Artefacts play a vital role in today's software and systems development processes. The notion of artefacts is widely used in software development communities. Despite the relevance and frequent use of the term artefact', the exact denotation of this term is still not clear, due to a variety of different understandings of the term and to a careless negligent usage. This often leads to conceptual and formal results being grounded in a fuzzy, unclear understanding of the essential concepts involved. In fact, there does not exist a common terminology. Other terms like documents, deliverables, or work products are used instead of the term artefact. Therefore, it is our goal that the term artefact be standardised so that researchers and practitioners have a common understanding for discussions and contributions. In this position paper, we provide a critical reflection upon the notion of artefact in software engineering at different levels of perception and how these relate to each other. We further contribute a meta model that provides a description of an artefact that is independent from any underlying process. The meta model defines an artefact at three levels. Abstraction and refinement relations between these levels allow correlating artefacts to each other and defining the notion of equivalent artefacts. Our contribution shall foster the long overdue and too often underestimated terminological discussion on what artefacts are to provide a common ground with clearer concepts and principles for future software engineering contributions, such as the design of artefact-oriented development processes and tools.
Overall, we can observe that different research areas came up with their own definition of what constitutes an artefact in order to examine or transform it at certain levels of perception. However, not all definitions are explicit and relationship between the levels of perception often remain vague. For instance, model versioning and comparison @cite_12 consider a model as an artefact; unclear is if they refer to a physical file, a graph according to a certain meta model or to content representing an abstraction of some part of reality. Moreover, those levels of perception are referred to by different authors in the same research area by different names, e.g., static instead of syntactic @cite_2 . The lack of a common vocabulary and precise understanding limits the comparability of different techniques and approaches, as identified independently in @cite_9 Closing this gap of a common vocabulary was in scope of our paper at hands.
@cite_9 @cite_12 @cite_2
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The perceptual recognition of objects is conceptualized to be a process in which the image of the input is segmented at regions of deep concavity into an arrangement of simple geometric components, such as blocks, cylinders, wedges, and cones. The fundamental assumption of the proposed theory, recognition-by-components (RBC), is that a modest set of generalized-cone components, called geons (N £ 36), can be derived from contrasts of five readily detectable properties of edges in a two-dimensiona l image: curvature, collinearity, symmetry, parallelism, and cotermination. The detection of these properties is generally invariant over viewing position an$ image quality and consequently allows robust object perception when the image is projected from a novel viewpoint or is degraded. RBC thus provides a principled account of the heretofore undecided relation between the classic principles of perceptual organization and pattern recognition: The constraints toward regularization (Pragnanz) characterize not the complete object but the object's components. Representational power derives from an allowance of free combinations of the geons. A Principle of Componential Recovery can account for the major phenomena of object recognition: If an arrangement of two or three geons can be recovered from the input, objects can be quickly recognized even when they are occluded, novel, rotated in depth, or extensively degraded. The results from experiments on the perception of briefly presented pictures by human observers provide empirical support for the theory. Any single object can project an infinity of image configurations to the retina. The orientation of the object to the viewer can vary continuously, each giving rise to a different two-dimensional projection. The object can be occluded by other objects or texture fields, as when viewed behind foliage. The object need not be presented as a full-colored textured image but instead can be a simplified line drawing. Moreover, the object can even be missing some of its parts or be a novel exemplar of its particular category. But it is only with rare exceptions that an image fails to be rapidly and readily classified, either as an instance of a familiar object category or as an instance that cannot be so classified (itself a form of classification). [Background:] Nowadays, industries are facing the problem that the Requirements Engineering (RE) process is highly volatile, since it depends on project influences from the customer's domain or from process models used. Artefact-based approaches promise to provide guidance in the creation of consistent artefacts in volatile project environments, because these approaches concentrate on the artefacts and their dependencies, instead of prescribing processes. Yet missing, however, is empirical evidence on the advantages of applying artefact-based RE approaches in real projects. [Aim:] We developed a customisable artefact-based RE approach for the domain of business information systems. Our goal is to investigate the advantages and limitations of applying this customisable approach in an industrial context. [Method:] We conduct a case study with our artefact-based RE approach and its customisation procedure. For this, we apply it at a software development project at Siemens following the steps of the customisation procedure. We assess our approach in direct comparison with the previously used RE approach considering possible improvements in the process and in the quality of the produced artefacts. [Results:] We show that our approach is flexible enough to respond to the individual needs in the analysed project environment. Although the approach is not rated to be more productive, we find an improvement in the syntactic and the semantic quality of the created artefacts. [Conclusions:] We close a gap in the RE literature by giving empirical evidence on the advantages of artefact orientation in RE in an industrial setting. Imagining a scene described in natural language with realistic layout and appearance of entities is the ultimate test of spatial, visual, and semantic world knowledge. Towards this goal, we present the Composition, Retrieval and Fusion Network (Craft), a model capable of learning this knowledge from video-caption data and applying it while generating videos from novel captions. Craft explicitly predicts a temporal-layout of mentioned entities (characters and objects), retrieves spatio-temporal entity segments from a video database and fuses them to generate scene videos. Our contributions include sequential training of components of Craft while jointly modeling layout and appearances, and losses that encourage learning compositional representations for retrieval. We evaluate Craft on semantic fidelity to caption, composition consistency, and visual quality. Craft outperforms direct pixel generation approaches and generalizes well to unseen captions and to unseen video databases with no text annotations. We demonstrate Craft on Flintstones (Flintstones is available at https: prior.allenai.org projects craft), a new richly annotated video-caption dataset with over 25000 videos. For a glimpse of videos generated by Craft, see https: youtu.be 688Vv86n0z8. We present a dense reconstruction approach that overcomes the drawbacks of traditional multiview stereo by incorporating semantic information in the form of learned category-level shape priors and object detection. Given training data comprised of 3D scans and images of objects from various viewpoints, we learn a prior comprised of a mean shape and a set of weighted anchor points. The former captures the commonality of shapes across the category, while the latter encodes similarities between instances in the form of appearance and spatial consistency. We propose robust algorithms to match anchor points across instances that enable learning a mean shape for the category, even with large shape variations across instances. We model the shape of an object instance as a warped version of the category mean, along with instance-specific details. Given multiple images of an unseen instance, we collate information from 2D object detectors to align the structure from motion point cloud with the mean shape, which is subsequently warped and refined to approach the actual shape. Extensive experiments demonstrate that our model is general enough to learn semantic priors for different object categories, yet powerful enough to reconstruct individual shapes with large variations. Qualitative and quantitative evaluations show that our framework can produce more accurate reconstructions than alternative state-of-the-art multiview stereo systems.