Situating requirements engineering methods within design science research
Bibliographic record
Abstract
Design Science Research Methodologies (DSRM) are increasingly used to guide research in fields beyond Information Systems, in particular those of Requirements Engineering and Software Engineering (RE/SE). While a number of DSR methodologies have been developed by scholars in the RE/SE fields, there remains a certain level of confusion about the way in which the aim and scope of DSRM and those of methods typically used in RE/SE differ. This issue can be observed in graduate students' work as well as in published literature. In particular, the difference be-tween the research orientation of DSRM and the solution orientation of RE/SE methods can be difficult to navigate. We propose to address this challenge by situating three RE/SE methodologies proposed in published literature within one common DSRM; doing so clarifies the scope of these methodologies and highlights ways in which the knowledge contributions of their results could be further enhanced. This effort is a first step towards providing better guidance to researchers who are new to design science research in order to ensure that recognized DSR principles are promoted and respected.
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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 | 0.233 | 0.181 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.019 | 0.014 |
| Science and technology studies | 0.005 | 0.032 |
| Scholarly communication | 0.028 | 0.029 |
| Open science | 0.007 | 0.014 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".