Name
Abstract | ||
|---|---|---|
Configurable software systems allow stakeholders to derive program variants by selecting features. Understanding the correlation between feature selections and performance is important for stakeholders to be able to derive a program variant that meets their requirements. A major challenge in practice is to accurately predict performance based on a small sample of measured variants, especially when features interact. We propose a variability-aware approach to performance prediction via statistical learning. The approach works progressively with random samples, without additional effort to detect feature interactions. Empirical results on six real-world case studies demonstrate an average of 94% prediction accuracy based on small random samples. Furthermore, we investigate why the approach works by a comparative analysis of performance distributions. Finally, we compare our approach to an existing technique and guide users to choose one or the other in practice.
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Year | Venue | Keywords |
|---|---|---|
2013 | ASE | statistical analysis,configuration management,learning artificial intelligence |
Field | DocType | ISSN |
Data mining,Computer science,Software system,Artificial intelligence,Statistical learning,Configuration management,Software quality,Performance prediction,Machine learning,Statistical analysis | Conference | 1527-1366 |
ISBN | Citations | PageRank |
978-1-4799-0215-6 | 55 | 1.26 |
References | Authors | |
15 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jianmei Guo | 1 | 390 | 22.80 |
| Krzysztof Czarnecki | 2 | 6064 | 411.57 |
| Sven Apel | 3 | 3980 | 184.13 |
| Norbert Siegmund | 4 | 1002 | 51.87 |
| Andrzej Wasowski | 5 | 1282 | 60.47 |