The Lack of Shared Understanding of Non-Functional Requirements in\n Continuous Software Engineering: Accidental or Essential?
Bibliographic record
Abstract
Building shared understanding of requirements is key to ensuring downstream\nsoftware activities are efficient and effective. However, in continuous\nsoftware engineering (CSE) some lack of shared understanding is an expected,\nand essential, part of a rapid feedback learning cycle. At the same time, there\nis a key trade-off with avoidable costs, such as rework, that come from\naccidental gaps in shared understanding. This trade-off is even more\nchallenging for non-functional requirements (NFRs), which have significant\nimplications for product success. Comprehending and managing NFRs is especially\ndifficult in small, agile organizations. How such organizations manage shared\nunderstanding of NFRs in CSE is understudied. We conducted a case study of\nthree small organizations scaling up CSE to further understand and identify\nfactors that contribute to lack of shared understanding of NFRs, and its\nrelationship to rework. Our in-depth analysis identified 41 NFR-related\nsoftware tasks as rework due to a lack of shared understanding of NFRs. Of\nthese 41 tasks 78% were due to avoidable (accidental) lack of shared\nunderstanding of NFRs. Using a mixed-methods approach we identify factors that\ncontribute to lack of shared understanding of NFRs, such as the lack of domain\nknowledge, rapid pace of change, and cross-organizational communication\nproblems. We also identify recommended strategies to mitigate lack of shared\nunderstanding through more effective management of requirements knowledge in\nsuch organizations. We conclude by discussing the complex relationship between\nshared understanding of requirements, rework and, CSE.\n
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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 | 0.050 | 0.175 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".