Towards a classification of bugs to facilitate software maintainability tasks
Bibliographic record
Abstract
Software maintainability is an important software quality attribute that defines the degree by which a software system is understood, repaired, or enhanced. In recent years, there has been an increase in attention in techniques and tools that mine large bug repositories to help software developers understand the causes of bugs and speed up the fixing process. These techniques, however, treat all bugs in the same way. Bugs that are fixed by changing a single location in the code are examined the same way as those that require complex changes. After examining more than 100 thousand bug reports of 380 projects, we found that bugs can be classified into four types based on the location of their fixes. Type 1 bugs are the ones that fixed by modifying a single location in the code, while Type 2 refers to bugs that are fixed in more than one location. Type 3 refers to multiple bugs that are fixed in the exact same location. Type 4 is an extension of Type 3, where multiple bugs are resolved by modifying the same set of locations. This classification can help companies put the resources where they are needed the most. It also provides useful insight into the quality of the code. Knowing, for example, that a system contains a large number of bugs of Type 4 suggests high maintenance efforts. This classification can also be used for other tasks such as predicting the type of incoming bugs for an improved bug handling process. For example, if a bug is found to be of Type 4 then it should be directed to experienced developers.
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 | 0.008 | 0.055 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.031 | 0.014 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".