Bibliographic record
Abstract
Many successful software projects do not follow the commonly assumed best practice of engineering well-formed requirements at project inception. Instead, the requirements are captured less formally, and only fully elaborated once the implementation begins, known as `just-in-time' requirements. Given the apparent disparity between best practices and actual practices, several questions arise. One concerns the nature of requirements engineering in non-traditional forms. What types of tools and practices are used? Another is formative: what types of problems are encountered in just-intime requirements, and how might we support organizations in solving those problems? In this paper we conduct separate case studies on the requirements practices of three open-source software projects. Using an individual task as the unit of analysis, we study how the project proceeds from requirement to implementation, in order to understand how each project manages requirements. We then comment on the benefits and problems of just-in-time requirements analysis. This allows us to propose research directions about requirements engineering in just-in-time settings. In particular, we see the need to better understand the context of practice, and the need to properly evaluate the cost of decisions. We propose a taxonomy to describe the requirements practices spectrum from fully formal to just-in-time.
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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 | 0.035 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".