Ten Heuristics from Applying Agile Practices across Different Distribution Scenarios: A Multiple-Case Study
Bibliographic record
Abstract
Distributed software development (DSD) has become increasingly popular due to benefits such as cost savings, access to large multi-skilled workforces and a reduced time to market. Agile practices can potentially help increase transparency and mitigate communication and coordination issues in these complex environments. While empirical studies in the field exist, most are single-case studies that miss out on the chance to compare different distribution scenarios, which calls for further investigation. We report on results of a four-year exploratory multiple-case study investigating the agile process implementation in three different distribution scenarios: within-city, within-country and within-continent. We purposefully selected the three different cases and found ten common heuristics emerge which are based on empirical evidence in at least two cases as well as four further candidate heuristics that lack evidence in more than one case. In particular, the understanding of and adaptation to each development site's inherent challenges, travelling ambassadors/proxies between sites, and a balanced distribution of decision makers proved to be important heuristics for a successful process implementation.
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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 | 0.026 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.004 |
| 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".