Analog and mixed signal device testing is resource intensive due to the spectral and temporal speci cations of the input/output interface signals. These devices and circuits are commonly validated by parametric speci fication tests to ensure compliance with the required performance criteria. Analog signal complexity increases resource requirements for the Automatic Test Equipment (ATE) systems used for commercial testing, making mixed signal testing resource ine cient as compared to digital structural testing. This dissertation proposes and implements a test ecosystem to address these constraints where Built In Self Test (BIST) modules are designed for internal stimulus generation. Data learning and processing algorithms are developed for output response shaping. This modi ed output response is then compared against the established performance matrices to maintain test quality with low cost receiver hardware. BIST modules reduce dependence on ATE resources for stimulus and output observation while improving capability to test multiple devices in parallel. Data analysis algorithms are used to predict specification parameters based on learning methods applied to measurable device parameters. Active hardware resources can be used in conjunction with post processing resources to implement complex speci cation based tests within the hardware limitations. This dissertation reviews the results obtained with the consolidated approach of using BIST, output response analysis and active hardware resources to reduce test cost while maintaining test quality. / text