Abstract. Fast production of a solution is a necessity in the world of competitive IT consulting business today. In engagements where early user interface design mock-ups are needed to visualize proposed business processes, the need to quickly create UI becomes prominent very early in the process. Our work aims to speed up the UI design process, enabling rapid creation of lowfidelity UI design with traditional user-centered design thinking but different tooling concepts. This paper explains the approach and the rationale behind our model and tools. One key focal point is in leveraging business process models as a starting point of the UI design process. The other focal point is on using a model-driven approach with designer-centered tools to eliminate some design overheads, to help manage a large design space, and to cope with changes in requirements. We used examples from a real business engagement to derive and strengthen this work.
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Regression testing is an important activity performed to validate modified software, and one of its key tasks is regression test selection (RTS)-selecting a subset of existing test cases to run on the modified software. Most existing RTS techniques focus on changes made to code components and completely ignore non-code elements, such as configuration files and databases, that can also change and affect the system behavior. To address this issue, we present a new RTS technique that performs accurate test selection in the presence of changes to non-code components. Our technique computes traceability between test cases and the external data accessed by an application, and uses this information to perform RTS in the presence of changes to non-code elements. We present our technique, a prototype implementation of the technique, and a set of preliminary empirical results that illustrate the feasibility, effectiveness, and potential usefulness of our approach.
Model transforms are a class of applications that convert a model to another model or text. The inputs to such transforms are often large and complex; therefore, faults in the models that cause a transformation to generate incorrect output can be difficult to identify and fix. In previous work, we presented an approach that uses dynamic tainting to help locate input-model faults. In this paper, we present techniques to assist with repairing input-model faults. Our approach collects runtime information for the failing transformation, and computes repair actions that are targeted toward fixing the immediate cause of the failure. In many cases, these repair actions result in the generation of the correct output. In other cases, the initial fix can be incomplete, with the input model requiring further repairs. To address this, we present a pattern-analysis technique that identifies correct output fragments that are similar to the incorrect fragment and, based on the taint information associated with such fragments, computes additional repair actions. We present the results of empirical studies, conducted using real model transforms, which illustrate the applicability and effectiveness of our approach for repairing different types of faults.
Open-source software projects are primarily driven by community contribution. However, commit access to such projects' software repositories is often strictly controlled. These projects prefer to solicit external participation in the form of patches or pull requests.In this paper, we analyze a set of 89 top-starred GitHub projects and their forks in order to explore the nature and distribution of such community contribution. We first classify commits (and developers) into three categories: CORE, EXTERNAL and MUTANT, and study the relative sizes of each of these classes through a ringbased visualization. We observe that projects written in mainstream scripting languages such as JavaScript and Python tend to include more external participation than projects written in upcoming languages such as Scala. We also visualize the geographic spread of these communities via geocoding. Finally, we classify the types of pull requests submitted based on their labels and observe that bug fixes are more likely to be merged into the main projects as compared to feature enhancements.
Traditional software engineering programming paradigms are mostly object or procedure oriented, driven by deterministic algorithms. With the advent of deep learning and cognitive sciences there is an emerging trend for data-driven programming, creating a shift in the programming paradigm among the software engineering communities. Visualizing and interpreting the execution of a current large scale data-driven software development is challenging. Further, for deep learning development there are many libraries in multiple programming languages such as TensorFlow (Python), CAFFE (C++), Theano (Python), Torch (Lua), and Deeplearning4j (Java), driving a huge need for interoperability across libraries.We propose a model driven development based solution framework, that facilitates intuitive designing of deep learning models in a platform agnostic fashion. This framework could potentially generate library specific code, perform program translation across languages, and debug the training process of a deep learning model from a fault localization and repair perspective.Further we identify open research problems in this emerging domain, and discuss some new software tooling requirements to serve this new age data-driven programming paradigm.
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