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Pattern Mining and Genetic Improvement in Compilers and Interpreters
Oliver Krauss
Research Center Hagenberg
Research output
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Types of Theses
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Doctoral Thesis
Overview
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Dive into the research topics of 'Pattern Mining and Genetic Improvement in Compilers and Interpreters'. Together they form a unique fingerprint.
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Keyphrases
Interpreter
100%
Genetic Improvement
100%
Compiler
100%
Pattern Mining
100%
Source Code
37%
Non-functional Properties
25%
Runtime Performance
25%
Software Design Patterns
25%
Knowledge-guided
25%
Performance Measurement
12%
Programming Paradigms
12%
Challenging Tasks
12%
Memory Consumption
12%
Energy Efficiency
12%
Software Development Methodology
12%
Population Diversity
12%
Functional Domain
12%
Novel Algorithm
12%
Depth Information
12%
Hardware Architecture
12%
Search-based Software Engineering
12%
Anti-patterns
12%
Novel Combinations
12%
Discriminative Patterns
12%
Domain Software
12%
Mutation Strategy
12%
Complex Computing
12%
Bug Patterns
12%
Fixed Pattern
12%
Complex Concepts
12%
Usage Efficiency
12%
Complex Algorithm
12%
Software Representation
12%
Computer Science
Source Codes
100%
Interpreter
100%
Pattern Mining
100%
Nonfunctional Property
66%
Time Performance
66%
Programming Paradigm
33%
Depth Information
33%
Hardware Architecture
33%
Energy Efficiency
33%
Anti-Pattern
33%
Software Domain
33%
Complex Concept
33%
Functional Domain
33%