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- W2969777417 abstract "Performance regression testing is highly expensive as it delays system development when optimally conducted after each code change. Therefore, it is important to prioritize the schedule of performance tests by executing them when a newly committed change is most likely to introduce performance regression. This paper introduces a novel formulation of the detection of performance regression introducing code changes as an optimization problem. Static and dynamic metrics are combined to generate a detection rule, which is being optimized in terms of its ability to flag problematic code changes, and avoid false positives. We evaluated our approach using performance issues, extracted from the Git project. Results show the effectiveness of our approach in accurately detecting performance regression introducing code changes compared with state-of-the-art techniques. Moreover, our suggested detection rules were found to be robust to the software changes over time, which reduces the overhead of updating them frequently." @default.
- W2969777417 created "2019-08-29" @default.
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- W2969777417 date "2019-01-01" @default.
- W2969777417 modified "2023-09-26" @default.
- W2969777417 title "PRICE: Detection of Performance Regression Introducing Code Changes Using Static and Dynamic Metrics" @default.
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- W2969777417 doi "https://doi.org/10.1007/978-3-030-27455-9_6" @default.
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