Developers want requirements, but their project manager doesn't; and a possibly transcendent Hawthorne effect
Bibliographic record
Abstract
This paper reports the results of a case study conducted in July 2010 of one industrial software development project to determine how the project's lack of any explicit requirements gathering process affected the project's development and the product that it produced. The study reveals that the lack of any requirements gathering process apparently led to missing functions in the product, reduced productivity among the project's members, and poor cost estimation. This lack converted a potentially profitable project into a liability. In the end, the project members completed the product, but much time was wasted. A requirements specification could have saved this time. Conducting the case study appears to have resulted in an increased awareness among the study's subjects, i.e., the project's manager and members, that a requirements engineering process was needed. This awareness apparently led to a Hawthorne effect, in which the project manager and members improved their requirements process. The next project conducted by the project manager was begun with an explicit requirements gathering process. This improved process continued through at least the end of July 2011, 12 months after completion of the study.
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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 | 0.014 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".