Designing and implementing a measurement program for Scrum teams
Bibliographic record
Abstract
Agile developers are generally reluctant to non-agile practices. Promoted by senior software practitioners, agile methods were intended to avoid traditional engineering practices and rather focus on delivering working software as quickly as possible. Thus, the unique measure in Scrum, a well known framework for managing agile projects, is velocity. Its main purpose is to demonstrate the progress in delivering working software. In software engineering (SE), measurement programs have more in depth purposes and allow teams and individuals to improve their development process along with providing better product quality and control over the project. This paper will describe the experience and the approach used in an agile SE company to design and initiate a measurement program taking into account the specificities of their agile environment, principles and values. The lessons learned after five months of investigation are twofold. The first one shows how agile teams, in comparison to traditional teams, have different needs when trying to establish a measurement program. The second confirms that agile teams, as many other groups of workers, are reluctant and resistant to change. Finally, the preliminary results show that agile people are more interested in value delivery, technical debt, and multiple aspects related to team dynamics and will cooperate to the collection of data as soon as there tools can do it for them. It is believed that this research could suggest new guidelines for elaborating specific measurement programs in other agile environments.
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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 | 0.044 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".