The extended operational profile model for usage-based software testing
Bibliographic record
Abstract
Operational profiles are a quantification of customer usage patterns for a software application. These profiles are used to measure software reliability by testing the software in a manner that represents customer use. The current definition of an operational profile states that it is the set of operations available in the application, and the operations' probabilities of occurrence in customer usage scenarios. This definition is too limited. In most industrial applications, focusing on operations alone does not offer adequate representation of the use of software. The limited definition of operational profiles has restricted their applicability and hence software reliability analysis for many software development organizations. Our work provides a formal and practical extension of the current definition of operational profiles to increase their applicability. The extended operational profile model is composed of three parts that address realistic software execution. The first part is taken from the original model and consists of the operations and the probabilities with which they occur in execution. The second part captures the configuration of the application and the environment in which it is executed. The third part captures the values of the data that is passed to the application. An industrial case study is used to demonstrate the validity of the model by showing that particular recorded defects may be discovered using the new model. Use of the extended operational profile is enabled with a framework and a toolkit. The framework incorporates the model into a general software development life cycle, and the toolkit automates the process of extracting the extended operational profile from log files of software usage.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".