public ClassificationResult Apply(
ICollection<string> codePaths, string searchPattern, FeatureEncoder featureEncoder,
Classifier classifier, StreamWriter writer = null) {
var allCsts = GenerateValidCsts(codePaths);
var encodingResult = featureEncoder.Encode(codePaths, allCsts, this);
var time = Environment.TickCount;
var groupCache = new GroupCache(encodingResult, classifier);
var result = Classify(int.MaxValue, classifier, groupCache, encodingResult);
Console.WriteLine("Time: " + (Environment.TickCount - time));
if (writer != null) {
encodingResult.WriteResult(writer);
}
return result;
}
public ClassificationResult Apply(
ICollection <string> codePaths, string searchPattern, FeatureEncoder featureEncoder,
Classifier classifier, StreamWriter writer = null)
{
var allCsts = GenerateValidCsts(codePaths);
var encodingResult = featureEncoder.Encode(codePaths, allCsts, this);
var time = Environment.TickCount;
var groupCache = new GroupCache(encodingResult, classifier);
var result = Classify(int.MaxValue, classifier, groupCache, encodingResult);
Console.WriteLine("Time: " + (Environment.TickCount - time));
if (writer != null)
{
encodingResult.WriteResult(writer);
}
return(result);
}
public LearningResult Learn(
ICollection <string> seedPaths, ICollection <string> codePaths, string searchPattern,
StreamWriter writer = null)
{
var allCsts = GenerateValidCsts(codePaths);
var seedCsts = GenerateValidCsts(seedPaths).ToList();
var seedNodes = seedCsts
.SelectMany(
cst => LearningExperimentUtil.GetUppermostNodesByNames(cst, OracleNames))
.Where(ProtectedIsAcceptedUsingOracle)
.ToList();
var seedCst = seedCsts.First();
var seedCode = seedCst.Code;
var structuredCode = new StructuredCode(seedCode);
var acceptingFragments = ConstructAcceptingFragments(structuredCode, seedCst, seedNodes);
var rejectingFragments = ConstructRejectingFragments(structuredCode, seedCst);
SeedNodeSet.Create(acceptingFragments, this);
var preparingTime = Environment.TickCount;
var extractor = CreateExtractor();
var seedNodeSet = new SeedNodeSet(acceptingFragments.Select(f => f.Node), seedCsts, this);
Console.WriteLine("#Accepted seed nodes: " + seedNodeSet.AcceptedNodes.Count
+ " (" + acceptingFragments.Count + ")");
Console.WriteLine("#Rejected seed nodes: " + seedNodeSet.RejectedNodes.Count
+ " (" + rejectingFragments.Count + ")");
var featureSet = new FeatuerSet(seedNodeSet, extractor, acceptingFragments, rejectingFragments);
var groupPaths = seedNodeSet.SelectedNodeNames.Select(n => ">" + n + ">");
var classifier = new Classifier(groupPaths, featureSet);
Console.WriteLine(
"#Features: " + featureSet.AcceptingFeatureCount + ", "
+ featureSet.RejectingFeatureCount);
Console.WriteLine("Inner: " + extractor.IsInner);
var featureEncoder = new FeatureEncoder(seedNodeSet.SelectedNodeNames, extractor,
featureSet);
var encodingResult = featureEncoder.Encode(codePaths, allCsts, this, seedNodeSet);
Console.WriteLine("#Unique Elements: " + encodingResult.VectorCount);
if (encodingResult.IdealAcceptedVector2GroupPath.Keys.ToHashSet()
.Overlaps(encodingResult.IdealRejectedVector2GroupPath.Keys.ToHashSet()))
{
var others = encodingResult.IdealRejectedVector2GroupPath;
var vector =
encodingResult.IdealAcceptedVector2GroupPath.Keys.First(others.ContainsKey);
foreach (var featureString in featureEncoder.GetFeatureStringsByVector(vector))
{
Console.WriteLine(Experiment.Beautify(featureString));
}
throw new Exception("Master predicates can't classify elements!");
}
var groupCache = new GroupCache(encodingResult, classifier);
var trainingSet = encodingResult.CreateTrainingVectorSet();
classifier.Create(trainingSet, groupCache);
Experiment.WriteFeatureStrings(Console.Out, classifier, featureEncoder);
Console.WriteLine("Preparing time: " + (Environment.TickCount - preparingTime));
var count = 0;
var sumTime = Environment.TickCount;
ClassificationResult classificationResult;
while (true)
{
var time = Environment.TickCount;
classificationResult = Classify(count, classifier, groupCache, encodingResult,
trainingSet);
if (classificationResult.SuspiciousNodes == null)
{
break;
}
var additionalAcceptedSet = RevealSuspiciousElements(
encodingResult.IdealAcceptedVector2GroupPath.Keys,
classificationResult.SuspiciousNodes, encodingResult, trainingSet);
if (!classifier.Update(additionalAcceptedSet, trainingSet, groupCache))
{
count++;
}
else
{
count = 0;
}
Console.WriteLine("Time: " + (Environment.TickCount - time));
}
classifier.MakeImmutable();
Console.WriteLine();
Console.WriteLine("Sum time: " + (Environment.TickCount - sumTime));
