A Literature Review
The following section is a summary of paper Using Machine Learning to Generate Test Oracles: A Systematic Literature Review. This paper summarizes the studies in this field, which published on Aug 24, 2021.
Background and Statistics
Problems concerned
- Types of oracles generated
- Researchers goal using ML
- Which specific ML technique
- How such skill is trained and validated
- How the success of the generation process is accessed
- Limitations
Basic Statistics
- Goal
- Test verdict: 18%
- Metamorphic relation: 27%
- Expected output: 55%
- Types of ML
- Supervised learning: 96%
- Input
- Labeled system execution logs
- Source code metadata
- Method used
- NN (59%): Backpropagation NN, Multipalyer Perceptrons, RBF NN, probabilistic NN, DNN
- SVM (23%)
- DT (5%)
- Adaptive boosting (5%)
- Evaluation
- Mutation Score (55%)
- Accuracy (18%)
- Number of correct classifications (18%)
- ROC (5%)
- Limitations
- Quantity of training data
- Hard to cope with multiple output functions - DL and ensemble should be explored
- Lack of common benchmarks.