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Graph-Based Source Code Analysis of JavaScript Repositories

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Dániel Stein
Dániel Stein

A graph-based approach to analyze JavaScript source codes, using Neo4j as the graph database backend and ShapeSecurity Shift as the parser. Hungarian version (presented at a Neo4j meetup): http://www.slideshare.net/steindani/forrskdtrak-grfalap-statikus-analzise

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Graph-Based Source Code Analysis of JavaScript Repositories Budapest University of Technology and Economics Department of Measurement and Information Systems Fault Tolerant Systems Research Group Dániel Stein Gábor Szárnyas
Content 1. Context 2. Tooling 3. Use Cases 4. Neo4j Observations 2
Continuous Integration (CI) – Developers working together – Prevent integration problems – Examples – Jenkins – Hudson – Travis CI 3 Verziókezelés Fordítás Fejlesztés Egység- és integrációs teszt Development Version Control System Compilation Unit and Integration Tests
4 Apple, https://blog.codecentric.de/en/2014/02/curly-braces/
4 Apple, https://blog.codecentric.de/en/2014/02/curly-braces/
4 whoops Apple, https://blog.codecentric.de/en/2014/02/curly-braces/
Static Analysis – No need for compilation or execution of the application – Formatting, structural and semantic rule checking – Can extend the workflow of continuous integration and improve it – In this research we used code analysis utilizing pattern matching 5 Verziókezelés Fordítás Fejlesztés Egység- és integrációs teszt Kódanalízis Verziókezelés Fordítás Fejlesztés Egység- és integrációs teszt Kódanalízis Development Version Control System Compilation Unit and Integration Tests Static Analysis
Static Analysis – No need for compilation or execution of the application – Formatting, structural and semantic rule checking – Can extend the workflow of continuous integration and improve it – In this research we used code analysis utilizing pattern matching 5 Verziókezelés Fordítás Fejlesztés Egység- és integrációs teszt Kódanalízis Verziókezelés Fordítás Fejlesztés Egység- és integrációs teszt Kódanalízis Development Version Control System Compilation Unit and Integration Tests Static Analysis
Static Analysis – No need for compilation or execution of the application – Formatting, structural and semantic rule checking – Can extend the workflow of continuous integration and improve it – In this research we used code analysis utilizing pattern matching 5 Verziókezelés Fordítás Fejlesztés Egység- és integrációs teszt Kódanalízis Verziókezelés Fordítás Fejlesztés Egység- és integrációs teszt Kódanalízis – Java – FindBugs – PMD – CheckStyle Development Version Control System Compilation Unit and Integration Tests Static Analysis
Static Analysis – No need for compilation or execution of the application – Formatting, structural and semantic rule checking – Can extend the workflow of continuous integration and improve it – In this research we used code analysis utilizing pattern matching 5 Verziókezelés Fordítás Fejlesztés Egység- és integrációs teszt Kódanalízis Verziókezelés Fordítás Fejlesztés Egység- és integrációs teszt Kódanalízis – Java – FindBugs – PMD – CheckStyle – JavaScript – ESLint – Facebook Infer, Flow – Tern – TAJS Development Version Control System Compilation Unit and Integration Tests Static Analysis
– Thorough code analysis is time-consuming and resource-intensive – For large projects it can be too slow Problems to Solve 6 unit tests static analysis ☼ ☆☾☆
– Thorough code analysis is time-consuming and resource-intensive – For large projects it can be too slow – Temporary solution: batching Problems to Solve 6 unit tests static analysis ☼ ☆☾☆ unit tests static analyis
– Thorough code analysis is time-consuming and resource-intensive – For large projects it can be too slow – Temporary solution: batching Present results as soon and as fast as possible. Problems to Solve 6 unit tests static analysis ☼ ☆☾☆ unit tests static analyis
Problems to Solve – Memory limits appear when... – Global rules are checked – Storing the structure in-memory – For large code repositories – Not being incremental – Batched execution simply does not cut it – Small change induces complete recheck 7
Our Approach – Incremental methodology – Instead of batched execution – Update the prepared results with the effects of the change – Only store the required parts in the memory 8 analyzer Δ2.-1.1.
