Outputs (9)

Evidence-based Software Engineering Guidelines Revisited (2025)
Journal Article
Pfleeger, S. L., & Kitchenham, B. (2025). Evidence-based Software Engineering Guidelines Revisited. IEEE Transactions on Software Engineering, 51(3), 1-6. https://doi.org/10.1109/tse.2025.3526730

In 2002, the authors and their colleagues proposed some preliminary guidelines for empirical software engineering research. In this paper, we revisit them. We believe that for the purpose of supporting the development of project-based bespoke softwar... Read More about Evidence-based Software Engineering Guidelines Revisited.

Using rapid reviews to support software engineering practice: a systematic review and a replication study (2024)
Journal Article
Pizard, S., Lezama, J., García, R., Vallespir, D., & Kitchenham, B. (2025). Using rapid reviews to support software engineering practice: a systematic review and a replication study. Empirical Software Engineering, 30(1), https://doi.org/10.1007/s10664-024-10545-6

Context
A few years ago, rapid reviews (RR) were introduced in software engineering (SE) to address the problem that standard systematic reviews take too long and too much effort to be of value to practitioners. Prior to our study, few practice-driv... Read More about Using rapid reviews to support software engineering practice: a systematic review and a replication study.

Recommendations for analysing and meta-analysing small sample size software engineering experiments (2024)
Journal Article
Kitchenham, B., & Madeyski, L. (2024). Recommendations for analysing and meta-analysing small sample size software engineering experiments. Empirical Software Engineering, 29(6), Article 137. https://doi.org/10.1007/s10664-024-10504-1

Context: Software engineering (SE) experiments often have small sample sizes. This can result in data sets with non-normal characteristics, which poses problems as standard parametric meta-analysis, using the standardized mean difference (StdMD) effe... Read More about Recommendations for analysing and meta-analysing small sample size software engineering experiments.

The Importance of the Correlation in Crossover Experiments (2022)
Journal Article
Kitchenham, B., Madeyski, L., Scanniello, G., & Gravino, C. (2022). The Importance of the Correlation in Crossover Experiments. IEEE Transactions on Software Engineering, 48(8), 2802 - 2813. https://doi.org/10.1109/tse.2021.3070480

Context: In empirical software engineering, crossover designs are popular for experiments comparing software engineering techniques that must be undertaken by human participants. However, their value depends on the correlation ( r ) between the outco... Read More about The Importance of the Correlation in Crossover Experiments.

A longitudinal case study on the effects of an evidence-based software engineering training (2022)
Presentation / Conference Contribution
Pizard, S., Vallespir, D., & Kitchenham, B. (2022, May). A longitudinal case study on the effects of an evidence-based software engineering training. Presented at ICSE-SEET 2022, Pittsburgh, Pennsylvania, USA

Context: Evidence-based software engineering (EBSE) can be an effective resource to bridge the gap between academia and industry by balancing research of practical relevance and academic rigor. To achieve this, it seems necessary to investigate EBSE... Read More about A longitudinal case study on the effects of an evidence-based software engineering training.

SEGRESS: Software Engineering Guidelines for REporting Secondary Studies (2022)
Journal Article
Kitchenham, B., Madeyski, L., & Budgen, D. (in press). SEGRESS: Software Engineering Guidelines for REporting Secondary Studies. IEEE Transactions on Software Engineering, 49(3), 1273-1298. https://doi.org/10.1109/tse.2022.3174092

Training students in evidence-based software engineering and systematic reviews: a systematic review and empirical study (2021)
Journal Article
Pizard, S., Acerenza, F., Otegui, X., Moreno, S., Vallespir, D., & Kitchenham, B. (2021). Training students in evidence-based software engineering and systematic reviews: a systematic review and empirical study. Empirical Software Engineering, 26(3), Article 50. https://doi.org/10.1007/s10664-021-09953-9

Context Although influential in academia, evidence-based software engineering (EBSE) has had little impact on industry practice. We found that other disciplines have identified lack of training as a significant barrier to Evidence-Based Practice. Obj... Read More about Training students in evidence-based software engineering and systematic reviews: a systematic review and empirical study.

OECD Recommendation's draft concerning access to research data from public funding: A review (2021)
Journal Article
Madeyski, L., Lewowski, T., & Kitchenham, B. (2021). OECD Recommendation's draft concerning access to research data from public funding: A review. Bulletin of the Polish Academy of Sciences Technical Sciences, 69(1), Article e135401. https://doi.org/10.24425/bpasts.2020.135401

Sharing research data from public funding is an important topic, especially now, during times of global emergencies like the COVID-19 pandemic, when we need policies that enable rapid sharing of research data. Our aim is to discuss and review the rev... Read More about OECD Recommendation's draft concerning access to research data from public funding: A review.

Problems with Statistical Practice in Software Engineering Research (2019)
Presentation / Conference Contribution
Kitchenham, B., Madeyski, L., & Brereton, P. (2019, April). Problems with Statistical Practice in Software Engineering Research. Presented at EASE '19: Proceedings of the 23rd International Conference on Evaluation and Assessment in Software Engineering, Copenhagen, Denmark

Background
Examples of questionable statistical practice, when published in high quality software engineering (SE) journals, may lead to novice researchers adopting incorrect statistical practices.

Objective
Our goal is to highlight issues contr... Read More about Problems with Statistical Practice in Software Engineering Research.