Sorting and Transforming Program Repair Ingredients via Deep Learning Code Similarities

Martin White*, Michele Tufano*, Matías Martínez†, Martin Monperrus‡, Denys Poshyvanyk*

*College of William and Mary, Williamsburg, Virginia, USA

†Université Polytechnique Hauts-de-France, Valenciennes, France

‡KTH Royal Institute of Technology, Stockholm, Sweden

(Software) bugs are obnoxious.

Automated Program Repair

Transformation of an unacceptable behavior of a program execution into an acceptable one according to a specification.

Automated program repair is hard.

Automated program repair is really hard.

Background

Generate-and-validate repair techniques (generally) search for statement-level modifications and validate patches against the test suit.

 

Correct-by-construction repair techniques use program analysis and program synthesis to construct code with particular properties.

 

public final class MathUtils {
    public static boolean equals(double x, double y) {
        return (Double.isNaN(x) && Double.isNaN(y)) || x == y;
    }
}
public final class MathUtils {
    public static boolean equals(double x, double y) {
        return equals(x, y, 1);
    }
}
if (max_range_endpoint < eol_range_start)
    max_range_endpoint = eol_range_start;

printable_field = xzalloc(max_range_endpoint/CHAR_BIT+1);
if (max_range_endpoint < eol_range_start)
    max_range_endpoint = eol_range_start;

if (1)
    printable_field = xzalloc(max_range_endpoint/CHAR_BIT+1);
if (AAA)
    max_range_endpoint = BBB;

if (CCC)
    printable_field = xzalloc(max_range_endpoint/CHAR_BIT+1);
if (0)
    max_range_endpoint = eol_range_start;

if (!(max_range_endpoint == 0))
    printable_field = xzalloc(max_range_endpoint/CHAR_BIT+1);

S. Mechtaev, J. Yi, and A. Roychoudhury, Angelix: Scalable Multiline Program Patch Synthesis via Symbolic Analysis, ICSE 2016.

The Redundancy Assumption

Large programs contain the seeds of their own repair [Martinez'14,Barr'14]

  • Line-level. Most redundancy is localized in the same file [Martinez'14]
  • Token-level. Repairs need never invent a new token [Martinez'14]

On the problem of navigating complex fix spaces, we use code similarities to intelligently select and adapt program repair ingredients.

Why?

Patches that use novel expressions are unattainable with existing redundancy-based repair techniques.

Technical Approach

  • Recognition

  • Learning

  • Repair

Recognition

  • Build source model

  • Build corpora

  • Normalize corpora

package org.apache.commons.math.util;

import java.math.BigDecimal;
import java.math.BigInteger;
import java.util.Arrays;

import org.apache.commons.math.MathRuntimeException;
import org.apache.commons.math.exception.util.Localizable;
import org.apache.commons.math.exception.util.LocalizedFormats;
import org.apache.commons.math.exception.NonMonotonousSequenceException;

/**
 * Some useful additions to the built-in functions in {@link Math}.
 * @version $Revision$ $Date$
 */
public final class MathUtils {

    /** Smallest positive number such that 1 - EPSILON is not numerically equal to 1. */
    public static final double EPSILON = 0x1.0p-53;

    /** Safe minimum, such that 1 / SAFE_MIN does not overflow.
     * <p>In IEEE 754 arithmetic, this is also the smallest normalized
     * number 2<sup>-1022</sup>.</p>
     */
    public static final double SAFE_MIN = 0x1.0p-1022;

MathUtils.java

Learning

  • Train language model

  • Encode fragments

  • Cluster identifiers

Repair

  • Core repair loop

  • Sorting ingredients

  • Transforming ingredients

Recognition

  • Build source model

  • Build corpora

  • Normalize corpora

org apache commons math ode events public EventHandler int STOP 0 int RESET_STATE 1 int RESET_DERIVATIVES 2
org apache commons math ode nonstiff public MidpointIntegrator RungeKuttaIntegrator private static final double STATIC_C
org apache commons math distribution org apache commons math MathException public ContinuousDistribution Distribution
org apache commons math distribution org apache commons math MathException public HasDensity P double density P x
org apache commons math genetics java util List public PermutationChromosome T List T decode List T sequence
org apache commons math optimization java io Serializable public GoalType Serializable MAXIMIZE MINIMIZE
org apache commons math linear public AnyMatrix boolean isSquare int getRowDimension int getColumnDimension
org apache commons math stat ranking public TiesStrategy SEQUENTIAL MINIMUM MAXIMUM AVERAGE RANDOM
org apache commons math genetics public CrossoverPolicy ChromosomePair crossover Chromosome first Chromosome second
org apache commons math distribution public DiscreteDistribution Distribution double probability double x
org apache commons math stat ranking public NaNStrategy MINIMAL MAXIMAL REMOVED FIXED
org apache commons math stat ranking public RankingAlgorithm double rank double data
org apache commons math genetics public SelectionPolicy ChromosomePair select Population population
org apache commons math genetics public StoppingCondition boolean isSatisfied Population population
org apache commons math genetics public MutationPolicy Chromosome mutate Chromosome original
org apache commons math public Field T T getZero T getOne
org apache commons math optimization general public ConjugateGradientFormula FLETCHER_REEVES POLAK_RIBIERE
org apache commons math random public RandomVectorGenerator double nextVector
org apache commons math random public NormalizedRandomGenerator double nextNormalizedDouble
org apache commons math genetics public Fitness double fitness

