Name
Abstract | ||
|---|---|---|
Detecting defective entities from existing software systems is a problem of great importance for increasing both the software quality and the efficiency of software testing related activities. We introduce in this paper a novel approach for predicting software defects using fuzzy decision trees. Through the fuzzy approach we aim to better cope with noise and imprecise information. A fuzzy decision tree will be trained to identify if a software module is or not a defective one. Two open source software systems are used for experimentally evaluating our approach. The obtained results highlight that the fuzzy decision tree approach outperforms the non-fuzzy one on almost all case studies used for evaluation. Compared to the approaches used in the literature, the fuzzy decision tree classifier is shown to be more efficient than most of the other machine learning-based classifiers. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/SYNASC.2016.046 | 2016 18th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) |
Keywords | Field | DocType |
software defect prediction,machine learning,decision tree,fuzzy theory | Information Fuzzy Networks,Data mining,Computer science,Classification Tree Method,Fuzzy logic,Software system,Artificial intelligence,Software metric,Software quality,Machine learning,Decision tree learning,Incremental decision tree | Conference |
ISSN | ISBN | Citations |
2470-881X | 978-1-5090-5708-5 | 1 |
PageRank | References | Authors |
0.35 | 10 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zsuzsanna Marian | 1 | 42 | 3.71 |
| Ioan-Gabriel Mircea | 2 | 4 | 1.08 |
| István Gergely Czibula | 3 | 91 | 11.79 |
| Gabriela Czibula | 4 | 80 | 19.53 |