Name
Abstract | ||
|---|---|---|
Software maintenance and evolution can introduce defects in software systems. For this reason, there is a great interest to identify defect prediction and estimation techniques. Recent research proposes just-in-time techniques to predict defective changes just at the commit level allowing the developers to fix the defect when it is introduced. However, the performance of existing just-in-time defect prediction models still requires to be improved. This paper proposes a new approach based on a large feature set containing product and process software metrics extracted from commits of software projects along with their evolution. The approach also introduces a deep temporal convolutional networks variant based on hierarchical attention layers to perform the fault prediction. The proposed approach is evaluated on a large dataset, composed of data gathered from six Java open-source systems. The obtained results show the effectiveness of the proposed approach in timely predicting defect proneness of code components. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1007/s00521-021-06659-3 | NEURAL COMPUTING & APPLICATIONS |
Keywords | DocType | Volume |
Software fault prediction, Software quality, Software fault, Deep learning | Journal | 34 |
Issue | ISSN | Citations |
5 | 0941-0643 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pasquale Ardimento | 1 | 0 | 0.34 |
| Lerina Aversano | 2 | 0 | 1.01 |
| Mario Luca Bernardi | 3 | 156 | 29.89 |
| Marta Cimitile | 4 | 0 | 0.34 |
| Martina Iammarino | 5 | 8 | 2.85 |