VCS Workspace Abstact Syntax Tree Abstract Semantic Graph Well-formedness Rules Query Execution Database Main.js | ++---- Dependency.js | +++++- FIterator.js | ---- Parser.js | ++ Automatic Well-formedness Rule Evaluation Manual Execution and Data Extraction Querying and Transformation . discoverer ChangeProcessor.js CommandParser.js FileIterator.js iterators DepCollector.js FileDiscoverer.js InitIterator.js Main.js whitepages ConnectionMgr.js DependencyMgr.js neo4jValidation Report <!> <?> <.> Module declaration declarators items binding init left right Architecture overview 9
VCS Workspace Abstact Syntax Tree Abstract Semantic Graph Well-formedness Rules Query Execution Database Main.js | ++---- Dependency.js | +++++- FIterator.js | ---- Parser.js | ++ Automatic Well-formedness Rule Evaluation Manual Execution and Data Extraction Querying and Transformation . discoverer ChangeProcessor.js CommandParser.js FileIterator.js iterators DepCollector.js FileDiscoverer.js InitIterator.js Main.js whitepages ConnectionMgr.js DependencyMgr.js neo4jValidation Report <!> <?> <.> Module declaration declarators items binding init left right Architecture overview 9
VCS Workspace Abstact Syntax Tree Abstract Semantic Graph Well-formedness Rules Query Execution Database Main.js | ++---- Dependency.js | +++++- FIterator.js | ---- Parser.js | ++ Automatic Well-formedness Rule Evaluation Manual Execution and Data Extraction Querying and Transformation . discoverer ChangeProcessor.js CommandParser.js FileIterator.js iterators DepCollector.js FileDiscoverer.js InitIterator.js Main.js whitepages ConnectionMgr.js DependencyMgr.js neo4jValidation Report <!> <?> <.> Module declaration declarators items binding init left right Architecture overview 9
VCS Workspace Abstact Syntax Tree Abstract Semantic Graph Well-formedness Rules Query Execution Database Main.js | ++---- Dependency.js | +++++- FIterator.js | ---- Parser.js | ++ Automatic Well-formedness Rule Evaluation Manual Execution and Data Extraction Querying and Transformation . discoverer ChangeProcessor.js CommandParser.js FileIterator.js iterators DepCollector.js FileDiscoverer.js InitIterator.js Main.js whitepages ConnectionMgr.js DependencyMgr.js neo4jValidation Report <!> <?> <.> Module declaration declarators items binding init left right Architecture overview 9
Code Processing Steps 20 tokenizer source code tokens AST ASG parser scope analyzer tokenizer source code tokens AST ASG parser scope analyzer
Code Processing Steps 21 tokenizer source code tokens AST ASG parser scope analyzer
Code Processing Steps 22 tokenizer source code tokens AST ASG parser scope analyzer Sequence of statements formalized in a given language
Code Processing Steps 23 tokenizer source code tokens AST ASG parser scope analyzer Sequence of statements formalized in a given language
Code Processing Steps 24 tokenizer source code tokens AST ASG parser scope analyzer
Code Processing Steps 25 tokenizer source code tokens AST ASG parser scope analyzer token – the shortest character sequence still having meaning.
Code Processing Steps 26 tokenizer source code tokens AST ASG parser scope analyzer token – the shortest character sequence still having meaning.
Code Processing Steps 27 tokenizer source code tokens AST ASG parser scope analyzer Token Token type VAR (Keyword) IDENTIFIER (Ident) ASSIGN (Punctuator) NUMBER (NumericLiteral) DIV (Punctuator) NUMBER (NumericLiteral) token – the shortest character sequence still having meaning.
tokenizer source code tokens AST ASG parser scope analyzer Code Processing Steps 12 Module VariableDeclarationStatement VariableDeclaration VariableDeclarator BindingIdentifier name = `foo` BinaryExpression operator = `Div` LiteralNumericExpression value = 1.0 LiteralNumericExpression value = 0.0 declaration declarators items binding init left right
tokenizer source code tokens AST ASG parser scope analyzer Code Processing Steps 12 Abstract Syntax Tree (AST) – Tree representation of – the grammar structure of – the sequence of tokens. Module VariableDeclarationStatement VariableDeclaration VariableDeclarator BindingIdentifier name = `foo` BinaryExpression operator = `Div` LiteralNumericExpression value = 1.0 LiteralNumericExpression value = 0.0 declaration declarators items binding init left right
tokenizer source code tokens AST ASG parser scope analyzer Code Processing Steps 12 Module VariableDeclarationStatement VariableDeclaration VariableDeclarator BindingIdentifier name = `foo` BinaryExpression operator = `Div` LiteralNumericExpression value = 1.0 LiteralNumericExpression value = 0.0 declaration declarators items binding init left right
tokenizer source code tokens AST ASG parser scope analyzer Code Processing Steps 13 Module VariableDeclarationStatement VariableDeclaration VariableDeclarator BindingIdentifier name = `foo` BinaryExpression operator = `Div` LiteralNumericExpression value = 1.0 LiteralNumericExpression value = 0.0 declaration declarators items binding init left right
tokenizer source code tokens AST ASG parser scope analyzer Code Processing Steps 13 Module VariableDeclarationState VariableDeclaration VariableDeclarator BindingIdentifier name = `foo` BinaryE operato LiteralNumericExpression value = 1.0 declaration declarators items binding init left right
tokenizer source code tokens AST ASG parser scope analyzer Code Processing Steps 13 Module VariableDeclarationState VariableDeclaration VariableDeclarator BindingIdentifier name = `foo` BinaryE operato LiteralNumericExpression value = 1.0 declaration declarators items binding init left right GlobalScope Scope Variable name = `foo` Reference accessibility = `Write` variables references children Declaration kind = `Var` declarations node astNode
