An Empirical Investigation of Software Testing Methods and Techniques in the Province of Vojvodina
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A high-quality test design is a conditio sine qua non of successful software testing process, and its effectiveness depends, among other things, on the choice and proper use of appropriate methods and relevant software testing techniques. The main goal of this study was to provide insight into the use of current methods and relevant software testing techniques used in the test design phase of software testing process in software companies in the Province of Vojvodina. The empirical study was conducted by a survey research strategy in twenty-four software organisations. Eighty-three respondents took part in the survey. Descriptive analysis, correlation analysis, hierarchical cluster analysis, the multidimensional scaling, binomial test and Cohran's Q test were used for analyzing gathered quantitative data. The survey results have shown that respondents use to a significant extent the techniques belonging to ISO/IEC/IEEE 29119 testing standard. Comparison of the gathered data with individual results of similar studies conducted in Canada, Australia and Turkey has shown similarities between them and companies in the Province of Vojvodina. The findings of this study present empirically verified recommendations for testing design phase realization in the form of least and most used software testing methods and techniques, their benefits, limitations and details in application, similarities between software testing techniques, software testing techniques clusters and the probability of use of individual techniques.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it