; David Budgen (IEEE Trans. Software Eng., 2023)

SEGRESS: Software Engineering Guidelines for REporting Secondary Studies. - Barbara A. Kitchenham; Lech Madeyski; David Budgen (IEEE Trans. Software Eng., 2023)

Continuous build outcome prediction: an experimental evaluation and acceptance modelling. - Marcin Kawalerowicz; Lech Madeyski (Appl. Intell., 2023)

Industrial applications of software defect prediction using machine learning: A business-driven systematic literature review. - Szymon Stradowski; Lech Madeyski (Inf. Softw. Technol., 2023)

An Empirical Analysis of the Usage of Requirements Attributes in Requirements Engineering Research and Practice. - Krzysztof Wnuk; Lech Madeyski; Waleed Abdeen; Sneha Penmetsa; Navya Lingampalli (ICCCI (2), 2024)

Costs and Benefits of Machine Learning Software Defect Prediction: Industrial Case Study. - Szymon Stradowski; Lech Madeyski (SIGSOFT FSE Companion, 2024)

The impact of hard and easy negative training data on vulnerability prediction performance. - Fahad Al Debeyan; Lech Madeyski; Tracy Hall; David Bowes (J. Syst. Softw., 2024)

Recommendations for analysing and meta-analysing small sample size software engineering experiments. - Barbara A. Kitchenham; Lech Madeyski (Empir. Softw. Eng., 2024)

Emerging Results in Using Explainable AI to Improve Software Vulnerability Prediction. - Fahad Al Debeyan; Tracy Hall; Lech Madeyski (SIGSOFT FSE Companion, 2025)

Predicting test failures induced by software defects: A lightweight alternative to software defect prediction and its industrial application. - Lech Madeyski; Szymon Stradowski (J. Syst. Softw., 2025)

"Your AI is impressive, but my code does not have any bugs" managing false positives in industrial contexts. - Szymon Stradowski; Lech Madeyski (Sci. Comput. Program., 2025)

Interpretability/Explainability Applied to Machine Learning Software Defect Prediction: An Industrial Perspective. - Szymon Stradowski; Lech Madeyski (IEEE Softw., 2025)