Communicating Study Design Trade-offs in Software Engineering
Bibliographic record
Abstract
Reflecting on the limitations of a study is a crucial part of the research process. In software engineering studies, this reflection is typically conveyed through discussions of study limitations or threats to validity. In current practice, such discussions seldom provide sufficient insight to understand the rationale for decisions taken before and during the study, and their implications. We revisit the practice of discussing study limitations and threats to validity and identify its weaknesses. We propose to refocus this practice of self-reflection to a discussion centered on the notion of trade-offs . We argue that documenting trade-offs allows researchers to clarify how the benefits of their study design decisions outweigh the costs of possible alternatives. We present guidelines for reporting trade-offs in a way that promotes a fair and dispassionate assessment of researchers’ work.
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.
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 | 0.907 | 0.942 |
| Meta-epidemiology (narrow) | 0.004 | 0.006 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.013 | 0.008 |
| Science and technology studies | 0.012 | 0.048 |
| Scholarly communication | 0.027 | 0.044 |
| Open science | 0.010 | 0.035 |
| Research integrity | 0.025 | 0.027 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".