File-level corpus

Learning

  • Train language model

  • Encode fragments

  • Cluster identifiers

Repair

  • Core repair loop

  • Sorting ingredients

  • Transforming ingredients

Recognition

  • Build source model

  • Build corpora

  • Normalize corpora

org apache commons math ode events public EventHandler int STOP <INT> int RESET_STATE <INT> int RESET_DERIVATIVES <INT>
org apache commons math ode nonstiff public MidpointIntegrator RungeKuttaIntegrator private static final double STATIC_C
org apache commons math distribution org apache commons math MathException public ContinuousDistribution Distribution
org apache commons math distribution org apache commons math MathException public HasDensity P double density P x
org apache commons math genetics java util List public PermutationChromosome T List T decode List T sequence
org apache commons math optimization java io Serializable public GoalType Serializable MAXIMIZE MINIMIZE
org apache commons math linear public AnyMatrix boolean isSquare int getRowDimension int getColumnDimension
org apache commons math stat ranking public TiesStrategy SEQUENTIAL MINIMUM MAXIMUM AVERAGE RANDOM
org apache commons math genetics public CrossoverPolicy ChromosomePair crossover Chromosome first Chromosome second
org apache commons math distribution public DiscreteDistribution Distribution double probability double x
org apache commons math stat ranking public NaNStrategy MINIMAL MAXIMAL REMOVED FIXED
org apache commons math stat ranking public RankingAlgorithm double rank double data
org apache commons math genetics public SelectionPolicy ChromosomePair select Population population
org apache commons math genetics public StoppingCondition boolean isSatisfied Population population
org apache commons math genetics public MutationPolicy Chromosome mutate Chromosome original
org apache commons math public Field T T getZero T getOne
org apache commons math optimization general public ConjugateGradientFormula FLETCHER_REEVES POLAK_RIBIERE
org apache commons math random public RandomVectorGenerator double nextVector
org apache commons math random public NormalizedRandomGenerator double nextNormalizedDouble
org apache commons math genetics public Fitness double fitness

Normalized file-level corpus

Learning

  • Train language model

  • Encode fragments

  • Cluster identifiers

Repair

  • Core repair loop

  • Sorting ingredients

  • Transforming ingredients

Neural network language model

Recognition

  • Build source model

  • Build corpora

  • Normalize corpora

Learning

  • Train language model

  • Encode fragments

  • Cluster identifiers

Repair

  • Core repair loop

  • Sorting ingredients

  • Transforming ingredients

return (Double.isNaN(x) && Double.isNaN(y)) || x == y;

Recognition

  • Build source model

  • Build corpora

  • Normalize corpora

Learning

  • Train language model

  • Encode fragments

  • Cluster identifiers

Repair

  • Core repair loop

  • Sorting ingredients

  • Transforming ingredients

Math-63 Identifiers' Embeddings

vecAbsoluteTolerance

vecRelativeTolerance

maxStep

minStep

nSteps

scalRelativeTolerance

scalAbsoluteTolerance

blockColumn

blockEndRow

blockStartColumn

columnsShift

iRow

jColumn

blockRow

absAsinh

cosaa

defaultMaximalIterationCount

tolerance

y3

x3

cosab

sinb

absAtanh

dstWidth

srcEndRow

pBlock

srcBlock

srcWidth

absoluteAccuracy

functionValueAccuracy

yMin

relativeAccuracy

oldt

oldx

oldDelta

delta

tol1

steadyStateThreshold

maxDenominator

upperBounds

SAFE_MIN

MIN_VALUE

stop

NEGATIVE_INFINITY

DEFAULT_EPSILON

accuracy

maxAbsoluteValue

tol

stepEnd

dstPos

srcPos

mIndex

srcRow

srcStartRow

cosa

sina

cotanFlag

cosb

lastTime

blockEndColumn

blockStartRow

nextGeneration

population

populationLimit

rln10b

rln10a

absSinh

endIndex

rowsShift

maxColSum

minRatioPositions

errfac

stopTime

eps

iterationCount

chromosomes

maxDegree

outBlock

totalEvaluations

Recognition

  • Build source model

  • Build corpora

  • Normalize corpora

Learning

  • Train language model

  • Encode fragments

  • Cluster identifiers

Repair

  • Core repair loop

  • Sorting ingredients

  • Transforming ingredients

Stmt 1

Stmt 2

Stmt 3

Pass/Fail

Entity

T

1

T

2

T

3

T

4

T

5

P

F

P

F

P

Test Cases

Fault Localization

Repair Operators

  • InsertOp
  • RemoveOp
  • ReplaceOp

Recognition

  • Build source model

  • Build corpora

  • Normalize corpora

Learning

  • Train language model

  • Encode fragments

  • Cluster identifiers

Repair

  • Core repair loop

  • Sorting ingredients

  • Transforming ingredients

MathUtils::equals(double, double)

public final class MathUtils {

    /** Safe minimum, such that 1 / SAFE_MIN does not overflow.
     * <p>In IEEE 754 arithmetic, this is also the smallest normalized
     * number 2<sup>-1022</sup>.</p>
     */
    public static final double SAFE_MIN = 0x1.0p-1022;

