Ateret Anaby-Tavor

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.

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.

Self-organizing maps for multi-objective pareto frontiers

Decision makers often need to take into account multiple conflicting objectives when selecting a solution for their problem. This can result in a potentially large number of candidate solutions to be considered. Visualizing a Pareto Frontier, the optimal set of solutions to a multi-objective problem, is considered a difficult task when the problem at hand spans more than three objective functions. We introduce a novel visual-interactive approach to facilitate coping with multi-objective problems. We propose a characterization of the Pareto Frontier data and the tasks decision makers face as they reach their decisions. Following a comprehensive analysis of the design alternatives, we show how a semantically-enhanced Self-Organizing Map, can be utilized to meet the identified tasks. We argue that our newly proposed design provides both consistent orientation of the 2D mapping as well as an appropriate visual representation of individual solutions. We then demonstrate its applicability with two real-world multi-objective case studies. We conclude with a preliminary empirical evaluation and a qualitative usefulness assessment.

Towards a Model Driven Service Engineering Process

Developments in the realm of business services have opened new challenges in the business consulting world. Consequently, new procedures for services system development are being proposed. In this work, we present a model driven approach for streamlining the service engineering process. An important aspect of our work is a method for model transformation, which is essential for providing and enhancing the management and governance capabilities of the service engineering process. We then introduce a novel infrastructure which leverages Web 2.0 technologies to address the challenge of developing semantic modeling editors. Finally, we use this infrastructure as the target platform to validate our work.

The WaaSaBE Model: Marrying WaaS and Business-Entities to Support Cross-Organization Collaboration

With a growing services-based focus in enterprises, functionally-tiered organizational structures have emerged. Synchronizing among the tiers is difficult due to differing concerns and vocabularies, especially when cross-enterprise collaboration is involved. Furthermore, deficiencies in work handoff among different roles and parties also occur within tiers. Building on the notions of work-as-a-service for work execution and business entities in operations, this paper proposes the WaaSaBE model as a boundary object for integrated management across and within tiers and across enterprises. We describe the framework, a formal model arising from the framework, and its basic instantiation for a given problem domain.

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.

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.

Pareto Landscapes Analyses via Graph-Based Modeling for Interactive Decision-Making

We consider two complementary tasks for consuming optimization results of a given multiobjective problem by decision-makers. The underpinning in both exploratory tasks is analyzing Pareto landscapes, and we propose in both cases discrete graph-based reductions. Firstly, we introduce interactive navigation from a given suboptimal reference solution to Pareto efficient solution-points. The proposed traversal mechanism is based upon landscape improvement-transitions from the reference towards Pareto-dominating solutions in a baby-steps fashion – accepting relatively small variations in the design-space. The Efficient Frontier and the archive of Pareto suboptimal points are to be obtained by population-based multiobjective solvers, such as Evolutionary Multiobjective Algorithms. Secondly, we propose a framework for automatically recommending a preferable subset of points belonging to the Frontier that accounts for the decision-maker’s tendencies. We devise a line of action that activates one of two approaches: either recommending the top offensive team – the gain-prone subset of points, or the top defensive team – the loss-averse subset of points. We describe the entire recommendation process and formulate mixed-integer linear programs for solving its combinatorial graph-based problems.

Pareto optimization and tradeoff analysis applied to meta-learning of multiple simulation criteria

Simulation performance may be evaluated according to multiple quality measures that are in competition and their simultaneous consideration poses a conflict. In the current study we propose a practical framework for investigating such simulation performance criteria, exploring the inherent conflicts amongst them and identifying the best available tradeoffs, based upon multiobjective Pareto optimization. This approach necessitates the rigorous derivation of performance criteria to serve as objective functions and undergo vector optimization. We demonstrate the effectiveness of our proposed approach by applying it to a specific Artificial Neural Networks (ANN) simulation, with multiple stochastic quality measures. We formulate performance criteria of this use-case, pose an optimization problem, and solve it by means of a simulation-based Pareto approach. Upon attainment of the underlying Pareto Frontier, we analyze it and prescribe preference-dependent configurations for the optimal simulation training.

Evaluating Multivariate Visualizations as Multi-objective Decision Aids

When choosing a solution, decision makers are often required to account for multiple conflicting objectives. This is a situation that can result in a potentially huge number of candidate solutions. Despite the wide selection of multivariate visualizations that can potentially help decide between various candidates, no designated means exist to assess the effectiveness of these visualizations under different circumstances. As a first contribution in this work, we developed a method to evaluate different types of multivariate visualization. The method focuses on the visualization’s ability to facilitate a better understanding of inter-objective trade-offs as a proxy to more sensible decision making. We used the method to evaluate two existing visualization aids: Parallel-Coordinates and an adaptation of Self Organizing Maps (SOM). Both visualizations were compared with tabular data presentation. Our results show that the first visualization is more effective than a plain tabular visualization for the purpose of multi-objective decision making.