On the usage of context for requirements elicitation: End-user involvement in IT ecosystems
Bibliographic record
Abstract
Today's systems are faced with the need of constant evolution to remain competitive, especially when looking at IT Ecosystems and their growing number of subsystems. As a prerequisite for these to stay competitive, system providers need a clear understanding of their stakeholder's needs. As systems tend to be increasingly complex nowadays, support an increasingly number of stakeholders, have a shorter release cycles to evolve and need to adapt to the environment and the users, some of the standard requirements elicitation techniques tend not to be suitable any more. Especially when adaptivity is necessary, system providers need to understand the context, in which the systems are used, but also the context of users for the adaptation. In this paper I concentrate on the largest stakeholder group, namely the end-users for requirements elicitation. Evaluation criteria include (i) support of context, (ii) scalability to large numbers of end-users, and (iii) scalability to large numbers of end-user's needs and problems that lead to new requirements. My literature review suggests that this important field is currently underrepresented in Requirements Engineering research. This research proposes to develop a framework that explains the different context types and their role for requirements elicitation. The framework is then used to investigate existing requirements elicitation techniques and their potential for considering context. It is also used to show how emerging techniques can further support requirements elicitation with context.
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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 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".