    /**
     * Returns true iff they are equal as defined by
     * {@link #equals(double,double,int) equals(x, y, 1)}.
     *
     * @param x first value
     * @param y second value
     * @return {@code true} if the values are equal.
     */
    public static boolean equals(double x, double y) {
        return (Double.isNaN(x) && Double.isNaN(y)) || x == y;
    }

    public static boolean equals(double x, double y, double eps) {
        return equals(x, y, 1) || FastMath.abs(y - x) <= eps;
    }

}

Recognition

  • Build source model

  • Build corpora

  • Normalize corpora

Learning

  • Train language model

  • Encode fragments

  • Cluster identifiers

Repair

  • Core repair loop

  • Sorting ingredients

  • Transforming ingredients

DeepRepair Patch

--- a/src/main/java/org/apache/commons/math/util/MathUtils.java	
+++ b/src/main/java/org/apache/commons/math/util/MathUtils.java	
@@ -181,7 +181,7 @@
     }

     public static boolean equals(double x, double y) {
-        return ((Double.isNaN(x)) && (Double.isNaN(y))) || (x == y);
+        return (equals(x, y, 1)) || ((FastMath.abs((y - x))) <= (SAFE_MIN));
     }

     public static boolean equalsIncludingNaN(double x, double y) {

Human-written Patch

--- a/src/main/java/org/apache/commons/math/util/MathUtils.java
+++ b/src/main/java/org/apache/commons/math/util/MathUtils.java
@@ -414,7 +414,7 @@ public final class MathUtils {
      * @return {@code true} if the values are equal.
      */
     public static boolean equals(double x, double y) {
+        return equals(x, y, 1);
-        return (Double.isNaN(x) && Double.isNaN(y)) || x == y;
     }

Recognition

  • Build source model

  • Build corpora

  • Normalize corpora

Learning

  • Train language model

  • Encode fragments

  • Cluster identifiers

Repair

  • Core repair loop

  • Sorting ingredients

  • Transforming ingredients

Empirical Validation

  • Research questions (see paper for specifics)
    • RQ1. Evaluated sorting in isolation.
    • RQ2. Evaluated transforming in isolation.
    • RQ3. Evaluated sorting with the ability to transform.
    • RQ4. Conducted a quality study.
  • Data collection procedure
  • Analysis procedure

Data Collection Procedure

  • Recognition
    • Spoon
    • File-, type-, and executable-level corpora
    • Normalized chars, floats, ints, and strings
  • Learning
    • word2vec
    • Recursive autoencoders
    • k-means and simulated annealing
  • Repair
    • Defects4J: 6 Java projects including 374 buggy program revisions
    • GZoltar (Ochiai); Astor 3-hour evolutionary loop
    • 20,196 trials (374 revisions, 6 strategies, 3 scopes, 3 seeds)
    • 2,616 days (62,784 hours) of computation time

Analysis Procedure

  • Quantitative (Effectiveness)
    • Compare # test-adequate patches using Wilcoxon with Bonferroni
    • Compute difference between sets of test-adequate patches
    • Compare # attempts to generate test-adequate patches using Mann-Whitney with Bonferroni
    • Compute # attempts to generate a compilable ingredient
  • Qualitative (Correctness)
    • Correctness
    • Confidence
    • Readability

Empirical Results

  • Six bugs were unlocked by DeepRepair configurations
  • DeepRepair finds compilable ingredients faster than jGenProg
  • Neither yields test-adequate patches in fewer attempts (on average)
  • Nor finds significantly more patches than jGenProg
  • Notable differences between DeepRepair and jGenProg patches
  • No significant difference in quality

Conclusion

  • Patches that use novel expressions are unattainable with existing redundancy-based repair techniques.
  • We use code similarities to intelligently select and adapt ingredients.

Recognition

  • Build source model

  • Build corpora

  • Normalize corpora

Learning

  • Train language model

  • Encode fragments

  • Cluster identifiers

Repair

  • Core repair loop

  • Sorting ingredients

  • Transforming ingredients

  • Key results
    • DeepRepair finds patches that cannot be found by existing redundancy-based repair techniques.
    • We conducted a computationally intensive empirical study that introduced new metrics.

Backups

deeprepair

By martingwhite

deeprepair

SANER 2019

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