tokenizer source code tokens AST ASG parser scope analyzer Code Processing Steps Abstract Semantic Graph (ASG) – Graph, not necessarily tree. – Semantic information besides the syntactic structure. – Contains cross-edges → 13 Module VariableDeclarationState VariableDeclaration VariableDeclarator BindingIdentifier name = `foo` BinaryE operato LiteralNumericExpression value = 1.0 declaration declarators items binding init left right GlobalScope Scope Variable name = `foo` Reference accessibility = `Write` variables references children Declaration kind = `Var` declarations node astNode
tokenizer source code tokens AST ASG parser scope analyzer Code Processing Steps 13 Module VariableDeclarationState VariableDeclaration VariableDeclarator BindingIdentifier name = `foo` BinaryE operato LiteralNumericExpression value = 1.0 declaration declarators items binding init left right GlobalScope Scope Variable name = `foo` Reference accessibility = `Write` variables references children Declaration kind = `Var` declarations node astNode
AST vs ASG 14
AST vs ASG 14
AST vs ASG 14 1 SLOC 20-40-50 nodes
Overview of the Approach 15 Verziókezelés Fordítás Fejlesztés Egység- és integrációs teszt Kódanalízis Verziókezelés Fordítás Fejlesztés Egység- és integrációs teszt Kódanalízis Development Version Control System Compilation Unit and Integration Tests Static Analysis
Overview of the Approach 16
Overview of the Approach 16 Version Control System Integrated Development Environment Git,VisualStudioCode
Overview of the Approach 16 Version Control System Integrated Development Environment tokenizer source code tokens AST ASG parser scope analyzer Git,VisualStudioCode ShapeSecurityShift
Overview of the Approach 16 Version Control System transformation Integrated Development Environment tokenizer source code tokens AST ASG parser scope analyzer Git,VisualStudioCode ShapeSecurityShift Java,Cypher
Overview of the Approach 16 Version Control System transformation graph database Integrated Development Environment tokenizer source code tokens AST ASG parser scope analyzer Git,VisualStudioCode ShapeSecurityShift Java,Cypher Neo4j
Overview of the Approach 16 Version Control System transformation graph database Integrated Development Environment tokenizer source code tokens AST ASG parser scope analyzer result processing Git,VisualStudioCode ShapeSecurityShift Java,Cypher Neo4j
Overview of the Approach 16 Version Control System transformation graph database Integrated Development Environment tokenizer source code tokens AST ASG parser scope analyzer result processing Git,VisualStudioCode ShapeSecurityShift Java,Cypher Neo4j
Overview of the Approach 16 Version Control System transformation graph database Integrated Development Environment tokenizer source code tokens AST ASG parser scope analyzer result processing Git,VisualStudioCode ShapeSecurityShift Java,Cypher Neo4j
Overview of the Approach 16 Version Control System transformation graph database Integrated Development Environment tokenizer source code tokens AST ASG parser scope analyzer result processing Git,VisualStudioCode ShapeSecurityShift Java,Cypher Neo4j
Overview of the Approach 16 Version Control System transformation graph database Integrated Development Environment tokenizer source code tokens AST ASG parser scope analyzer result processing Git,VisualStudioCode ShapeSecurityShift Java,Cypher Neo4j
Overview of the Approach 16 Version Control System transformationtransformation graph database Integrated Development Environment tokenizer source code tokens AST ASG parser scope analyzer result processing result processing Git,VisualStudioCode ShapeSecurityShift Java,Cypher Neo4j
Graph Pattern Matching 17 VariableDeclarator BindingIdentifier name = `foo` BinaryExpression operator = `Div` LNExpression value = 1.0 LNExpression value = 0.0
Graph Pattern Matching – Graph pattern – A declarative, – graph-like formalism – expressing constraints. 17 VariableDeclarator BindingIdentifier name = `foo` BinaryExpression operator = `Div` LNExpression value = 1.0 LNExpression value = 0.0
Graph Pattern Matching – Graph pattern – A declarative, – graph-like formalism – expressing constraints. 17 VariableDeclarator BindingIdentifier name = `foo` BinaryExpression operator = `Div` LNExpression value = 1.0 LNExpression value = 0.0 binding be right
Graph Pattern Matching – Graph pattern – A declarative, – graph-like formalism – expressing constraints. 17 VariableDeclarator BindingIdentifier name = `foo` BinaryExpression operator = `Div` LNExpression value = 1.0 LNExpression value = 0.0 binding be right
Graph Pattern Matching – Graph pattern – A declarative, – graph-like formalism – expressing constraints. 17 BindingIdentifier name = `foo` Graphpatternquery expressedinCypher lookingfora divisionbyzero binding Resultsofthe patternmatching
Use Cases static analysis – Searching for local bad smells (linter warnings) – without a case – value set more than once – Not used variable – Global rules – Unreachable code parts – Framework – Freely extendable – User-defined rules – Easier to use than visitor pattern solutions 18
Use Cases transformation Control Flow Graph (CFG) – Graph representation of – every possible statement sequence – during code execution. 19
Use Cases transformation Control Flow Graph (CFG) – Graph representation of – every possible statement sequence – during code execution. 19 statement
Use Cases transformation Control Flow Graph (CFG) – Graph representation of – every possible statement sequence – during code execution. 19 statement statement
Use Cases transformation Control Flow Graph (CFG) – Graph representation of – every possible statement sequence – during code execution. 19 statement statement if
Use Cases transformation Control Flow Graph (CFG) – Graph representation of – every possible statement sequence – during code execution. 19 statement statement if condition
Use Cases transformation Control Flow Graph (CFG) – Graph representation of – every possible statement sequence – during code execution. 19 statement statement statement statement if condition
