Inferring Behavioral Specifications from Large-scale Repositories by Leveraging Collective Intelligence

Robert Dyer

Bowling GreenState University

Tien N. Nguyen

Iowa State University

Hridesh Rajan

Iowa StateUniversity

Gary T. Leavens

University ofCentral Florida

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Lack of specifications* is a critical SE problem

*specifications: {Pre} S {Post} i.e. behavioral specifications

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void m(…) { … }

void m(…) { // pre: P … // post: Q}

SpecificationInference

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Our Goal

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SpecGuru

A specification inference infrastructure

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Our Goal

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Related work: focuses on single projects [Ernst et al. 99] [Flanagan et al. 01] [Weimer et al. 05]

[Ramanathan et al. 07] [Wei et al. 11]

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How can we effectivelyleverage these projects?

Big Code:large number of readily available projects

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Aim: multi-step specification inference- Bootstrap using consensus- Propagate using similarity- Last mile using decomposition

Consensus DecompositionSimilarity

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Challenge:

separate project specific constraints (chaff)mixed with API-specific preconditions

(wheat)

Consensus DecompositionSimilarity

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Our initial work1:

use consensus acrosslarge number of projects

to infer API-specific preconditions

Consensus DecompositionSimilarity

[1] FSE’14 – H. Nguyen, Dyer, T. Nguyen, Rajan

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Key Ideas

Preconditions can be mined from guarded conditions at the call sites of the code using the APIs

Preconditions mined from multiple projects in a large-scale code corpus can be used to filter out chaff

Consensus DecompositionSimilarity

void m(…) { … if (pred) lib.api(); …}

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Challenge:

not all APIs are widely used

Consensus DecompositionSimilarity

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Key Ideas

Similar code should havesimilar specifications

Cluster pairs of (code, spec)from previous phase to findsimilar code/specs

Consensus DecompositionSimilarity

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Challenge:

unique API code would still be un-specified

Consensus DecompositionSimilarity

16Consensus DecompositionSimilarity

Key Ideas

Revision history and slicing can help decompose code into fragments

Simple fragments may already be specified by prior phases – compose new specification from those pieces

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Lack of specifications,a critical hurdle for high assurance SE,

can be overcome by leveraging big code mining,esp. consensus, similarity, and decomposition.

boa.cs.iastate.edu