Bibliographic record
Abstract
Classification of the causes of memory leaks. Tracing memory leaks in C programs using location reporting and allocation/deallocation information-gathering versions of the C allocators and deallocators. Tracing memory leaks in C++ programs: overloading the operators new and delete and the problems it causes. Techniques for location tracing. Counting objects in C++. Smart pointers as a remedy for memory leaks caused by the undetermined ownership problem. As mentioned previously, I do not like the terms “memory leaks” or “leaking memory”. They somehow put the onus on memory, as if it were the memory's inadequacy that caused the problem. Every time I hear a project manager or a student explain in a grave tone of voice that “the project is delayed because we have memory leaking”, I feel like retorting “OK, find a better memory that doesn't leak”. In truth, it's not the memory but rather the program that is inadequate. We should be talking about leaking programs, not about leaking memory. In this chapter we will classify the most common problems leading to memory leaks and discuss how to identify and locate them. We will start with trivial and obvious problems and proceed to more subtle ones that are harder to deal with. The first class of memory leaks is called orphaned allocation and is characterized by allocation of a memory segment whose address is not preserved for later deallocation.
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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".