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Deep probabilistic programming concatenates the strengths of deep learning to the context of probabilistic modeling for efficient and flexible computation in practice. Being an evolving field, there exist only a few expressive programming languages for uncertainty management. This paper discusses an application for analysis of ultrasound nerve segmentation-based biomedical images. Our method uses the probabilistic programming language Edward with the U-Net model and generative adversarial networks under different optimizers. The segmentation process showed the least Dice loss (-0.54) and the highest accuracy (0.99) with the Adam optimizer in the U-Net model with the least time consumption compared to other optimizers. The smallest amount of generative network loss in the generative adversarial network model gained was 0.69 for the Adam optimizer. The Dice loss, accuracy, time consumption and output image quality in the results show the applicability of deep probabilistic programming in the long run. Thus, we further propose a neuroscience decision support system based on the proposed approach.

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Software development in DevOps practice is a widely used approach to cope with the demand for frequent artefact changes. These changes require a well-defined method to manage artefact consistency to ease the continuous integration process. This chapter proposes a traceability management approach for the artefact types in the main phases of the software process including requirements, design, source code, testing, and configuration. This chapter addresses traceability management, including trace link creation, change detection, impact analysis, change propagation, validation, and visualisation. This chapter presents a tool named SAT-Analyser that is applicable for any software development method and designed for continuous integration, multi-user collaboration, and DevOps tool stack compatibility. The SAT-Analyser is assessed using case studies and shown an impact analysis accuracy of 0.93 of F-measure. Further, the feedback by DevOps practitioners has shown the suitability and innovativeness of the proposed approach.