Generating performance model to optimize or to reason about variability intensive systems is costly and the user never know (unless she or he is a seer) when to stop sampling. The performances measurement can take from several seconds to several days, depending on measurement and the noise, so it is very important to know if new measurement are profit. Here is where SEER can support user, by estimating the sampling size to generate performance models below a relative prediction error.
There are three folders their files that can help you to start using SEER. The folder CODES include all the source code needed to use it. SEER has been validated with ten configurable systems. Folders Ground Truth and Results include the measurements done to validate SEER.
Contact: jballesteros@uma.es
- European Union’s H2020 research and innovation programme (grant agreement DAEMON 101017109).
- Spanish projects RTI2018-096701-B-C21, LEIA UMA18-FEDERJA-15, MEDEA RTI2018-099213-B-I00 and Rhea P18-FR-1081.