Sophia Krasikov

Flexible modeling tools for pre-requirements analysis: conceptual architecture and research challenges

A serious tool gap exists at the start of the software lifecy-cle, before requirements formulation. Pre-requirements analysts gather information, organize it to gain insight, en-vision possible futures, and present insights and recom-mendations to stakeholders. They typically use office tools, which give great freedom, but no help with consistency management, change propagation, or information migration to downstream tools. Despite these downsides, office tools are still favored over modeling tools, which are constrain-ing and difficult to use. We introduce the notion of flexible modeling tools, which blend the advantages of office and modeling tools. We propose a conceptual architecture for such tools, and outline research challenges to be met in realizing them. We briefly describe the Business Insight Toolkit, a prototype tool embodying this architecture.

Execution patterns for visualizing web services

Web Services are well on their way to becoming the Lingua Franca for distributed computing. Although tools for building and monitoring web services applications are more powerful and easier to use than ever, they do not yet fully address the horizontal complexity of mature applications built as large nets of interconnected web services. We present a pattern-based visualization that enables business owners, application designers, programmers, and operations staff to quickly understand the behavior of complex web services applications. We describe a novel pattern extraction algorithm that captures important trends from web services execution traces. We demonstrate a new way to visualize these patterns that shows the behavior of web services applications at different levels of abstraction. Finally, we explain how this can help developers with performance analysis by showing both the averages and variations in the data contained in each pattern.

Using tagging to identify and organize concerns during pre-requirements analysis

Before requirements analysis takes place in a business context, business analysis is usually performed. Important concerns emerge during this analysis that need to be captured and communicated to requirements engineers. In this paper, we take the position that tagging is a promising approach for identifying and organizing these concerns. The fact that tags can be attached freely to entities, often with multiple tags attached to the same entity and the same tag attached to multiple entities, leads to multi-dimensional structures that are suitable for representing crosscutting concerns and exploring their relationships. The resulting tag structures can be hardened into classifications that capture and communicate important concerns.

An algorithm for identifying the abstract syntax of graph-based diagrams

Diagrams play a key role in the information systems domain. However to be meaningful, the diagrams are understood by interpreting visual cues in specific, conventionalized ways, termed conceptual models. One of the major pain points of conceptual models, specified as visual languages, is the inability to capture these visual languages effectively in conventional modeling tools. Instead, conceptual models are drawn using drawing tools and sometimes even by hand. We propose an automatic procedure to derive the syntactic building blocks of graph-based conceptual models. This high-level specification of the visual language can then serve as input for the automatic construction of syntax-aware diagram editors. Our aim is to achieve minimum effort on the part of the users when they eventually work with the graphical editor to produce a new diagram using the proposed syntax.

A Deep Learning and Knowledge Transfer Based Architecture for Social Media User Characteristic Determination

Determining explicit user characteristics based on interactions on Social Media is a crucial task in developing recommendation and social polling solutions. For this purpose, rule based and N-gram based techniques have been proposed to develop user profiles, but they are only fit for detecting user attributes that can be classified by a relatively simple logic or rely on the presence of a large amount of training data. In this paper, we propose a general purpose, end-to-end architecture for text analytics, and demonstrate its effectiveness for analytics based on tweets with a relatively small training set. By performing unsupervised feature learning and deep learning over labeled and unlabeled tweets, we are able to learn in a more generalizable way than N-gram techniques. Our proposed hidden layer sharing approach makes it possible to efficiently transfer knowledge between related NLP tasks. This approach is extensible, and can learn even more from metadata available about Social Media users. For the task of user age prediction over a relatively small corpus, we demonstrate 38.3% error reduction over single task baselines, a total of 44.7% error reduction with the incorporation of two related tasks, and achieve 90.1% accuracy when useful metadata is present.

Business insight toolkit: Flexible pre-requirements modeling

Pre-requirements analysis requires modeling tools with unprecedented flexibility. The Business Insight Toolkit (BITKit) is a prototype of a new kind of modeling tool, aimed at offering the flexibility of office tools along with many of the advantages of modeling tools.

Blending freeform and managed information in tables (NIER track)

Tables are an important tool used by business analysts engaged in early requirements activities (in fact it is safe to say that tables appeal to many other types of user, in a variety of activities and domains). Business analysts typically use the tables provided by office tools. These tables offer great flexibility, but no underlying model, and hence no consistency management, multiple views or other advantages familiar to the users of modeling tools. Modeling tools, however, are usually too rigid for business analysts. In this paper we present a flexible modeling approach to tables, which combines the advantages of both office and modeling tools. Freeform information can co-exist with information managed by an underlying model, and an incremental formalization approach allows each item of information to transition fluidly between freeform and managed. As the model evolves, it is used to guide the user in the process of formalizing any remaining freeform information. The model therefore helps users without restricting them. Early feedback is described, and the approach is analyzed briefly in terms of cognitive dimensions.