How social interactions can affect Modern Code Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Introduction Modern Code Review (MCR) is a multistage process where developers evaluate source code written by others to enhance the software quality. Despite the numerous studies conducted on the effects of MCR on software quality, the non-technical issues in the MCR process have not been extensively studied. This study aims to investigate the social problems in the MCR process and to find possible ways to prevent them and improve the overall quality of the MCR process. Methodology To achieve the research objectives, we applied the grounded theory research shaped by GQM approach to collect data on the attitudes of developers from different teams toward MCR. We conducted interviews with 25 software developers from 13 companies to obtain the information necessary to investigate how social interactions affect the code reviewing process. Results Our findings show that interpersonal relationships within the team can have significant consequences on the MCR process. We also received a list of possible strategies to overcome these problems. Discussion Our study provides a new perspective on the non-technical issues in the MCR process, which has not been extensively studied before. The findings of this study can help software development teams to address the social problems in the MCR process and improve the overall quality of their software products. Conclusion This study provides valuable insights into the non-technical issues in the MCR process and the possible ways to prevent them. The findings of this study can help software development teams to improve the MCR process and the quality of their software products. Future research could explore the effectiveness of the identified strategies in addressing the social problems in the MCR process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it