Name
Abstract | ||
|---|---|---|
A Search-based test input generator produces a high volume of auto-generated test inputs. However, manually checking a test oracle for these test inputs is impractical due to the lacking of a systematic way to produce corresponding expected results automatically. This paper presents a mining approach to build decision tree models containing the estimated expected results for checking a test oracle. We first choose a subset of the auto-generated test inputs as a training set. Then, we mine the training set to generate a decision tree from which the estimated expected results can be retrieved. For evaluation purpose, we have applied our approach to two legacy examples, Triangle and Next Date. Our controlled experiments have shown that the mining approach is able to generate highly accurate behavioral models and achieve strong fault detectability. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ITNG.2013.126 | ITNG |
Keywords | Field | DocType |
auto-generated test input,training set,test input,decision tree model,mining approach,test oracle,search-based test input generator,decision tree,mining auto-generated test inputs,corresponding expected result,next date,computational modeling,triangle,decision trees,fault detection,data models,data mining,training data,accuracy | Training set,Decision tree,Data mining,Classification Tree Method,Computer science,Oracle,Artificial intelligence,Program testing,Machine learning,Decision tree learning,Incremental decision tree | Conference |
Citations | PageRank | References |
0 | 0.34 | 6 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Weifeng Xu | 1 | 28 | 2.53 |
| Hanlin Wang | 2 | 1 | 0.69 |
| Tao Ding | 3 | 15 | 8.48 |