What is it about?
Distributed software systems are pervasive and serve as backbones of modern society. Analyzing run-time code dependencies in these systems is a key technique for assuring their quality, but it is very challenging to balance the cost incurred by the analysis and the accuracy of analysis results. This work proposes a reinforcement learning based technique to automatically attain and sustain optimal cost-accuracy balance.
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Why is it important?
Achieving an optimal cost-effectiveness balance is a long-standing and fundamental challenge in building software quality assurance tools. And typically the tradeoff is tuned manually before the tool starts, which gives secondary cost-effectiveness because of the execution dynamics of the system being analyzed.
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EADS, ACM Transactions on Software Engineering and Methodology, January 2021, ACM (Association for Computing Machinery), DOI: 10.1145/3379345.
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