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The use of commercial-off-the-shelf (COTS) / government-off-the-shelf (GOTS) applications as components in software systems is increasingly prevalent. The critical step of tool selection for an integrated suite is usually based on identifying the tools that best match the functionality requirements needed. Other factors tangential to technical performance are playing a more important role in the tool selection process and making the mapping of customer needs to technical requirements less obvious. This paper suggests a shift from the traditional "best tools" selection approach, where tools are selected for their performance to a more holistic "end-to-end" approach, where customer concerns, business and cost benefits, and technical performance are weighed concurrently.The end-to-end methodology was applied to an integrated suite for the intelligence analysis process and was compared to a theoretical system employing a best tools approach. This showed that the end-to-end approach resulted in significant software related cost reductions.

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Advanced manufacturing techniques have enabled the production of materials with state-of-the-art properties. In many cases however, the development of physics-based models of these techniques lags behind their use in the lab. This means that designing and running experiments proceeds largely via trial and error. This is sub-optimal since experiments are cost-, time-, and labor-intensive. In this work we propose a machine learning framework, differential property classification (DPC), which enables an experimenter to leverage machine learning's unparalleled pattern matching capability to pursue data-driven experimental design. DPC takes two possible experiment parameter sets and outputs a prediction of which will produce a material with a more desirable property specified by the operator. We demonstrate the success of DPC on AA7075 tube manufacturing process and mechanical property data using shear assisted processing and extrusion (ShAPE), a solid phase processing technology. We show that by focusing on the experimenter's need to choose between multiple candidate experimental parameters, we can reframe the challenging regression task of predicting material properties from processing parameters, into a classification task on which machine learning models can achieve good performance.