Enhanced Context-Aware Testing for Mobile Applications Using a Hybrid Grammatical Evolution Method
Bibliographic record
Abstract
Since the inception of computing, testing has remained a fundamental activity, with software test generation being both critical and challenging. Key obstacles include constructing highly complex objects, handling variable-length tests, and fully integrating tester domain expertise into the search process. Current mobile application testing tools provide insufficient support for context-aware applications. Although search-based test generation offers potential by reducing expert computational effort, it faces notable limitations. With the rise of automated systems, new challenges have emerged for both developers and the testing community. This study demonstrates how Grammatical Evolution (GE) can address these challenges, with Genetic Programming (GP) serving as an effective method for inducing classifiers to support data classification. The proposed framework incorporates Genetic Algorithm (GA), GE, Artificial Bee Colony (ABC), GE-GA, and GE-ABC approaches to enhance context-aware mobile application testing by generating test cases from system models. The experimental results validate the proposed approach, demonstrating superior performance compared to other advanced methods.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".