Democratized image analytics by visual programming through integration of deep models and small-scale machine learning

Analysis of biomedical images requires computational expertize that are uncommon among biomedical scientists. Deep learning approaches for image analysis provide an opportunity to develop user-friendly tools for exploratory data analysis. Here, we use the visual programming toolbox Orange (http://orange.biolab.si) to simplify image analysis by integrating deep-learning embedding, machine learning procedures, and data visualization. Orange supports the construction of data analysis workflows by assembling components for data preprocessing, visualization, and modeling. We equipped Orange with components that use pre-trained deep convolutional networks to profile images with vectors of features. These vectors are used in image clustering and classification in a framework that enables mining of image sets for both novel and experienced users. We demonstrate the utility of the tool in image analysis of progenitor cells in mouse bone healing, identification of developmental competence in mouse oocytes, subcellular protein localization in yeast, and developmental morphology of social amoebae

Automated recognition of low-level process: A pilot validation study of Zorro for test-driven development

Abstract. Zorro is a system designed to automatically determine whether a developer is complying with the Test-Driven Development (TDD) process. Automated recognition of TDD could benefit the software engineering community in a variety of ways, from pedagogical aids to support the learning of test-driven design, to support for more rigorous empirical studies on the effectiveness of TDD in practice. This paper presents the Zorro system and the results of a pilot validation study, which shows that Zorro was able to recognize test-driven design episodes correctly 89 % of the time. The results also indicate ways to improve Zorro’s classification accuracy further, and provide evidence for the effectiveness of this approach to low-level software process recognition.