When Scramble Met South Station: Navigating Deadlocks in Typewriter Programs with Mantle and Beaverton

When Scramble Met South Station: Navigating Deadlocks in Typewriter Programs with Mantle and Beaverton

Abstract

In this study, we present a novel approach for navigating deadlocks in typewriter programs, which are characterized by their unpredictable and non-deterministic behavior. We introduce two new tools, Mantle and Beaverton, which work in tandem to dynamically analyze and modify program behavior in order to avoid deadlocks. Mantle is a runtime analysis tool that monitors program execution and identifies potential sources of deadlock, while Beaverton is a modification tool that adjusts program behavior in real-time to prevent deadlocks from occurring. We demonstrate the effectiveness of our approach through a case study involving the Scramble program and the South Station dataset, which is known to contain difficult-to-navigate deadlocks. Our results show that our approach significantly reduces the occurrence of deadlocks in the program, while also improving overall program performance. This work has important implications for the development of reliable and efficient typewriter programs, which are increasingly important in a wide range of fields, including computer science, engineering, and finance.

Citation

Victor Jeswin "When Scramble Met South Station: Navigating Deadlocks in Typewriter Programs with Mantle and Beaverton".  IEEE Exploration in Machine Learning, 2021.

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This paper appears in:
Date of Release: 2021
Author(s): Victor Jeswin.
IEEE Exploration in Machine Learning
Page(s): 8
Product Type: Conference/Journal Publications