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Final published version
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - SlowCoach
T2 - Mutating Code to Simulate Performance Bugs
AU - Chen, Yiqun
AU - Schwahn, Oliver
AU - Natella, Roberto
AU - Bradbury, Matthew
AU - Suri, Neeraj
N1 - ©2022 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
PY - 2022/12/21
Y1 - 2022/12/21
N2 - Performance bugs are unnecessarily inefficient code chunks in software codebases that cause prolonged execution times and degraded computational resource utilization. For performance bug diagnostics, tools that aid in the identification of said bugs, such as benchmarks and profilers, are commonly employed. However, due to factors such as insufficient workloads or ineffective benchmarks, software defects related to code inefficiencies are inherently difficult to diagnose. Hence, the capabilities of performance bug diagnostic tools are limited and performance bug instances may be missed. Traditional mutation testing (MT) is a technique for quantifying a test suite's ability to find functional bugs by mutating the code of the test subject. Similarly, we adopt performance mutation testing (PMT) to evaluate performance bug diagnostic tools and identify where improvements need to be made to a performance testing methodology. We carefully investigate the different performance bug fault models and how synthesized performance bugs based on these models can evaluate benchmarks and workload selection to help improve performance diagnostics. In this paper, we present the design of our PMT framework, SLOWCOACH, and evaluate it with over 1600 mutants from 4 real-world software projects.
AB - Performance bugs are unnecessarily inefficient code chunks in software codebases that cause prolonged execution times and degraded computational resource utilization. For performance bug diagnostics, tools that aid in the identification of said bugs, such as benchmarks and profilers, are commonly employed. However, due to factors such as insufficient workloads or ineffective benchmarks, software defects related to code inefficiencies are inherently difficult to diagnose. Hence, the capabilities of performance bug diagnostic tools are limited and performance bug instances may be missed. Traditional mutation testing (MT) is a technique for quantifying a test suite's ability to find functional bugs by mutating the code of the test subject. Similarly, we adopt performance mutation testing (PMT) to evaluate performance bug diagnostic tools and identify where improvements need to be made to a performance testing methodology. We carefully investigate the different performance bug fault models and how synthesized performance bugs based on these models can evaluate benchmarks and workload selection to help improve performance diagnostics. In this paper, we present the design of our PMT framework, SLOWCOACH, and evaluate it with over 1600 mutants from 4 real-world software projects.
U2 - 10.1109/ISSRE55969.2022.00035
DO - 10.1109/ISSRE55969.2022.00035
M3 - Conference contribution/Paper
SN - 9781665451338
T3 - 2022 IEEE 33rd International Symposium on Software Reliability Engineering (ISSRE)
SP - 274
EP - 285
BT - 33rd IEEE International Symposium on Software Reliability Engineering
PB - IEEE
CY - New York
ER -