Supporting Acceptance Testing in Distributed Software Projects with Integrated Feedback Systems: Experiences and Requirements
Bibliographic record
Abstract
Abstract—During acceptance testing customers assess whether a system meets their expectations and often identify issues that should be improved. These findings have to be communicated to the developers – a task we observed to be error prone, especially in distributed teams. Here, it is normally not possible to have developer representatives from every site attend the test. Developers who were not present might misunderstand insufficiently documented findings. This hinders fixing the issues and endangers customer satisfaction. Integrated feedback systems promise to mitigate this problem. They allow to easily capture findings and their context. Correctly applied, this technique could improve feedback, while reducing customer effort. This paper collects our experiences from comparing acceptance testing with and without feedback systems in a distributed project. Our results indicate that this technique can improve acceptance testing – if certain requirements are met. We identify key requirements feedback systems should meet to support acceptance testing. Keywords-distributed software development; requirements engineering; acceptance testing I.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".