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A mathematical framework for design discovery from multi-threaded applications using neural sequence solvers
- Source :
- Innovations in Systems and Software Engineering. 17:289-307
- Publication Year :
- 2021
- Publisher :
- Springer Science and Business Media LLC, 2021.
-
Abstract
- Comprehending existing multi-threaded applications effectively is a challenge without proper assistance. Research has been proposed to mine programs to extract aspects of high-level design but not much to reverse-engineer the concurrent design from multi-threaded applications. To address the same, we develop a generic mathematical model to interpret run-time non-deterministic events and encode functional as well as thread-specific behaviour in form of quantifiable features, which can be fitted into a standard solver for automated inference of design aspects from multi-threaded applications. We build a tool Dcube based on the mathematical model and use various classifiers of a machine learning framework to infer design aspects related to concurrency and resource management. We collect a dataset of 480 projects from Github, CodeProject and Stack Overflow and 3 benchmark suites—CDAC Pthreads, Open POSIX Test Suites and PARSEC 3.0 and achieve an accuracy score of around 93.71% for all the design choices.
- Subjects :
- POSIX Threads
Concurrent engineering
business.industry
Computer Applications
Computer science
Concurrency
020207 software engineering
02 engineering and technology
Solver
Machine learning
computer.software_genre
Parsec
POSIX
020204 information systems
0202 electrical engineering, electronic engineering, information engineering
Benchmark (computing)
Artificial intelligence
business
computer
Software
Subjects
Details
- ISSN :
- 16145054 and 16145046
- Volume :
- 17
- Database :
- OpenAIRE
- Journal :
- Innovations in Systems and Software Engineering
- Accession number :
- edsair.doi...........d84fa993d5cf49f83f262c4eea67726e
- Full Text :
- https://doi.org/10.1007/s11334-021-00393-8