Analytics for Software Project Management -- Where are We and Where do We Go?
Bibliographic record
Abstract
Software project management is a decision intensive process. Success or failure of the project is highly dependent on these decisions. Analytical techniques and tools can support project managers throughout the software project life cycle by increasing the predictability and chance of success in these projects. In this paper, we report the results of a systematic mapping study within which we investigate the usage of different types of analytics for software project management. We analyze the accessibility of the data as well as the degree of validation reported in the 115 studies selected for final analysis. This resulted in a picture of the status quo (Where are we?) of analytics in software project management. From comparing this status quo with the results of an industrial survey on the industrial needs of different types of analysis, we propose an agenda on future work (Where do we go?).
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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 | 0.035 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.020 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.013 | 0.027 |
| Open science | 0.001 | 0.004 |
| 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".