TY - GEN
T1 - Resource-Aware Program Analysis Via Online Abstraction Coarsening
AU - Heo, Kihong
AU - Oh, Hakjoo
AU - Yang, Hongseok
N1 - Funding Information:
We thank Mayur Naik and Kee-Eung Kim for helpful comments. Oh was supported by Samsung Research Funding & Incubation Center of Samsung Electronics under Project Number SRFC-IT1701-09. Yang was supported by the Engineering Research Center Program through the National Research Foundation of Korea (NRF) funded by the Korean Government MSIT (NRF-2018R1A5A1059921), and also by Next-Generation Information Computing Development Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT (2017M3C4A7068177).
Publisher Copyright:
© 2019 IEEE.
PY - 2019/5
Y1 - 2019/5
N2 - We present a new technique for developing a resource-aware program analysis. Such an analysis is aware of constraints on available physical resources, such as memory size, tracks its resource use, and adjusts its behaviors during fixpoint computation in order to meet the constraint and achieve high precision. Our resource-aware analysis adjusts behaviors by coarsening program abstraction, which usually makes the analysis consume less memory and time until completion. It does so multiple times during the analysis, under the direction of what we call a controller. The controller constantly intervenes in the fixpoint computation of the analysis and decides how much the analysis should coarsen the abstraction. We present an algorithm for learning a good controller automatically from benchmark programs. We applied our technique to a static analysis for C programs, where we control the degree of flow-sensitivity to meet a constraint on peak memory consumption. The experimental results with 18 real-world programs show that our algorithm can learn a good controller and the analysis with this controller meets the constraint and utilizes available memory effectively.
AB - We present a new technique for developing a resource-aware program analysis. Such an analysis is aware of constraints on available physical resources, such as memory size, tracks its resource use, and adjusts its behaviors during fixpoint computation in order to meet the constraint and achieve high precision. Our resource-aware analysis adjusts behaviors by coarsening program abstraction, which usually makes the analysis consume less memory and time until completion. It does so multiple times during the analysis, under the direction of what we call a controller. The controller constantly intervenes in the fixpoint computation of the analysis and decides how much the analysis should coarsen the abstraction. We present an algorithm for learning a good controller automatically from benchmark programs. We applied our technique to a static analysis for C programs, where we control the degree of flow-sensitivity to meet a constraint on peak memory consumption. The experimental results with 18 real-world programs show that our algorithm can learn a good controller and the analysis with this controller meets the constraint and utilizes available memory effectively.
KW - learning
KW - resource constraint
KW - static analysis
UR - http://www.scopus.com/inward/record.url?scp=85072276957&partnerID=8YFLogxK
U2 - 10.1109/ICSE.2019.00027
DO - 10.1109/ICSE.2019.00027
M3 - Conference contribution
AN - SCOPUS:85072276957
T3 - Proceedings - International Conference on Software Engineering
SP - 94
EP - 104
BT - Proceedings - 2019 IEEE/ACM 41st International Conference on Software Engineering, ICSE 2019
PB - IEEE Computer Society
T2 - 41st IEEE/ACM International Conference on Software Engineering, ICSE 2019
Y2 - 25 May 2019 through 31 May 2019
ER -
