How can Open Source Software Development Help Requirements Management Gain the Potential of Open Innovation: An Exploratory Study
Bibliographic record
Abstract
A key component in successfully managing software products is to properly, and in a timely manner, identify and secure competitive advantage by innovation via feature differentiation. Although open source software (OSS) is not a new idea, several product development companies that operate in a market-driven context have started to use open source solutions as core software components in their products. Adopting open source core components implies a lower degree of control over software development and increased business risk associated with integrating differentiating contributions into the core release stream. Whether and how to adjust the current requirements management practices after the adoption of OSS components to fully benefit from the concept of open innovation has not yet been empirically explored. We outline experiences and challenges related to leveraging open innovation via engaging in OSS identified during 19 interviews with practitioners occupying different roles in the requirements management process at a large company followed by four validation interviews with other practitioners. We then propose a research agenda for requirements and decision management in the open innovation context and suggest which challenges in requirements engineering open innovation affects.
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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 | 0.019 | 0.064 |
| 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 | 0.004 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 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".