Classifying warning messages generated by software developer tools

A method, comprising:

• receiving, by a hardware processor, a first data set, the first data set including a first plurality of data entries, wherein each data entry is associated with a warning message generated based on a first set of software codes, each data entry includes indications for a plurality of features, and each data entry is associated with one of a plurality of class labels;
• generating, by the hardware processor, a second data set by sampling the first data set;
• based on the second data set, selecting, by the hardware processor, at least one feature from the plurality of features, wherein selecting the at least one feature comprises selecting the at least one feature that is clustered above a cut-off value;
• generating, by the hardware processor, a third data set by filtering the second data set with the selected at least one feature;
• determining, by the hardware processor, a machine learning classifier based on the third data set, wherein determining the machine learning classifier comprises dividing the third data set into a training data set and a testing data set;
• and
• classifying, by the hardware processor, a second warning message generated based on a second set of software codes to one of the plurality of class labels using the machine learning classifier, wherein the second set of software codes is different than the first set of software codes.

A device, comprising:

• a memory;
• and
• at least one hardware processor communicatively coupled with the memory and configured to:
• receive a first data set, the first data set including a first plurality of data entries, wherein each data entry is associated with a warning message generated based on a first set of software codes, each data entry includes indications for a plurality of features, and each data entry is associated with one of a plurality of class labels;
• generate a second data set by sampling the first data set;
• based on the second data set, select at least one feature from the plurality of features, wherein selecting the at least one feature comprises selecting the at least one feature that is clustered above a cut-off value;
• generate a third data set by filtering the second data set with the selected at least one feature;
• determine a machine learning classifier based on the third data set, wherein determining the machine learning classifier comprises dividing the third data set into a training data set and a testing data set;
• and
• classify a second warning message generated based on a second set of software codes to one of the plurality of class labels using the machine learning classifier, wherein the second set of software codes is different than the first set of software codes.

A non-transitory computer-readable medium containing instructions which, when executed, cause a computing device to perform operations comprising:

• receiving a first data set, the first data set including a first plurality of data entries, wherein each data entry is associated with a warning message generated based on a first set of software codes, each data entry includes indications for a plurality of features, and each data entry is associated with one of a plurality of class labels;
• generating a second data set by sampling the first data set;
• based on the second data set, selecting at least one feature from the plurality of features, wherein selecting the at least one feature comprises selecting the at least one feature that is clustered above a cut-off value;
• generating a third data set by filtering the second data set with the selected at least one feature;
• determining a machine learning classifier based on the third data set, wherein determining the machine learning classifier comprises dividing the third data set into a training data set and a testing data set;
• and
• classifying a second warning message generated based on a second set of software codes to one of the plurality of class labels using the machine learning classifier, wherein the second set of software codes is different than the first set of software codes.