Name
Abstract | ||
|---|---|---|
Reliably predicting software defects is one of the most significant tasks in software engineering. Two of the major components of modern software reliability modeling approaches are: 1) extraction of salient features for software system representation, based on appropriately designed software metrics and 2) development of intricate regression models for count data, to allow effective software reliability data modeling and prediction. Surprisingly, research in the latter frontier of count data regression modeling has been rather limited. More specifically, a lack of simple and efficient algorithms for posterior computation has made the Bayesian approaches appear unattractive, and thus underdeveloped in the context of software reliability modeling. In this paper, we try to address these issues by introducing a novel Bayesian regression model for count data, based on the concept of max-margin data modeling, effected in the context of a fully Bayesian model treatment with simple and efficient posterior distribution updates. Our novel approach yields a more discriminative learning technique, making more effective use of our training data during model inference. In addition, it allows of better handling uncertainty in the modeled data, which can be a significant problem when the training data are limited. We derive elegant inference algorithms for our model under the mean-field paradigm and exhibit its effectiveness using the publicly available benchmark data sets. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/TNNLS.2015.2391171 | IEEE transactions on neural networks and learning systems |
Keywords | Field | DocType |
mean-field,dirichlet process (dp),max-margin,count data,software reliability.,software reliability,hidden markov models,computational modeling,data models,integrated circuits | Data modeling,Data mining,Bayesian inference,Computer science,Bayesian linear regression,Software system,Software,Artificial intelligence,Software metric,Software quality,Use Case Points,Machine learning | Journal |
Volume | Issue | ISSN |
PP | 99 | 2162-2388 |
Citations | PageRank | References |
1 | 0.35 | 20 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sotirios P. Chatzis | 1 | 250 | 24.25 |
| Andreas S. Andreou | 2 | 216 | 36.65 |