var trainingVectorCount = trainingSet.Count;
var idealVectorCount = encodingResult.IdealVectorSet.Count;
Console.WriteLine("#Required vectors: " + trainingVectorCount + " / " + idealVectorCount);
if (writer != null)
{
encodingResult.WriteResult(writer, trainingSet);
}
foreach (var groupPath in classifier.GroupPaths)
{
Console.WriteLine(groupPath);
}
classifier.Optimize(encodingResult.IdealRejectedVector2GroupPath.Keys, groupCache);
return(new LearningResult {
ClassificationResult = classificationResult,
Classifier = classifier,
EncodingResult = encodingResult,
FeatureEncoder = featureEncoder,
});
}
public LearningResult Learn(
ICollection<string> seedPaths, ICollection<string> codePaths, string searchPattern,
StreamWriter writer = null) {
var allCsts = GenerateValidCsts(codePaths);
var seedCsts = GenerateValidCsts(seedPaths).ToList();
var seedNodes = seedCsts
.SelectMany(
cst => LearningExperimentUtil.GetUppermostNodesByNames(cst, OracleNames))
.Where(ProtectedIsAcceptedUsingOracle)
.ToList();
var seedCst = seedCsts.First();
var seedCode = seedCst.Code;
var structuredCode = new StructuredCode(seedCode);
var acceptingFragments = ConstructAcceptingFragments(structuredCode, seedCst, seedNodes);
var rejectingFragments = ConstructRejectingFragments(structuredCode, seedCst);
SeedNodeSet.Create(acceptingFragments, this);
var preparingTime = Environment.TickCount;
var extractor = CreateExtractor();
var seedNodeSet = new SeedNodeSet(acceptingFragments.Select(f => f.Node), seedCsts, this);
Console.WriteLine("#Accepted seed nodes: " + seedNodeSet.AcceptedNodes.Count
+ " (" + acceptingFragments.Count + ")");
Console.WriteLine("#Rejected seed nodes: " + seedNodeSet.RejectedNodes.Count
+ " (" + rejectingFragments.Count + ")");
var featureSet = new FeatuerSet(seedNodeSet, extractor, acceptingFragments, rejectingFragments);
var groupPaths = seedNodeSet.SelectedNodeNames.Select(n => ">" + n + ">");
var classifier = new Classifier(groupPaths, featureSet);
Console.WriteLine(
"#Features: " + featureSet.AcceptingFeatureCount + ", "
+ featureSet.RejectingFeatureCount);
Console.WriteLine("Inner: " + extractor.IsInner);
var featureEncoder = new FeatureEncoder(seedNodeSet.SelectedNodeNames, extractor,
featureSet);
var encodingResult = featureEncoder.Encode(codePaths, allCsts, this, seedNodeSet);
Console.WriteLine("#Unique Elements: " + encodingResult.VectorCount);
if (encodingResult.IdealAcceptedVector2GroupPath.Keys.ToHashSet()
.Overlaps(encodingResult.IdealRejectedVector2GroupPath.Keys.ToHashSet())) {
var others = encodingResult.IdealRejectedVector2GroupPath;
var vector =
encodingResult.IdealAcceptedVector2GroupPath.Keys.First(others.ContainsKey);
foreach (var featureString in featureEncoder.GetFeatureStringsByVector(vector)) {
Console.WriteLine(Experiment.Beautify(featureString));
}
throw new Exception("Master predicates can't classify elements!");
}
var groupCache = new GroupCache(encodingResult, classifier);
var trainingSet = encodingResult.CreateTrainingVectorSet();
classifier.Create(trainingSet, groupCache);
Experiment.WriteFeatureStrings(Console.Out, classifier, featureEncoder);
Console.WriteLine("Preparing time: " + (Environment.TickCount - preparingTime));
var count = 0;
var sumTime = Environment.TickCount;
ClassificationResult classificationResult;
while (true) {
var time = Environment.TickCount;
classificationResult = Classify(count, classifier, groupCache, encodingResult,
trainingSet);
if (classificationResult.SuspiciousNodes == null) {
break;
}
var additionalAcceptedSet = RevealSuspiciousElements(
encodingResult.IdealAcceptedVector2GroupPath.Keys,
classificationResult.SuspiciousNodes, encodingResult, trainingSet);
if (!classifier.Update(additionalAcceptedSet, trainingSet, groupCache)) {
count++;
} else {
count = 0;
}
Console.WriteLine("Time: " + (Environment.TickCount - time));
}
classifier.MakeImmutable();
Console.WriteLine();
Console.WriteLine("Sum time: " + (Environment.TickCount - sumTime));
var trainingVectorCount = trainingSet.Count;
var idealVectorCount = encodingResult.IdealVectorSet.Count;
Console.WriteLine("#Required vectors: " + trainingVectorCount + " / " + idealVectorCount);
if (writer != null) {
encodingResult.WriteResult(writer, trainingSet);
}
foreach (var groupPath in classifier.GroupPaths) {
Console.WriteLine(groupPath);
}
classifier.Optimize(encodingResult.IdealRejectedVector2GroupPath.Keys, groupCache);
return new LearningResult {
ClassificationResult = classificationResult,
Classifier = classifier,
EncodingResult = encodingResult,
FeatureEncoder = featureEncoder,
};
}