An Orchestrated Survey of Available Algorithms and Tools for Combinatorial Testing
Bibliographic record
Abstract
For functional testing based on the input domain of a functionality, parameters and their values are identified and a test suite is generated using a criterion exercising combinations of those parameters and values. Since software systems are large, resulting in large numbers of parameters and values, a technique based on combinatorics called Combinatorial Testing (CT) is used to automate the process of creating those combinations. CT is typically performed with the help of combinatorial objects called Covering Arrays. The goal of the present work is to determine available algorithms/tools for generating a combinatorial test suite. We tried to be as complete as possible by using a precise protocol for selecting papers describing those algorithms/tools. The 75 algorithms/tools we identified are then categorized on the basis of different comparison criteria, including: the test suite generation technique, the support for selection (combination)criteria, mixed covering array, the strength of coverage, and the support for constraints between parameters. Results can be of interest to researchers or software companies who are looking for a CT algorithm/tool suitable for their needs.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".