Job rotation in software engineering : theory and practice
Bibliographic record
Abstract
Job rotation has been proposed as a managerial practice to be applied in the organizational environment to reduce job monotony, boredom, and exhaustion resulting from job simplification, specialization, and repetition. The scientific literature distinguishes between job-to-job and project-to-project rotations. Despite the potential benefits and its actual use by software companies, software engineering research did not accumulate an extensive body of scientific knowledge about benefits and limitations of job rotation in software engineering practice. In fact, there is a known knowledge gap regarding how practitioners can apply this practice in software industry. This research aims to identify and discuss evidence about project-to-project (P2P) job rotation in software companies, seeking to understand its benefits and limitations, in order to build a model that could guide research and practice towards the use of this managerial practice in software development environments. A mix-method research strategy was applied to collect, analyze, and synthesize empirical evidence in order to build and validate a consistent model that could be applied to guide industry practice. This research identified evidence from multiple sources and from different data types (qualitative and quantitative) about the use, benefits and limitations of rotation in software engineering practice. An amount of 25 factors (benefits and limitations) of such rotations in software engineering were identified and discussed. Different research methods yielded complementary evidence that could be used to inform practitioners about the effects of this managerial practice in software professionals’ work. Finally, a managerial model was build and its comprehensiveness was checked in order to be applied in software companies in the process of plan, execute and evaluate job rotations. Before this research, evidence related to job rotations in Software Engineering was restricted to studies that did not investigate this phenomenon as their primary goals. Now, relevant novel evidence and significant findings based on practice were added to the body of knowledge about this specific topic, supporting researchers into the development of future research about the theme, and guiding practitioners into the improvement industry practice.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.005 | 0.024 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".