Bug detection is central to software engineering, but what motivates programmers to perform as optimally as possible? Despite a long history of economic experiments on incentivisation, there is surprisingly little research on how different incentives shape software engineering performance.

 

In the current study, Bershadskyy et al. (2024) undertook an experiment to evaluate how the pay-off functions associated with different financial incentives influence the performance of participants in identifying bugs during code review. The authors hypothesised that performance-based incentivisation would result in higher average performance, as defined using the F1-score, and that different incentivisation schemes may also differ in their effectiveness.

 

The results did not support the preregistered predictions, with no statistically significant differences in F1-score observed between groups that received performance-based incentives compared to a control group that received no incentive. Exploratory analyses suggested some potential trends of interest, but the main implication of this work is methodological: that experiments in this field require substantially larger sample sizes to provide definitive tests. The current work is valuable in providing a novel unbiased insight on the magnitude of this challenge, which is now primed for further investigation.

 

The Stage 2 manuscript was evaluated over one round of in-depth review. Based on detailed responses to the recommender and reviewer's comments, the recommender judged that the manuscript met the Stage 2 criteria and awarded a positive recommendation.
 
URL to the preregistered Stage 1 protocol: https://osf.io/s36c2

 

Level of bias control achieved: Level 6. No part of the data or evidence that was used to answer the research question was generated until after IPA. 
 
List of eligible PCI RR-friendly journals:

 

 

 

Bershadskyy, D., Krüger, J., Çalıklı, G., Siegmar, O., Zabel, S., Greif, J. and Heyer, R. (2024). A Laboratory Experiment on Using Different Financial-Incentivization Schemes in Software-Engineering Experimentation. Acceptance of Version 8 by Peer Community in Registered Reports. https://arxiv.org/pdf/2202.10985

Bug detection is central to software engineering, but what motivates programmers to perform as optimally as possible? Despite a long history of economic experiments on incentivisation, there is surprisingly little research on how different incentives shape software engineering performance. In the current study, Krüger et al. (2022) propose an experiment to evaluate how the pay-off functions associated with different financial incentives influence the performance of participants in identifying bugs during code review. The authors hypothesise that performance-based incentivisation will result in higher average performance, as defined using the F1-score, and that different incentivisation schemes may also differ in their effectiveness. As well as testing confirmatory predictions, the authors will explore a range of ancillary strands, including how the different incentivisation conditions influence search and evaluation behaviour (using eye-tracking), and the extent to which any effects are moderated by demographic factors.

 

The Stage 1 manuscript was evaluated over one round of in-depth review. Based on detailed responses to the recommender and reviewers' comments, the recommender judged that the manuscript met the Stage 1 criteria and therefore awarded in-principle acceptance (IPA).
 
URL to the preregistered Stage 1 protocol: https://osf.io/s36c2

 

Level of bias control achieved: Level 6. No part of the data or evidence that will be used to answer the research question yet exists and no part will be generated until after IPA. 
 
List of eligible PCI RR-friendly journals:

 

 

 

1. Krüger, J., Çalıklı, G., Bershadskyy, D., Heyer, R., Zabel, S. & Siegmar, O. (2022). Registered Report: A Laboratory Experiment on Using Different Financial-Incentivization Schemes in Software-Engineering Experimentation, in principle acceptance of Version 3 by Peer Community in Registered Reports. https://osf.io/s36c2