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JTL – The Java Tools Language Intuitively querying Java programs. Itay Maman The Technion – Israel Institute of Technology Joint work with Tal Cohen and Yossi Gil. October 24, 2006 OOPSLA’06, Portland. Wanted: A class with a static method returning float. The regular expression approach:

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jtl the java tools language intuitively querying java programs

JTL – The Java Tools LanguageIntuitively querying Java programs

Itay Maman

The Technion – Israel Institute of Technology

Joint work with Tal Cohen and Yossi Gil

October 24, 2006

OOPSLA’06, Portland

wanted a class with a static method returning float
Wanted: A class with a static method returning float
  • The regular expression approach:

static float


    • (Inaccurate)
  • The XQuery approach:



and @modifiers="static"]

    • (Too complicated to be used interactively)
  • There’s also a JTL way…
in this talk
In this Talk
  • Motivation, Design considerations
  • JTL 101
  • Evaluation algorithm
usage scenarios
Usage scenarios
  • Existing solutions: CodeQuest, BDDBDDB, XIRC, JQuery, ALPHA, …
  • Software engineering tools
    • Code lookup, (anti) pattern detection, …
  • Programming languages
    • Static pointcuts in aspect-oriented programming
    • Mixins (Jam)
    • Concepts (ConceptC++)
    • Pluggable type systems (JavaCOP)
queries embedded in the program
Queries embedded in the Program
  • Java 5 already provides code queries
    • Syntax close to Java
    • Limited expressive power
  • A hypothetical alternative (SQL)
    • Syntax significantly diverts from Java
    • Difficult to write/read

public<T extends Cloneable & Serializeable>

void f(T ts) { ... }

public<SELECT * FROM extends WHERE (super = 'Cloneable' or super = 'Serializeable') and subclass='T' ...>

void f(T ts) { … }

mission statement
Mission Statement

Define a logic language (over Java programs) which:

    • Is as powerful as Datalog
    • Has an intuitive, readable syntax
    • Has a simple data model
  • Other solutions fall short
jtl unary predicates
JTL: Unary Predicates
  • A simple predicate:
    • Three sub-queries
    • Space (and comma) denotes conjunction
  • The subject variable
    • The hash symbol, #, denotes the subject variable
    • A variable in a prefix position => becomes the subject of the callee
      • If not specified, defaults to # (the subject)
  • static, final, etc. are library predicates, not keywords.

const := static final field;

const := #static #final #field;

jtl binary predicates
JTL: Binary Predicates
  • Binary predicates
    • Accept a subject + an additional parameter (in a postfix position)
    • (Names of Variables/Parameters must begin with a capital letter)
  • Subject chaining: Repeatedly using a variable as a subject
    • Can be rewritten using the ampersand operator:

has_static_member X := class declares X, X static;

extends+ Y := extends Y | extends Y', Y' extends+ Y;

my_generic_method T :=

method, T extends Serializeable, T extends Cloneable;

my_generic_method T := method, T extends Serializeable & Cloneable;

jtl quantification
JTL: Quantification { }

some_class := class {

static int field;

field => private;

many method;


  • Quantifying the members of a class
    • Subject variable iterates over a set
    • Each condition must hold
    • Quantifiers:
      • Unary: exists, all, no, one, many
      • Binary: => (implies)
  • Quantifying with a generator
  • Default generator for classes: members:
  • Default quantifier: exists


no_abstract_super := class extends+: {

no abstract;


other features
Other Features

Type literal

sortable_list := class extends /java.util.ArrayList {

static final long field 'SerialVersionUID;

public void sort();


Name matching

(Regular expression)

Method signature pattern

  • Type system
    • MEMBER: constructors, methods, fields
    • TYPE: interfaces, classes, primitive types, enum types, annotation types
delving into methods
Delving into methods
  • The idea: Symbolically name intermediate results
  • The SCRATCH type
    • Constants
    • Values passed to parameters
    • Values pushed onto the stack by JVM instructions
  • Represent code sites
  • Library predicates for checking whether a scratch is:
    • Copied
    • Used in a calculation
    • Written/Read from a field
    • Stored/loaded from the local variable array
    • ...
scratches in action
Scratches in Action
  • Default generator expression for a method is: scratches:
    • Generates all scratches of the method

copy* S := is S | copy S | copy S', S' copy* S;

chain_method := !void method {

returned => copy* S, S this;


getter_method := !void method declared_by C,

C declares F {

returned => copy* V, V getfield [I,F], I copy* S,

S this;


evaluation scheme
Evaluation scheme
  • JTL is translated into Datalog
  • The Datalog program is evaluated top-down
  • “need to know basis”
    • The JTL evaluation algorithm does not see more information than it needs
    • Only necessary facts are extracted from the native predicates

=> No database setup is needed

- Which is a bottleneck in similar applications

q what s special about p2
Q: What’s special about p2?

p1 := # extends A, A abstract;

p2 := A extends #, A abstract;

p3 := A extends #, A abstract, # p4 A;

p4 := ...

  • A: Result of p2 is infinite
    • Result of p3 may be infinite (depending on the definition of p4)
  • Consequences
    • Use exhaustive iteration
      • Assume a closed world, usually: the classpath
      • Performance penalty
    • Beware: Fragile results!!

(Suppose we invoke p1, p2, p3 with some type passed in as a subject)

  • Can we determine whether a query is fragile?
    • In the general case: No
    • In JTL’s case: Yes
      • Requires the native predicates to maintain a simple property
      • Recently proven by Gil and Zarivach
  • JTL’s current implementation is more conservative
    • Subject is not known => a runtime error
    • Works well in most practical situations

JTL: A powerful language, intuitive notation, simple data model

  • Java-like clauses become Datalog queries
  • Queries on behavior
  • Efficient implementation