Use Cases transformation Control Flow Graph (CFG) – Graph representation of – every possible statement sequence – during code execution. 19 statement statement statement statement if statement condition
Use Cases transformation Control Flow Graph (CFG) – Graph representation of – every possible statement sequence – during code execution. 19 statement statement statement statement error if statement condition
Use Cases transformation Control Flow Graph (CFG) – Graph representation of – every possible statement sequence – during code execution. 19 statement statement statement statement statement error if statement condition
Use Cases transformation Control Flow Graph (CFG) – Graph representation of – every possible statement sequence – during code execution. 19 statement statement statement statement statement error if statement condition
Use Cases transformation Control Flow Graph (CFG) – Graph representation of – every possible statement sequence – during code execution. 19 statement statement statement statement statement error if return statement condition
error Use Cases test generation 20 statement statement statement statement statement if return condition statement
error Use Cases test generation – Inspecting control flows – Is the given statement reachable given the constraints on the edges? – Which one is the shortest route? 20 statement statement statement statement statement if return condition statement
error Use Cases test generation – Inspecting control flows – Is the given statement reachable given the constraints on the edges? – Which one is the shortest route? 20 statement statement statement statement statement if return condition statement
error Use Cases test generation – Inspecting control flows – Is the given statement reachable given the constraints on the edges? – Which one is the shortest route? 20 statement statement statement statement statement if return condition statement
error Use Cases test generation – Inspecting control flows – Is the given statement reachable given the constraints on the edges? – Which one is the shortest route? 20 statement statement statement statement statement if return condition statement
error Use Cases test generation – Inspecting control flows – Is the given statement reachable given the constraints on the edges? – Which one is the shortest route? 20 statement statement statement statement statement if return condition statement
error Use Cases test generation – Inspecting control flows – Is the given statement reachable given the constraints on the edges? – Which one is the shortest route? 20 statement statement statement statement statement if return condition statement
error Use Cases test generation – Inspecting control flows – Is the given statement reachable given the constraints on the edges? – Which one is the shortest route? 20 statement statement statement statement statement if return condition statement
error Use Cases test generation – Inspecting control flows – Is the given statement reachable given the constraints on the edges? – Which one is the shortest route? 20 statement statement statement statement statement if return condition statement
error Use Cases test generation – Inspecting control flows – Is the given statement reachable given the constraints on the edges? – Which one is the shortest route? – Producing test input for dynamic testing 20 statement statement statement statement statement if return condition statement
Use Cases type inference – Supporting dynamically typed languages – Python – JavaScript / ECMAScript 21
Use Cases type inference – Supporting dynamically typed languages – Python – JavaScript / ECMAScript 21 http://marijnhaverbeke.nl/blog/tern.html
Use Cases impact analysis – Adapting to the continuous integration workflow – Handling multiple branches – Following the modifications in a branch – File-level incremental granularity – Giving differential reports to the developers 22
Why Neo4j? +++ – Quick prototyping – Supporting transactions – Great tooling -- – Not scaling well – Only disk-based 23
Remarks MERGE – MATCH or CREATE – Great for the lazy – Can be expensive – Possible solutions: – Less MERGE – Separating queries – Create first if not present – Use MATCH instead of MERGE – Prevention – Prepare the structure when inserting the data 24
Remarks MERGE 25 3 1
Remarks if-then-else – Not a language element in Cypher – Can be solved with a trick – Verrrrrry sloww – Solution: – Two smaller, disjunct cases 26
Remarks if-then-else – Not a language element in Cypher – Can be solved with a trick – Verrrrrry sloww – Solution: – Two smaller, disjunct cases 26
Remarks if-then-else 27
Remarks if-then-else 28
Remarks if-then-else 28
Remarks if-then-else 28
Remarks if-then-else 28
Remarks if-then-else 28 ∞ vs 15 sec
Remarks if-then-else 28 ∞ vs 15 sec These are not chickens.
Remarks reachability – Transitive closure without length constraints is slow. – Transitive closure over repeating node/edge pattern is only possible using tricks. 29 A B *
Remarks reachability – Transitive closure without length constraints is slow. – Transitive closure over repeating node/edge pattern is only possible using tricks. 29 A B *
Remarks reachability – Transitive closure without length constraints is slow. – Transitive closure over repeating node/edge pattern is only possible using tricks. 29 A B *
Conclusions – Source code analyzer framework – Searching for global error patterns – Close to real time feedback – Type inference possible – Test input generation possible – Approach for both dynamically and statically typed languages – Using Neo4j for – Storing – Pattern matching – Transforming – Version control – Storing metadata 30
– Our work was supported by: – ÚNKP* – Microsoft Azure for Research – MTA-BME Lendület Program Project Details – The framework prototype is open- source. https://github.com/ ftsrg/codemodel-rifle 31 * Supported by the ÚNKP-16-2-I. New National Excellence Program of the Ministry of Human Capacities.
