In redesign and design for customization, products are changed. During this process a change to one part of the product will, in most cases, result in changes to other parts. The prediction of such change provides a significant challenge in the management of redesign and customization of complex products where many change propagation paths may be possible. This paper reports on an analysis of change behavior based on a case study in Westland Helicopters of rotorcraft design; the development of mathematical models to predict the risk of change propagation in terms of likelihood and impact of change; and the development of a prototype computer support tool to calculate such information for a specific product. With knowledge of likely change propagation paths and their impact on the delivery of the product, design effort can be directed towards avoiding change to “expensive” sub-systems and, where possible, allowing change where it is easier to implement while still achieving the overall changes required.

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Christopher (2020). A review of fuzzy AHP methods for decision-making with subjective judgements. Expert Systems With Applications, 161, article no. 113738. For guidance on citations see FAQs.

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Machine-learning methods have already been exploited as useful tools for detecting malicious executable files. They leverage data retrieved from malware samples, such as header fields, instruction sequences, or even raw bytes, to learn models that discriminate between benign and malicious software. However, it has also been shown that machine learning and deep neural networks can be fooled by evasion attacks (also referred to as adversarial examples), i.e., small changes to the input data that cause misclassification at test time. In this work, we investigate the vulnerability of malware detection methods that use deep networks to learn from raw bytes. We propose a gradient-based attack that is capable of evading a recentlyproposed deep network suited to this purpose by only changing few specific bytes at the end of each malware sample, while preserving its intrusive functionality. Promising results show that our adversarial malware binaries evade the targeted network with high probability, even though less than 1% of their bytes are modified.

Understanding how and why changes propagate during engineering design is critical because most products and systems emerge from predecessors and not through clean sheet design. This paper examines a large data set from industry including 41,500 change requests that were generated during the design of a complex sensor system spanning a period of 8 years. In particular, the networks of connected parent, child, and sibling changes are resolved over time and mapped to 46 subsystem areas of the sensor system. These change networks are then decomposed into one-, two-, and three-node motifs as the fundamental building blocks of change activity. A statistical analysis suggests that only about half (48.2%) of all proposed changes were actually implemented and that some motifs occur much more frequently than others. Furthermore, a set of indices is developed to help classify areas of the system as acceptors or reflectors of change and a normalized change propagation index shows the relative strength of each area on the absorber-multiplier spectrum between −1 and +1. Multipliers are good candidates for more focused change management. Another interesting finding is the quantitative confirmation of the “ripple” change pattern previously proposed. Unlike the earlier prediction, however, it was found that the peak of cyclical change activity occurred late in the program driven by rework discovered during systems integration and functional testing.

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