ComPy-Learn: A toolbox for exploring machine learning representations for compilers
Research output: Contribution to book/Conference proceedings/Anthology/Report › Conference contribution › Contributed › peer-review
Contributors
Abstract
Deep Learning methods have not only shown to improve software performance in compiler heuristics, but also e.g. to improve security in vulnerability prediction or to boost developer productivity in software engineering tools. A key to the success of such methods across these use cases is the expressiveness of the representation used to abstract from the program code. Recent work has shown that different such representations have unique advantages in terms of performance. However, determining the best-performing one for a given task is often not obvious and requires empirical evaluation. Therefore, we present ComPy-Learn, a toolbox for conveniently defining, extracting, and exploring representations of program code. With syntax-level language information from the Clang compiler frontend and low-level information from the LLVM compiler backend, the tool supports the construction of linear and graph representations and enables an efficient search for the best-performing representation and model for tasks on program code.
Details
Original language | English |
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Title of host publication | 2020 Forum for Specification and Design Languages (FDL) |
Number of pages | 4 |
ISBN (electronic) | 978-1-7281-8928-4 |
Publication status | Published - 15 Sept 2020 |
Peer-reviewed | Yes |
Publication series
Series | Forum on Specification, Verification and Design Languages, FDL |
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ISSN | 1636-9874 |
Conference
Title | 2020 Forum on Specification and Design Languages, FDL 2020 |
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Duration | 15 - 17 September 2020 |
City | Kiel |
Country | Germany |
External IDs
ORCID | /0000-0002-5007-445X/work/141545618 |
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Keywords
Research priority areas of TU Dresden
ASJC Scopus subject areas
Keywords
- Clang, Code Representations, Compilers, LLVM, Machine Learning