ВИКОРИСТАННЯ АГРЕГОВАНИХ КРИТЕРІЇВ ДЛЯ ОЦІНКИ ЯКОСТІ ТЕСТІВ ПРОГРАМНОГО ЗАБЕЗПЕЧЕННЯ
Bibliographic record
Abstract
An approach to evaluating the software tests quality using aggregated quality criteria is proposed. The article considers the finding of such characteristics of software tests that can be used to judge their quality and their need for improvement. The subject of the study is the formation of a software tests quality evaluation system, which can be used in the software development process. It is proposed to consider a software test as a multiattribute object. It is emphasized that it is necessary to take into account both quantitative and qualitative characteristics of tests and test coverage, which greatly complicates the construction of a model for evaluating the software tests quality. Various approaches to solving the problem of evaluating complex, multiattribute objects are considered. The problem of comparing and ordering complex objects taking into account different criteria is considered. The choice of the method of sequential aggregation of classified states to solve the problem of multi-criteria selection and assessment is justified. The stages of the procedure for solving the estimation problem using the method of sequential aggregation of classified states are considered. An activity diagram is constructed that reflects an algorithm for constructing a hierarchical system of criteria. The criteria for evaluating software tests are given, which belong to three groups - efficiency, coverage, and software implementation. For a hierarchical system of criteria aggregation, a set of indicators, their qualitative gradations with corresponding numerical intervals, are allocated. At the highest level of the hierarchy, it is proposed to use three composite criteria that correspond to the groups of efficiency, coverage and implementation, which will allow to obtain an integral indicator of the software tests quality. The resulting integral indicator includes five quality classes, each of which corresponds to a multitude of low-level indicator estimates. Tests quality evaluation will improve the testing process, which purpose is to ensure the specified quality of the software being developed.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".