Project Details – Supervisors – Ádám Lippai – Dávid Honfi – Gábor Szárnyas – Helped my research – Tamás Soma Lucz – Industrial case study 32

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Graph-Based Source Code Analysis of JavaScript Repositories

  • 1. Graph-Based Source Code Analysis of JavaScript Repositories Budapest University of Technology and Economics Department of Measurement and Information Systems Fault Tolerant Systems Research Group Dániel Stein Gábor Szárnyas
  • 2. Content 1. Context 2. Tooling 3. Use Cases 4. Neo4j Observations 2
  • 3. Continuous Integration (CI) – Developers working together – Prevent integration problems – Examples – Jenkins – Hudson – Travis CI 3 Verziókezelés Fordítás Fejlesztés Egység- és integrációs teszt Development Version Control System Compilation Unit and Integration Tests
  • 7. Static Analysis – No need for compilation or execution of the application – Formatting, structural and semantic rule checking – Can extend the workflow of continuous integration and improve it – In this research we used code analysis utilizing pattern matching 5 Verziókezelés Fordítás Fejlesztés Egység- és integrációs teszt Kódanalízis Verziókezelés Fordítás Fejlesztés Egység- és integrációs teszt Kódanalízis Development Version Control System Compilation Unit and Integration Tests Static Analysis
  • 8. Static Analysis – No need for compilation or execution of the application – Formatting, structural and semantic rule checking – Can extend the workflow of continuous integration and improve it – In this research we used code analysis utilizing pattern matching 5 Verziókezelés Fordítás Fejlesztés Egység- és integrációs teszt Kódanalízis Verziókezelés Fordítás Fejlesztés Egység- és integrációs teszt Kódanalízis Development Version Control System Compilation Unit and Integration Tests Static Analysis
  • 9. Static Analysis – No need for compilation or execution of the application – Formatting, structural and semantic rule checking – Can extend the workflow of continuous integration and improve it – In this research we used code analysis utilizing pattern matching 5 Verziókezelés Fordítás Fejlesztés Egység- és integrációs teszt Kódanalízis Verziókezelés Fordítás Fejlesztés Egység- és integrációs teszt Kódanalízis – Java – FindBugs – PMD – CheckStyle Development Version Control System Compilation Unit and Integration Tests Static Analysis
  • 10. Static Analysis – No need for compilation or execution of the application – Formatting, structural and semantic rule checking – Can extend the workflow of continuous integration and improve it – In this research we used code analysis utilizing pattern matching 5 Verziókezelés Fordítás Fejlesztés Egység- és integrációs teszt Kódanalízis Verziókezelés Fordítás Fejlesztés Egység- és integrációs teszt Kódanalízis – Java – FindBugs – PMD – CheckStyle – JavaScript – ESLint – Facebook Infer, Flow – Tern – TAJS Development Version Control System Compilation Unit and Integration Tests Static Analysis
  • 11. – Thorough code analysis is time-consuming and resource-intensive – For large projects it can be too slow Problems to Solve 6 unit tests static analysis ☼ ☆☾☆
  • 12. – Thorough code analysis is time-consuming and resource-intensive – For large projects it can be too slow – Temporary solution: batching Problems to Solve 6 unit tests static analysis ☼ ☆☾☆ unit tests static analyis
  • 13. – Thorough code analysis is time-consuming and resource-intensive – For large projects it can be too slow – Temporary solution: batching Present results as soon and as fast as possible. Problems to Solve 6 unit tests static analysis ☼ ☆☾☆ unit tests static analyis
  • 14. Problems to Solve – Memory limits appear when... – Global rules are checked – Storing the structure in-memory – For large code repositories – Not being incremental – Batched execution simply does not cut it – Small change induces complete recheck 7
  • 15. Our Approach – Incremental methodology – Instead of batched execution – Update the prepared results with the effects of the change – Only store the required parts in the memory 8 analyzer Δ2.-1.1.
