Name
Abstract | ||
|---|---|---|
Most contemporary programs are customizable. They provide many features that give rise to millions of program variants. Determining which feature selection yields an optimal performance is challenging, because of the exponential number of variants. Predicting the performance of a variant based on previous measurements proved successful, but induces a trade-off between the measurement effort and prediction accuracy. We propose the alternative approach of family-based performance measurement, to reduce the number of measurements required for identifying feature interactions and for obtaining accurate predictions. The key idea is to create a variant simulator (by translating compile-time variability to run-time variability) that can simulate the behavior of all program variants. We use it to measure performance of individual methods, trace methods to features, and infer feature interactions based on the call graph. We evaluate our approach by means of five feature-oriented programs. On average, we achieve accuracy of 98%, with only a single measurement per customizable program. Observations show that our approach opens avenues of future research in different domains, such an feature-interaction detection and testing. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1145/2517208.2517209 | GPCE |
Keywords | Field | DocType |
optimal performance,contemporary program,customizable program,program variant,family-based performance measurement,infer feature interaction,feature-oriented program,alternative approach,feature interaction,measurement effort | Data mining,Exponential function,Feature selection,Computer science,Call graph,Performance measurement,Performance prediction | Conference |
Volume | Issue | ISSN |
49 | 3 | 0362-1340 |
Citations | PageRank | References |
12 | 0.53 | 33 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Norbert Siegmund | 1 | 1002 | 51.87 |
| Alexander von Rhein | 2 | 316 | 11.35 |
| Sven Apel | 3 | 3980 | 184.13 |