The distribution of complexity of units (measured with McCabe index).
Intro
Conditional complexity (also called cyclomatic complexity) is a term used to measure the complexity of software. The term refers to the number of possible paths through a program function. A higher value ofter means higher maintenance and testing costs (infosecinstitute.com).
Conditional complexity is calculated by counting all conditions in the program that can affect the execution path (e.g. if statement, loops, switches, and/or operators, try and catch blocks...).
Conditional complexity is measured at the unit level (methods, functions...).
Units are classified in four categories based on the measured McCabe index: 1-5 (simple units), 6-10 (medium complex units), 11-25 (complex units), 26+ (very complex units).
private def encodeFeature() in spark/spark-tensorflow-connector/src/main/scala/org/tensorflow/spark/datasources/tfrecords/serde/DefaultTfRecordRowEncoder.scala
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private def decodeFeature() in spark/spark-tensorflow-connector/src/main/scala/org/tensorflow/spark/datasources/tfrecords/serde/DefaultTfRecordRowDecoder.scala
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private def getNumericPrecedence() in spark/spark-tensorflow-connector/src/main/scala/org/tensorflow/spark/datasources/tfrecords/TensorFlowInferSchema.scala
def encodeFeatureList() in spark/spark-tensorflow-connector/src/main/scala/org/tensorflow/spark/datasources/tfrecords/serde/DefaultTfRecordRowEncoder.scala
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private def decodeFeatureList() in spark/spark-tensorflow-connector/src/main/scala/org/tensorflow/spark/datasources/tfrecords/serde/DefaultTfRecordRowDecoder.scala
def apply[T : TypeTag]() in spark/spark-tensorflow-connector/src/main/scala/org/tensorflow/spark/datasources/tfrecords/TensorFlowInferSchema.scala
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private def findTightestCommonType() in spark/spark-tensorflow-connector/src/main/scala/org/tensorflow/spark/datasources/tfrecords/TensorFlowInferSchema.scala
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def run() in spark/spark-tensorflow-distributor/spark_tensorflow_distributor/mirrored_strategy_runner.py
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override def shortName() in spark/spark-tensorflow-connector/src/main/scala/org/tensorflow/spark/datasources/tfrecords/DefaultSource.scala
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private def saveDistributed() in spark/spark-tensorflow-connector/src/main/scala/org/tensorflow/spark/datasources/tfrecords/DefaultSource.scala
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def encodeSequenceExample() in spark/spark-tensorflow-connector/src/main/scala/org/tensorflow/spark/datasources/tfrecords/serde/DefaultTfRecordRowEncoder.scala
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def decodeSequenceExample() in spark/spark-tensorflow-connector/src/main/scala/org/tensorflow/spark/datasources/tfrecords/serde/DefaultTfRecordRowDecoder.scala
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def _get_gpus_owned() in spark/spark-tensorflow-distributor/spark_tensorflow_distributor/mirrored_strategy_runner.py
def _get_spark_task_program() in spark/spark-tensorflow-distributor/spark_tensorflow_distributor/mirrored_strategy_runner.py
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private def writePartitionLocal() in spark/spark-tensorflow-connector/src/main/scala/org/tensorflow/spark/datasources/tfrecords/DefaultSource.scala
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def decodeExample() in spark/spark-tensorflow-connector/src/main/scala/org/tensorflow/spark/datasources/tfrecords/serde/DefaultTfRecordRowDecoder.scala
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def inferFeatureListTypes() in spark/spark-tensorflow-connector/src/main/scala/org/tensorflow/spark/datasources/tfrecords/TensorFlowInferSchema.scala