modular heap analysis of higher order programs ravichandhran madhavan + * ganesan ramalingam * kapil...

Modular Heap Analysis Of Higher Order ProgramsRavichandhran Madhavan + *Ganesan Ramalingam *Kapil Vaswani *

* Microsoft Research India+ EPFL, Switzerland

Goal 1: Analyze Modularly

• Compute succinct summaries for procedures

• Summaries: total functions approximating the relational semantics

𝛾 (𝑆𝑢𝑚𝑚𝑎𝑟 𝑦 𝑃)

Input State

Output States

[𝑃 ]𝑐⊇

Goal 2: Track Heap Information

• The summary of a procedure should capture the transformation of the input mutable heap

Goal 3: Analyze HO programs

• Should be able to summarize higher order procedures• Input state includes data as well as code

Challenge• Indirect procedure calls esp. Call backs• Virtual method calls, function pointer calls, lambda expressions

Foo(PTR* p , FP* fp){ *p = (**fp)(0);}

Count() { iter = this.iterator(); i = 0; while(iter.HasNext()) { iter.next(); i++; } }

Challenge• All widely used languages support Higher Order constructs

But how do existing modular analyses

handle them ?

A Common Hack• Estimate the targets of the indirect calls through an

inexpensive analysis E.g.

• CHA, RTI analysis for OO programs• Light weight pointer analysis …

• Construct a conservative call graph

• Analyze bottom up

Limitations of the Hack• Over-approximated targets

• A call-graph is necessarily context insensitive for HO programs

A

C

B

D

EB’s context

A’s context

Limitations of the Hack• Inability to construct client independent summaries

Foo(FP* fp){ (*fp)(…);}

m1(){ …}

C1(){ Foo(m1);}

m2(){ …}

C2(){ Foo(m2)}

Resolved to m1

Summary:

Limitations of the Hack

• Reuse of summaries possible only within an analysis• Need to analyze libraries together with clients• Need to reanalyze libraries for each new client

Doesn’t allow library compositional analysis

Our approach

• Use existing techniques for summarizing first-order code segments:

• [Whaley, Salcianu, Rinard, OOPSLA ‘99, VMCAI ’04]• [Madhavan et al., SAS ‘11]

• Retain the call backs in the summaries

Our approach

• Perform as much simplification as possible without the knowledge of the calling context

• Eliminate fully resolved calls from the summaries

Enables efficient library compositional analysis

Illustration1

7

2

4

3

5

6

*fp(a,b)𝑆24

𝑆13𝑆12

𝑆56

𝑆67𝑆47

Illustration1

7

2

4

3

5

6

*fp(a,b)𝑆24

𝑆13𝑆12

𝑆56

𝑆67𝑆47

Illustration

3

5

6

*fp(a,b)

𝑆13

𝑆56

𝑆67

7

1

𝜏17

Illustration

3

5

*fp(a,b)

7

1

𝜏17

𝜏57

𝜏13

Exploiting Local Context

3

5

*fp(a,b)

7

1

𝜏17

𝜏57=(𝜏 𝑙 ,𝜏′)

𝜏13

3

5

*fp(a,b)

7

1

𝜏17

𝜏 ′

𝜏13

𝜏 𝑙∘𝜏13

Frame Rule

Exploiting Local Context

3

5

*fp(a,b)

7

1

𝜏17

𝜏57=(𝜏 𝑙 ,𝜏′)

𝜏13

3

5

*fp(a,b)

7

1

𝜏 ′

𝜏13Frame Rule

Flow Insensitive Abstraction

3

5

*fp(a,b)

7

1

𝜏

𝜏

𝜏

𝜏

3

5

*fp(a,b)

7

1

𝜏57

𝜏13

𝜏17

𝜏=𝜏13⊔𝜏17⊔𝜏57

Flow Insensitive Abstraction

(𝜏 , \{𝑐1 ,… ,𝑐𝑘 \})

