ATOS: Adaptive Program Tracing With Online Control Flow Graph Support
نویسندگان
چکیده
منابع مشابه
Adaptive Online Traffic Flow Prediction Using Aggregated Neuro Fuzzy Approach
Short term prediction of traffic flow is one of the most essential elements of all proactive traffic control systems. Although various methodologies have been applied to forecast traffic parameters, several researchers have showed that compared with the individual methods, hybrid methods provide more accurate results . These results made the hybrid tools and approaches a more common method for ...
متن کاملadaptive online traffic flow prediction using aggregated neuro fuzzy approach
short term prediction of traffic flow is one of the most essential elements of all proactive traffic control systems. although various methodologies have been applied to forecast traffic parameters, several researchers have showed that compared with the individual methods, hybrid methods provide more accurate results . these results made the hybrid tools and approaches a more common method for ...
متن کاملStreamlining Control Flow Graph Construction with DCFlow
A control flow graph (CFG) is used to model possible paths through a program, and is an essential part of many program analysis algorithms. While programs to construct CFGs can be written in metaprogramming languages such as Rascal, writing such programs is currently quite tedious. With the goal of streamlining this process, in this paper we present DCFlow, a domain-specific language and Rascal...
متن کاملSemantical Equivalence of the Control Flow Graph and the Program Dependence Graph
The program dependence graph (PDG) represents data and control dependence between statements in a program. This paper presents an operational semantics of program dependence graphs. Since PDGs exclude artificial order of statements that resides in sequential programs, executions of PDGs are not unique. However, we identified a class of PDGs that have unique final states of executions, called de...
متن کاملStable adaptive control with online learning
Learning algorithms have enjoyed numerous successes in robotic control tasks. In problems with time-varying dynamics, online learning methods have also proved to be a powerful tool for automatically tracking and/or adapting to the changing circumstances. However, for safety-critical applications such as airplane flight, the adoption of these algorithms has been significantly hampered by their l...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Access
سال: 2019
ISSN: 2169-3536
DOI: 10.1109/access.2019.2939566