An Empirical Investigation of Software Testing Methods and Techniques in the Province of Vojvodina
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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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".