To group or not to group? Group sizes for requirements elicitation
Bibliographic record
Abstract
Requirement elicitation can be done by individuals or by groups. Computer-based system development life-cycle models suggest having people working together for many steps. Also, recommendations about analysis and design methods indicate that some processes could take advantage of group work. In requirements engineering, groups are suggested for requirements elicitation. From the software and the requirements engineering viewpoints, and in turn for companies, a relevant overall research question is “What is a suitable size for a requirements elicitation group?” Our goal was to answer this question, first by looking for available guidelines in textbooks and secondly by investigating requirements elicitation in companies. To address the research question, we conducted two studies. The first was a review of most widely adopted software and requirements engineering textbooks. The second was a study aimed at identifying factors affecting group size for requirements elicitation, based on an online questionnaire submitted to professional analysts. The review of the textbooks showed that very few give advice on the number of analysts to involve in requirements elicitation sessions. When they do, guidelines are quite general and not supported by empirical data. According to data gathered from the questionnaire, most companies use and suggest using small groups. Data also allowed identifying four categories of factors useful to make decisions about requirements elicitation group sizes: people, relation, project, and output. Both the textbook review and the data from the questionnaire say that it is better to aim for small groups than to have individual analysts working separately. The ideal number of analysts for a requirements elicitation session appears to be 2, but large groups are necessary in some cases. Factors in all the four categories have to be considered in deciding the size of groups.
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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 | 0.071 | 0.306 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".