Bibliographic record
Abstract
In today’s world, systems that contain software are everywhere. They may be observed in common devices employed in everyday living (e.g., coffee machines, washing machines, cell phones) as well as in sophisticated engineering systems (e.g., cars, planes, spacecrafts). As the complexity of control systems grows, testing becomes more and time consuming. Poorly tested systems may cost producers billions of dollars annually especially when defects are found by end users in production environments. Barry Boehm’s research analysis [5] indicates that the cost of removing a software defect grows exponentially for each stage of the development life cycle in which it remains undiscovered. Boris Beizer [2] estimates that 30 up to 90 percentage of the effort in put into testing. Another research project conducted by the United States Department of Commerce, National Institute of Standards and Technology [21] estimated that software defects cost the U.S. economy $60 billion per year. There are several facts that show clearly possible consequences of poorly tested systems. On February 25, 1991, an Iraqi Scud hit the barracks in Dhahran in Saudi Arabia, killing 28 soldiers from the US Army. This accident was caused by software error in the system’s clock [20]. The Patriot missile battery has been in operation for 100 hours, by which time the system’s internal clock had drifted by one third of a second. For a target moving as fast as Scud, this was equivalent to a position error of 600 meters. Another example is connected with Therac-25 radiation therapy machine that was produced by Atomic Energy of Canada Limited and CGR of France. The machine was involved with at least six known accidents between 1985 and 1987, in which patients were given massive overdoses of radiation, which were in some cases on the order of hundreds of grays [13]. At least five patients died of the overdoses. These accidents were caused by errors in software control application. One of the most infamous computer bugs in history was found during flight 501 that took place on June 4, 1996. This was the first, and unsuccessful, test flight of the European Ariane 5 expendable lunch system. Due to an error in the software design (inadequate protection from integer
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".