Measuring and Visualizing Code Stability -- A Case Study at Three Companies
Bibliographic record
Abstract
Monitoring performance of software development organizations can be achieved from a number of perspectives - e.g. using such tools as Balanced Scorecards or corporate dashboards. In this paper we present results from a study on using code stability indicators as a tool for product stability and organizational performance, conducted at three different software development companies - Ericsson AB, Saab AB Electronic Defense Systems (Saab) and Volvo Group Trucks Technology (Volvo Group). The results show that visualizing the source code changes using heat maps and linking these visualizations to defect inflow profiles provide indicators of how stable the product under development is and whether quality assurance efforts should be directed to specific parts of the product. Observing the indicator and making decisions based on its visualization leads to shorter feedback loops between development and test, thus resulting in lower development costs, shorter lead time and increased quality. The industrial case study in the paper shows that the indicator and its visualization can show whether the modifications of software products are focused on parts of the code base or are spread widely throughout the product.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".