  • 16. VCS Workspace Abstact Syntax Tree Abstract Semantic Graph Well-formedness Rules Query Execution Database Main.js | ++---- Dependency.js | +++++- FIterator.js | ---- Parser.js | ++ Automatic Well-formedness Rule Evaluation Manual Execution and Data Extraction Querying and Transformation . discoverer ChangeProcessor.js CommandParser.js FileIterator.js iterators DepCollector.js FileDiscoverer.js InitIterator.js Main.js whitepages ConnectionMgr.js DependencyMgr.js neo4jValidation Report <!> <?> <.> Module declaration declarators items binding init left right Architecture overview 9
  • 17. VCS Workspace Abstact Syntax Tree Abstract Semantic Graph Well-formedness Rules Query Execution Database Main.js | ++---- Dependency.js | +++++- FIterator.js | ---- Parser.js | ++ Automatic Well-formedness Rule Evaluation Manual Execution and Data Extraction Querying and Transformation . discoverer ChangeProcessor.js CommandParser.js FileIterator.js iterators DepCollector.js FileDiscoverer.js InitIterator.js Main.js whitepages ConnectionMgr.js DependencyMgr.js neo4jValidation Report <!> <?> <.> Module declaration declarators items binding init left right Architecture overview 9
  • 18. VCS Workspace Abstact Syntax Tree Abstract Semantic Graph Well-formedness Rules Query Execution Database Main.js | ++---- Dependency.js | +++++- FIterator.js | ---- Parser.js | ++ Automatic Well-formedness Rule Evaluation Manual Execution and Data Extraction Querying and Transformation . discoverer ChangeProcessor.js CommandParser.js FileIterator.js iterators DepCollector.js FileDiscoverer.js InitIterator.js Main.js whitepages ConnectionMgr.js DependencyMgr.js neo4jValidation Report <!> <?> <.> Module declaration declarators items binding init left right Architecture overview 9
  • 19. VCS Workspace Abstact Syntax Tree Abstract Semantic Graph Well-formedness Rules Query Execution Database Main.js | ++---- Dependency.js | +++++- FIterator.js | ---- Parser.js | ++ Automatic Well-formedness Rule Evaluation Manual Execution and Data Extraction Querying and Transformation . discoverer ChangeProcessor.js CommandParser.js FileIterator.js iterators DepCollector.js FileDiscoverer.js InitIterator.js Main.js whitepages ConnectionMgr.js DependencyMgr.js neo4jValidation Report <!> <?> <.> Module declaration declarators items binding init left right Architecture overview 9
  • 20. Code Processing Steps 20 tokenizer source code tokens AST ASG parser scope analyzer tokenizer source code tokens AST ASG parser scope analyzer
  • 21. Code Processing Steps 21 tokenizer source code tokens AST ASG parser scope analyzer
  • 22. Code Processing Steps 22 tokenizer source code tokens AST ASG parser scope analyzer Sequence of statements formalized in a given language
  • 23. Code Processing Steps 23 tokenizer source code tokens AST ASG parser scope analyzer Sequence of statements formalized in a given language
  • 24. Code Processing Steps 24 tokenizer source code tokens AST ASG parser scope analyzer
  • 25. Code Processing Steps 25 tokenizer source code tokens AST ASG parser scope analyzer token – the shortest character sequence still having meaning.
  • 26. Code Processing Steps 26 tokenizer source code tokens AST ASG parser scope analyzer token – the shortest character sequence still having meaning.
  • 27. Code Processing Steps 27 tokenizer source code tokens AST ASG parser scope analyzer Token Token type VAR (Keyword) IDENTIFIER (Ident) ASSIGN (Punctuator) NUMBER (NumericLiteral) DIV (Punctuator) NUMBER (NumericLiteral) token – the shortest character sequence still having meaning.
  • 28. tokenizer source code tokens AST ASG parser scope analyzer Code Processing Steps 12 Module VariableDeclarationStatement VariableDeclaration VariableDeclarator BindingIdentifier name = `foo` BinaryExpression operator = `Div` LiteralNumericExpression value = 1.0 LiteralNumericExpression value = 0.0 declaration declarators items binding init left right
  • 29. tokenizer source code tokens AST ASG parser scope analyzer Code Processing Steps 12 Abstract Syntax Tree (AST) – Tree representation of – the grammar structure of – the sequence of tokens. Module VariableDeclarationStatement VariableDeclaration VariableDeclarator BindingIdentifier name = `foo` BinaryExpression operator = `Div` LiteralNumericExpression value = 1.0 LiteralNumericExpression value = 0.0 declaration declarators items binding init left right
  • 30. tokenizer source code tokens AST ASG parser scope analyzer Code Processing Steps 12 Module VariableDeclarationStatement VariableDeclaration VariableDeclarator BindingIdentifier name = `foo` BinaryExpression operator = `Div` LiteralNumericExpression value = 1.0 LiteralNumericExpression value = 0.0 declaration declarators items binding init left right
  • 31. tokenizer source code tokens AST ASG parser scope analyzer Code Processing Steps 13 Module VariableDeclarationStatement VariableDeclaration VariableDeclarator BindingIdentifier name = `foo` BinaryExpression operator = `Div` LiteralNumericExpression value = 1.0 LiteralNumericExpression value = 0.0 declaration declarators items binding init left right
  • 32. tokenizer source code tokens AST ASG parser scope analyzer Code Processing Steps 13 Module VariableDeclarationState VariableDeclaration VariableDeclarator BindingIdentifier name = `foo` BinaryE operato LiteralNumericExpression value = 1.0 declaration declarators items binding init left right
  • 33. tokenizer source code tokens AST ASG parser scope analyzer Code Processing Steps 13 Module VariableDeclarationState VariableDeclaration VariableDeclarator BindingIdentifier name = `foo` BinaryE operato LiteralNumericExpression value = 1.0 declaration declarators items binding init left right GlobalScope Scope Variable name = `foo` Reference accessibility = `Write` variables references children Declaration kind = `Var` declarations node astNode
