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on
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Intro
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How we usually think about code
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Our Journey
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Graphs explained in 30 seconds
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Graphs in Programming
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Building the Code Graph
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Storing the Graph: Merkle Trees
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AST Example
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Efficieny of this Approach
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Querying & Navigation
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Examples (contd.)
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Example: Code Complexity The cyclomatic complexity is a quantitative measure of the number of linearly
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Example: Flask
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Exploring Dependencies in a Code Base
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Pattern Matching: Text vs. Graphs
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Example: Building a Code Checker
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Adding an exception to the rule
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Example: Diff from Django Project
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Basic Problem: Tree Isomorphism (NP-complete!)
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Similar Problem: Chemical Similarity Benzene
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Applications
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Example: Semantic Diff
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Summary: Text vs. Graphs
Description:
Explore how graph technologies can revolutionize code understanding and software development in this EuroPython 2015 conference talk. Discover why treating code as text is limiting and learn about the benefits of viewing code as a graph structure. Examine specific examples of how this approach can improve comprehension of large codebases, enhance code quality, and automate aspects of software development. Gain insights into querying and navigating code graphs, analyzing code complexity, exploring dependencies, and performing semantic diffs. Consider the speaker's vision for the future of programming, moving beyond simple text editors. Delve into topics such as abstract syntax trees, Merkle trees, pattern matching, and the challenges of tree isomorphism in diff algorithms.

Code Is Not Text - How Graph Technologies Can Help Us to Understand Our Code Better

EuroPython Conference
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