Tom Tourwé

Demystifying Release Definition: From Requirements Prioritization to Collaborative Value Quantification

[Context and motivation] Most software products are developed and improved over time in iterative releases. Defining the contents of the next product release is an important, but challenging activity, as a large number of potential requirements is typically available. [Question/problem] Implementing these requirements in a single release is impossible, and prioritizing them is hard: which requirements deliver the most value, and what is their value exactly? A study among European software companies in the context of the Flexi project revealed that this release definition challenge is still significant, in spite of the available state-of-the-art. [Principle ideas/results] This paper reports on a number of myths surrounding release definition we observed during the study, and explains shortcomings of the available state-of-the-art in a context where many requirements should be considered and defining and quantifying value is hard. [Contribution] We then propose a novel approach for reducing the risk of making wrong choices, based on emerging social technologies.

Tool support for technology scouting using online sources

This paper describes a prototype of a software tool implementing an entity resolution method for topic-centered expert identification based on bottom-up mining of online sources. The tool extracts and unifies information extracted from a variety of online sources and subsequently builds a repository of user profiles to be used for technology scouting purposes.

A Collaborative Decision Support Platform for Product Release Definition

In this paper, we propose a collaborative decision support platform that supports the product manager in defining the contents of a product release. The platform allows interactive and collaborative decision making by facilitating the exchange of information about product features among individual autonomous stakeholders, providing reputation-enhanced collaboration, ensuring a positive collaboration atmosphere by avoiding public stakeholder ratings and reconciling individual goals with group decisions.

A semantic model of events for integrating photovoltaic monitoring data

Solar plants typically consist of several thousands of passive photovoltaic modules that are connected via thousands of string boxes to hundreds of inverters. In addition, a solar plant has meteo-sensors, power meters and control switches. All these components continuously generate data that is collected by monitoring systems or SCADA systems on-site. From there onwards, this data is pushed to remote analysis servers. The optimal exploitation of this data is hampered by a lack of harmonisation and standardisation in the photovoltaic domain. The data-generating components originate from several different manufacturers, models and versions, and their output is thus not easily commensurable. Crucial for this paper is the fact that conceptually identical failure events are not logged with the same identifying labels. Therefore, every analysis of monitoring system data coming from photovoltaic plants needs an initial integration step to resolve this labeling issue. Our proposal is to facilitate the integration with semantic modelling by means of creating a photovoltaic event ontology with an SWRL reasoning layer.

Data-driven Relevancy Estimation for Event Logs Exploration and Preprocessing.

With the realization of the industrial IoT, more and more industrial assets are continuously monitored by loggers that report events (states, warnings and failures) occurring in or around these devices. Unfortunately, the amount of events in these event logs prevent an efficient exploration, visualization and advanced exploitation of this data. Therefore, a method that could estimate the relevancy of an event is crucial. In this paper, we propose 10 methods, inspired from various research fields, to estimate event relevancy. These methods have been benchmarked on two industrial datasets composed of event logs from two photovoltaic plants. We have demonstrated that a combination of methods can detect irrelevant events (which can correspond to up to 90% of the data). Hence, this is a promising preprocessing step that can help domain experts to explore the logs in a more efficient way and can optimize the performance of analytical methods by reducing the training dataset size without losing information.

Semantic Modelling in Support of Adaptive Multimodal Interface Design

The design of multimodal interfaces requires intelligent data interpretation in order to guarantee seamless adaptation to the user’s needs and context. HMI (human-machine interaction) design accommodates varying forms of interaction patterns, depending on what is most appropriate for a particular user at a particular time. These design patterns are a powerful means of documenting reusable design know-how. The semantic modelling framework in this paper captures the available domain knowledge in the field of multimodal interface design and supports adaptive HMIs. A collection of multimodal design patterns is constructed from a diversity of real-world applications and organized into a meaningful repository. This enables a uniform and unambiguous description easing their identification, comprehensibility and applicability.

Towards a semantic modelling framework in support of multimodal user interface design

In this paper, we propose a semantic modelling framework to capture the available domain knowledge in the field of multimodal interface design and to support designers in their daily design tasks.