Black-box software testing with statistical learning

A method to relate inputs and outputs without assuming a specific model, the method comprising:

• receiving a data set that includes known inputs and corresponding known outputs associated with a software component under test and with unknown functionality;
• identifying a constraint;
• identifying neighbor data items based on proximity to a target, wherein each neighbor data item includes a neighbor input and a corresponding neighbor output;
• determining a new input based on averaging neighbor inputs for the neighbor data items;
• determining a corresponding new output based on the new input;
• determining whether the corresponding new output satisfies the constraint;
• and
• generating, using a processor device programmed to perform or control performance of the generating, a non-parametric model to automatically determine a functionality of the software component under test.

A method to analyze code whose functionality is unknown, the method comprising:

• receiving a data set that includes known inputs and corresponding known outputs associated with a software component under test and with unknown functionality;
• generating, using a processor device programmed to perform or control performance of the generating, a parametric model based on the data set;
• determining whether the parametric model applies to the data set;
• responsive to the parametric model failing to apply to the data set, identifying a constraint;
• determining a new input and a corresponding new output that satisfy the constraint based on one or more data items of the data set;
• and
• generating a non-parametric model to automatically determine a functionality of the software component under test.