HO summary = First order summary +

set of call backs

𝑐1…𝑐𝑘

2

3

4

1

𝜏

𝜏

𝜏

𝜏

Composition Operation

(𝜏1 ,𝜔1)𝑆1;𝑆2 ;…;𝑆𝑛 𝑐1…𝑐𝑘

2

3

4

1

𝜏1

𝜏1

𝜏1

𝜏1

(𝜏2 ,𝜔2)𝑆𝑛+1;…;𝑆𝑚 𝑑1…𝑑 𝑗

6

7

8

5

𝜏2

𝜏2

𝜏2

𝜏2

ID

Composition Operation

(𝜏1 ,𝜔1)𝑆1;𝑆2 ;…;𝑆𝑛

(𝜏2 ,𝜔2)𝑆𝑛+1;…;𝑆𝑚

𝑐1…𝑐𝑘

2

3

4

1

𝜏1

𝜏1

𝜏1

𝜏1

𝑑1…𝑑 𝑗

6

7

8

5

𝜏2

𝜏2

𝜏2

𝜏2

ID𝜏2∘𝜏1

Composition Operation

• where , is the composed abstract state

• When the first order summaries (and hence composition) are isotonic:

(𝜏2 ,𝜔2 )∘ (𝜏1 ,𝜔1 )=(𝜏2∘𝜏1 ,𝜔1∪𝜔2)

Handling Direct Calls• Handle direct calls via summary composition

(𝜏𝑒 ,𝜔𝑒)

(𝜏𝑟 ,𝜔𝑟)

¿

Call backs in the callee are inlined in the caller

Indirect call Resolution

(𝜏 , \{𝑐1 \})

3

5

7

1

𝜏 𝜏

𝜏

𝑐1B (𝜏𝑏 , \{𝑐2 \})

(𝜏 ∘𝜏𝑏 )∗∘𝜏

(𝜏2 , \{𝑐1 ,𝑐2 \})

𝜏

A

Indirect Call Resolution

A

(𝜏 , \{𝑐1 \})

calls B

B

(𝜏4❑ , \{𝑐1 ,𝑐2 ,𝑐3 \})(𝜏2

❑ , \{𝑐1 ,𝑐2 \})

calls C

C

(𝜏3❑ , \{𝑐1 ,𝑐2 ,𝑐3 \})

calls B calls A

(𝜏𝑏 , \{𝑐2 \})

(𝜏𝑐 , \{𝑐3 \})

Indirect Call Resolution

A

(𝜏 𝑖− 1❑ ,𝜔 𝑖−1)

…

BC

(𝜏𝑏 , \{𝑐2 \})

(𝜏𝑐 , \{𝑐3 \})

(𝜏 𝑖❑ ,𝜔𝑖)…..

Fixed point

Eliminating resolved calls

(a) is Non escaping.Unreachable from

indirect calls and prestate

𝑝2

(b) and are unreachable from prestate and other call backs

𝑓𝑝1

*fp1

…

Resolved calls

Foo

*fp2

𝑓𝑝2 …

𝑝1Bar

Experimental Evaluation

• Applied to Purity/Side-effects Analysis for C# libraries

• Every method is classified as:

• Pure – No side-effects • Conditionally Pure – Purity depends on the calling context• Impure – Has side-effects• Impure and Incomplete – Has side-effects and can have more

depending on the calling context

Experimental ResultsBenchmark LOC Pure C-Pure Impure I-Impure Time

DocX 10K ~ 1 min

FB APIs 2.2% 32%

Data Disp. 57%

Test APIs

Json Libs

Quickgraph

Refactory libs 30% 8%

Utility Libs 32% 8%

PDF libs 28.4%

GPS libs 250K ~ 2 hrs

10 – 20%

15 – 30%

20 – 30%

2 – 27 min

Analysis StatisticsBenchmark Unresolved

CallsNon Escaping Abs. Objects

DocX

FB APIs 9%

Data Disp.

Test APIs

Json Libs 7.3

Quickgraph

Refactory libs

Utility Libs

PDF libs 37%

GPS libs 5.9

2 – 4

10 – 33 %

Comparison with CHA CG based Bottom up Analysis

Benchmark Time # of SCCs Avg. Scc size

DocX 12x 0 NA

FB APIs 11x 3x 1.5x

Data Disp. 6x 6x

Test APIs 6x 2x 1.25x

Json Libs 2x 6x

Quickgraph 11x 33x

Refactory libs 1.4x 5.6x

Utility Libs 30x 4x 12x

PDF libs 2x 3.5x 1.5x

Conclusion

• A principled approach

• Formalized as an Abstract Interpretation

• A generic theory agnostic to the underlying compositional heap analysis

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