Bibliographic record
Abstract
Often stakeholders, such as developers, managers, or buyers, want to find out what software development processes are being followed within a software project. Their reasons include: CMM and ISO 9000 compliance, process validation, management, acquisitions, and business intelligence. Recovering the software development processes from an existing project is expensive if one must rely upon manual inspection of artifacts and interviews of developers and their managers. Researchers have suggested live observation and instrumentation of a project to allow for more measurement, but this is costly, invasive, and also requires a live running project. Instead, we propose an after the fact analysis: software process recovery. This approach analyzes version control systems, bug trackers and mailing list archives using a variety of supervised and unsupervised techniques from machine learning, topic analysis, natural language processing and statistics. We can combine all of these methods to recover process events that we map back to software development processes like the Unified Process. We can produce diagrams called Recovered Unified Process Views (RUPV) that are similar to the Unified Process diagram, a time-line of effort per parallel discipline occurring across time. We then validate these methods using case studies of multiple open source software systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".