A heuristic for estimating the impact of lingering defects: can debt analogy be used as a metric?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: Due to tight scheduling and limited budget, it may not be possible to resolve all the existing bugs in a current release of a software product. The accumulation of the deferred bugs in the issue tracking system are obligations (liabilities) of the software team similar to financial analogy of 'debt'. Defect debt is known as latent defects which are not resolved in the current release. Aim: In order to manage the defect debt, software managers need to be aware of the amount of debt (principal) as well as the price of the credit (interest) in their system. There are no studies in the literature to measure the principal or interest of defect debt. In this study, we propose a novel approach to identify the interest of defect debt. Methodology: We developed a heuristic to specify the interest based on three metrics: PageRank index, customer feedback and bug fixing duration. In order to investigate the feasibility of our heuristic, we employ it to two datasets that are extracted from both open source and commercial software products. We validate the heuristic using two metrics: the severity/priority of bugs, and the duration of bug fixing time. Result: The results show that 24% and 18% of the deferred bugs are high and medium impact bugs in project 1 and project 2, respectively.
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.
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 | 0.002 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 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 it