Replicating and Re-Evaluating the Theory of Relative Defect-Proneness
Bibliographic record
Abstract
A good understanding of the factors impacting defects in software systems is essential for software practitioners, because it helps them prioritize quality improvement efforts (e.g., testing and code reviews). Defect prediction models are typically built using classification or regression analysis on product and/or process metrics collected at a single point in time (e.g., a release date). However, current defect prediction models only predict if a defect will occur, but not when, which makes the prioritization of software quality improvements efforts difficult. To address this problem, Koru et al. applied survival analysis techniques to a large number of software systems to study how size (i.e., lines of code) influences the probability that a source code module (e.g., class or file) will experience a defect at any given time. Given that 1) the work of Koru et al. has been instrumental to our understanding of the size-defect relationship, 2) the use of survival analysis in the context of defect modelling has not been well studied and 3) replication studies are an important component of balanced scholarly debate, we present a replication study of the work by Koru et al. In particular, we present the details necessary to use survival analysis in the context of defect modelling (such details were missing from the original paper by Koru et al.). We also explore how differences between the traditional domains of survival analysis (i.e., medicine and epidemiology) and defect modelling impact our understanding of the size-defect relationship. Practitioners and researchers considering the use of survival analysis should be aware of the implications of our findings.
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 | 0.252 | 0.621 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.007 | 0.016 |
| Open science | 0.012 | 0.006 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".