Publish or Perish: Questioning the Impact of Our Research on the Software Developer
Bibliographic record
Abstract
How often do we pause to consider how we, as a community, decide which developer problems we address, or how well we are doing at evaluating our solutions within real development contexts? Many of our research contributions in software engineering can be considered as purely technical. Yet somewhere, at some time, a software developer may be impacted by our research. In this talk, I invite the community to question the impact of our research on software developer productivity. To guide the discussion, I first paint a picture of the modern-day developer and the challenges they experience. I then present 4+1 views of software engineering research - views that concern research context, method choice, research paradigms, theoretical knowledge and real-world impact. I demonstrate how these views can be used to design, communicate and distinguish individual studies, but also how they can be used to compose a critical perspective of our research at a community level. To conclude, I propose structural changes to our collective research and publishing activities - changes to provoke a more expeditious consideration of the many challenges facing today's software developer.
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 | 0.363 | 0.624 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.024 | 0.073 |
| Scholarly communication | 0.066 | 0.117 |
| Open science | 0.008 | 0.025 |
| Research integrity | 0.019 | 0.027 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".