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Auflistung nach Schlagwort "Software Performance"

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  • ConferencePaper
    Accurate Modeling of Performance Histories for Evolving Software Systems
    (Software Engineering 2021, 2021) Mühlbauer, Stefan; Apel, Sven; Siegmund, Norbert
    This work has been originally published in the proceedings of the 34th IEEE/ACM International Conference on Automated Software Engineering (ASE 2019). Learning from the history of a software system’s performance behavior does not only help discovering and locating performance bugs, but also supports identifying evolutionary performance patterns and general trends. Exhaustive regression testing is usually impractical, because rigorous performance benchmarking requires executing realistic workloads per revision, resulting in large execution times. We devise a novel active revision sampling approach that aims at tracking and understanding a system’s performance history by approximating the performance behavior of a software system across all of its revisions. In short, we iteratively sample and measure the performance of specific revisions to learn a performance-evolution model. We select revisions based on how uncertainty our models predicts their correspondent performance values. Technically, we use Gaussian Process models that not only estimates performance for each revision, but also provides an uncertainty value alongside. This way, we iteratively improve our model with only few measurements. Our evaluation with six real-world configurable software system demonstrates that Gaussian Process models are able to accurately estimate the performance-evolution histories with only few measurements and to reveal interesting behaviors and trends, such as change points.
  • Konferenzbeitrag
    Identifying Software Performance Changes Across Variants and Versions
    (Software Engineering 2022, 2022) Mühlbauer, Stefan; Apel, Sven; Siegmund, Norbert
    Performance changes of configurable software systems can occur and persist throughout their lifetime. Finding optimal configurations and configuration options that influence performance is already difficult, but in the light of software evolution, configuration-dependent performance changes may lurk in a potentially large number of different versions of the system. Building on previous work, we combine two perspectives---variability and time---and devise an approach to identify configuration-dependent performance changes retrospectively across the software variants and versions of a software system. In a nutshell, we iteratively sample pairs of configurations and versions and measure the respective performance, which we use to actively learn a model that estimates how likely a commit introduces a performance change. For such commits, we infer the configuration options that best explain observed performance changes. Pursuing a search strategy to measure selectively and incrementally further pairs, we increase the accuracy of identified change points related to configuration options and interactions. Our evaluation with both real-world software systems and synthesized data demonstrates that we can pinpoint performance shifts to individual configuration options and commits with high accuracy and at scale.