  • 34. tokenizer source code tokens AST ASG parser scope analyzer Code Processing Steps Abstract Semantic Graph (ASG) – Graph, not necessarily tree. – Semantic information besides the syntactic structure. – Contains cross-edges → 13 Module VariableDeclarationState VariableDeclaration VariableDeclarator BindingIdentifier name = `foo` BinaryE operato LiteralNumericExpression value = 1.0 declaration declarators items binding init left right GlobalScope Scope Variable name = `foo` Reference accessibility = `Write` variables references children Declaration kind = `Var` declarations node astNode
  • 35. tokenizer source code tokens AST ASG parser scope analyzer Code Processing Steps 13 Module VariableDeclarationState VariableDeclaration VariableDeclarator BindingIdentifier name = `foo` BinaryE operato LiteralNumericExpression value = 1.0 declaration declarators items binding init left right GlobalScope Scope Variable name = `foo` Reference accessibility = `Write` variables references children Declaration kind = `Var` declarations node astNode
  • 39. Overview of the Approach 15 Verziókezelés Fordítás Fejlesztés Egység- és integrációs teszt Kódanalízis Verziókezelés Fordítás Fejlesztés Egység- és integrációs teszt Kódanalízis Development Version Control System Compilation Unit and Integration Tests Static Analysis
  • 40. Overview of the Approach 16
  • 41. Overview of the Approach 16 Version Control System Integrated Development Environment Git,VisualStudioCode
  • 42. Overview of the Approach 16 Version Control System Integrated Development Environment tokenizer source code tokens AST ASG parser scope analyzer Git,VisualStudioCode ShapeSecurityShift
  • 43. Overview of the Approach 16 Version Control System transformation Integrated Development Environment tokenizer source code tokens AST ASG parser scope analyzer Git,VisualStudioCode ShapeSecurityShift Java,Cypher
  • 44. Overview of the Approach 16 Version Control System transformation graph database Integrated Development Environment tokenizer source code tokens AST ASG parser scope analyzer Git,VisualStudioCode ShapeSecurityShift Java,Cypher Neo4j
  • 45. Overview of the Approach 16 Version Control System transformation graph database Integrated Development Environment tokenizer source code tokens AST ASG parser scope analyzer result processing Git,VisualStudioCode ShapeSecurityShift Java,Cypher Neo4j
  • 46. Overview of the Approach 16 Version Control System transformation graph database Integrated Development Environment tokenizer source code tokens AST ASG parser scope analyzer result processing Git,VisualStudioCode ShapeSecurityShift Java,Cypher Neo4j
  • 47. Overview of the Approach 16 Version Control System transformation graph database Integrated Development Environment tokenizer source code tokens AST ASG parser scope analyzer result processing Git,VisualStudioCode ShapeSecurityShift Java,Cypher Neo4j
  • 48. Overview of the Approach 16 Version Control System transformation graph database Integrated Development Environment tokenizer source code tokens AST ASG parser scope analyzer result processing Git,VisualStudioCode ShapeSecurityShift Java,Cypher Neo4j
  • 49. Overview of the Approach 16 Version Control System transformation graph database Integrated Development Environment tokenizer source code tokens AST ASG parser scope analyzer result processing Git,VisualStudioCode ShapeSecurityShift Java,Cypher Neo4j
  • 50. Overview of the Approach 16 Version Control System transformationtransformation graph database Integrated Development Environment tokenizer source code tokens AST ASG parser scope analyzer result processing result processing Git,VisualStudioCode ShapeSecurityShift Java,Cypher Neo4j
  • 51. Graph Pattern Matching 17 VariableDeclarator BindingIdentifier name = `foo` BinaryExpression operator = `Div` LNExpression value = 1.0 LNExpression value = 0.0
  • 52. Graph Pattern Matching – Graph pattern – A declarative, – graph-like formalism – expressing constraints. 17 VariableDeclarator BindingIdentifier name = `foo` BinaryExpression operator = `Div` LNExpression value = 1.0 LNExpression value = 0.0
  • 53. Graph Pattern Matching – Graph pattern – A declarative, – graph-like formalism – expressing constraints. 17 VariableDeclarator BindingIdentifier name = `foo` BinaryExpression operator = `Div` LNExpression value = 1.0 LNExpression value = 0.0 binding be right
  • 54. Graph Pattern Matching – Graph pattern – A declarative, – graph-like formalism – expressing constraints. 17 VariableDeclarator BindingIdentifier name = `foo` BinaryExpression operator = `Div` LNExpression value = 1.0 LNExpression value = 0.0 binding be right
  • 55. Graph Pattern Matching – Graph pattern – A declarative, – graph-like formalism – expressing constraints. 17 BindingIdentifier name = `foo` Graphpatternquery expressedinCypher lookingfora divisionbyzero binding Resultsofthe patternmatching
  • 56. Use Cases static analysis – Searching for local bad smells (linter warnings) – without a case – value set more than once – Not used variable – Global rules – Unreachable code parts – Framework – Freely extendable – User-defined rules – Easier to use than visitor pattern solutions 18
  • 57. Use Cases transformation Control Flow Graph (CFG) – Graph representation of – every possible statement sequence – during code execution. 19
  • 58. Use Cases transformation Control Flow Graph (CFG) – Graph representation of – every possible statement sequence – during code execution. 19 statement
  • 59. Use Cases transformation Control Flow Graph (CFG) – Graph representation of – every possible statement sequence – during code execution. 19 statement statement
  • 60. Use Cases transformation Control Flow Graph (CFG) – Graph representation of – every possible statement sequence – during code execution. 19 statement statement if
  • 61. Use Cases transformation Control Flow Graph (CFG) – Graph representation of – every possible statement sequence – during code execution. 19 statement statement if condition
  • 62. Use Cases transformation Control Flow Graph (CFG) – Graph representation of – every possible statement sequence – during code execution. 19 statement statement statement statement if condition
