The various influences in the processes and application domains make requirements engineering (RE) inherently complex and difficult to implement. In general, we have two options for establishing an RE approach: We can either establish an activity-based RE approach, or we can establish an artefact-based one where project participants concentrate on the RE artefacts rather than on the way of creating them. While a number of activity-based RE approaches have been proposed in recent years, we have gained much empirical evidence and experiences about the advantages of the artefact-based paradigm for RE. However, artefact orientation is still a young paradigm with various interpretations and practical manifestations whereby we need a clear understanding of its basic concepts and a consolidated and evaluated view on the paradigm. In this article, we contribute an artefact-based approach to RE [artefact model for domain-independent RE (AMDiRE)] that emerges from 6 years of experiences in fundamental and evidence-based research. To this end, we first discuss the basic notion of artefact orientation and its evolution in recent years. We briefly introduce a set of artefact-based RE models we developed in industrial research cooperations for different application domains and show their empirical evaluations and their dissemination into academia and practice, eventually leading to the AMDiRE approach. We conclude with a discussion of experiences we made during the development and different industrial evaluations, and lessons learnt.
First content-related dependencies resulting from refinement and decomposition in the modelling concepts are provided by [chp. 2] BPKR09 . These cover the basic concepts previously developed in a research co-operation between Siemens Corporate Research and Technische Universit "at M "unchen (TUM) @cite_12 (see also Sect. ). They provide an RE artefact model and name the key components for measurable RE artefacts, include a first process guideline, and suggest practices for their elaboration.
@cite_12
1973931357 2121843002 2153335008 2563705555
[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. Requirements Engineering (RE) processes are highly volatile due to dependencies on customers' capabilities or used process models, both complicating a standardised RE process. A promising solution is given by artefactorientation that emphasises the results rather than dictating a strict development process. At such a basis one is able to incorporate domain-specific methods for producing artefacts without having to take into account the variability of process definitions. Although artefacts are known to support customisable development processes, there still is no common agreement about the structure and semantics of artefact-based methodologies. In this paper we discuss different interpretations of the term artefact considering aspects like process integration capabilities and necessities within individual project environments. We contribute a meta model for artefact-orientation that is inferred from two RE models elaborated within industrial cooperation projects of our research group. We conclude with a discussion of performed case studies and ongoing work. With broader availability of large-scale 3D model repositories, the need for efficient and effective exploration becomes more and more urgent. Existing model retrieval techniques do not scale well with the size of the database since often a large number of very similar objects are returned for a query, and the possibilities to refine the search are quite limited. We propose an interactive approach where the user feeds an active learning procedure by labeling either entire models or parts of them as “like” or “dislike” such that the system can automatically update an active set of recommended models. To provide an intuitive user interface, candidate models are presented based on their estimated relevance for the current query. From the methodological point of view, our main contribution is to exploit not only the similarity between a query and the database models but also the similarities among the database models themselves. We achieve this by an offline pre-processing stage, where global and local shape descriptors are computed for each model and a sparse distance metric is derived that can be evaluated efficiently even for very large databases. We demonstrate the effectiveness of our method by interactively exploring a repository containing over 100 K models. Recently, very deep convolutional neural networks (CNNs) have shown outstanding performance in object recognition and have also been the first choice for dense classification problems such as semantic segmentation. However, repeated subsampling operations like pooling or convolution striding in deep CNNs lead to a significant decrease in the initial image resolution. Here, we present RefineNet, a generic multi-path refinement network that explicitly exploits all the information available along the down-sampling process to enable high-resolution prediction using long-range residual connections. In this way, the deeper layers that capture high-level semantic features can be directly refined using fine-grained features from earlier convolutions. The individual components of RefineNet employ residual connections following the identity mapping mindset, which allows for effective end-to-end training. Further, we introduce chained residual pooling, which captures rich background context in an efficient manner. We carry out comprehensive experiments and set new state-of-the-art results on seven public datasets. In particular, we achieve an intersection-over-union score of 83.4 on the challenging PASCAL VOC 2012 dataset, which is the best reported result to date.