Applying data analytics towards optimized issue management
Bibliographic record
Abstract
This document describes our experience of applying data analytics at Plexina, a leading IT company working in the healthcare domain. The main goal of the project was to identify factors currently affecting issue management and to make analytics based suggestions for optimizing the process. Various statistical and machine learning techniques were applied on a data set extracted from six releases of Plexina, containing more than 666 issues. Statistical techniques successfully identified the various factors that leads to estimation inaccuracy related to issues as well as identified the hidden relationships existing among various variables. The employed predictive analytic models was also successful to some extent, in predicting effort estimation related inaccuracy associated with the issues. The insights provided by the entire data analytics study can be of great help to product managers or the developers to make more informed decisions. In addition, the guidelines presented in this paper based on the lessons learnt can be applied to other data analytics and academia-industry collaboration project.
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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 | 0.019 | 0.087 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| 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".