  • 63. Use Cases transformation Control Flow Graph (CFG) – Graph representation of – every possible statement sequence – during code execution. 19 statement statement statement statement if statement condition
  • 64. Use Cases transformation Control Flow Graph (CFG) – Graph representation of – every possible statement sequence – during code execution. 19 statement statement statement statement error if statement condition
  • 65. Use Cases transformation Control Flow Graph (CFG) – Graph representation of – every possible statement sequence – during code execution. 19 statement statement statement statement statement error if statement condition
  • 66. Use Cases transformation Control Flow Graph (CFG) – Graph representation of – every possible statement sequence – during code execution. 19 statement statement statement statement statement error if statement condition
  • 67. Use Cases transformation Control Flow Graph (CFG) – Graph representation of – every possible statement sequence – during code execution. 19 statement statement statement statement statement error if return statement condition
  • 68. error Use Cases test generation 20 statement statement statement statement statement if return condition statement
  • 69. error Use Cases test generation – Inspecting control flows – Is the given statement reachable given the constraints on the edges? – Which one is the shortest route? 20 statement statement statement statement statement if return condition statement
  • 70. error Use Cases test generation – Inspecting control flows – Is the given statement reachable given the constraints on the edges? – Which one is the shortest route? 20 statement statement statement statement statement if return condition statement
  • 71. error Use Cases test generation – Inspecting control flows – Is the given statement reachable given the constraints on the edges? – Which one is the shortest route? 20 statement statement statement statement statement if return condition statement
  • 72. error Use Cases test generation – Inspecting control flows – Is the given statement reachable given the constraints on the edges? – Which one is the shortest route? 20 statement statement statement statement statement if return condition statement
  • 73. error Use Cases test generation – Inspecting control flows – Is the given statement reachable given the constraints on the edges? – Which one is the shortest route? 20 statement statement statement statement statement if return condition statement
  • 74. error Use Cases test generation – Inspecting control flows – Is the given statement reachable given the constraints on the edges? – Which one is the shortest route? 20 statement statement statement statement statement if return condition statement
  • 75. error Use Cases test generation – Inspecting control flows – Is the given statement reachable given the constraints on the edges? – Which one is the shortest route? 20 statement statement statement statement statement if return condition statement
  • 76. error Use Cases test generation – Inspecting control flows – Is the given statement reachable given the constraints on the edges? – Which one is the shortest route? 20 statement statement statement statement statement if return condition statement
  • 77. error Use Cases test generation – Inspecting control flows – Is the given statement reachable given the constraints on the edges? – Which one is the shortest route? – Producing test input for dynamic testing 20 statement statement statement statement statement if return condition statement
  • 78. Use Cases type inference – Supporting dynamically typed languages – Python – JavaScript / ECMAScript 21
  • 79. Use Cases type inference – Supporting dynamically typed languages – Python – JavaScript / ECMAScript 21 http://marijnhaverbeke.nl/blog/tern.html
  • 80. Use Cases impact analysis – Adapting to the continuous integration workflow – Handling multiple branches – Following the modifications in a branch – File-level incremental granularity – Giving differential reports to the developers 22
  • 81. Why Neo4j? +++ – Quick prototyping – Supporting transactions – Great tooling -- – Not scaling well – Only disk-based 23
  • 82. Remarks MERGE – MATCH or CREATE – Great for the lazy – Can be expensive – Possible solutions: – Less MERGE – Separating queries – Create first if not present – Use MATCH instead of MERGE – Prevention – Prepare the structure when inserting the data 24
  • 84. Remarks if-then-else – Not a language element in Cypher – Can be solved with a trick – Verrrrrry sloww – Solution: – Two smaller, disjunct cases 26
  • 85. Remarks if-then-else – Not a language element in Cypher – Can be solved with a trick – Verrrrrry sloww – Solution: – Two smaller, disjunct cases 26
  • 92. Remarks if-then-else 28 ∞ vs 15 sec These are not chickens.
  • 93. Remarks reachability – Transitive closure without length constraints is slow. – Transitive closure over repeating node/edge pattern is only possible using tricks. 29 A B *
  • 94. Remarks reachability – Transitive closure without length constraints is slow. – Transitive closure over repeating node/edge pattern is only possible using tricks. 29 A B *
  • 95. Remarks reachability – Transitive closure without length constraints is slow. – Transitive closure over repeating node/edge pattern is only possible using tricks. 29 A B *
  • 96. Conclusions – Source code analyzer framework – Searching for global error patterns – Close to real time feedback – Type inference possible – Test input generation possible – Approach for both dynamically and statically typed languages – Using Neo4j for – Storing – Pattern matching – Transforming – Version control – Storing metadata 30
  • 97. – Our work was supported by: – ÚNKP* – Microsoft Azure for Research – MTA-BME Lendület Program Project Details – The framework prototype is open- source. https://github.com/ ftsrg/codemodel-rifle 31 * Supported by the ÚNKP-16-2-I. New National Excellence Program of the Ministry of Human Capacities.
  • 98. Project Details – Supervisors – Ádám Lippai – Dávid Honfi – Gábor Szárnyas – Helped my research – Tamás Soma Lucz – Industrial case study 32