Improving the Requirements Engineering Process: a process oriented approach

The Requirements Engineering (RE) process plays an important role in the software development process. In order to produce quality software greater attention must be given to the improvement of RE process. In this paper five key process areas (KPAs) have been identified from the research literature in order to improve the RE process. Firstly to support a goal-based approach in the RE process; secondly to support the incremental and cyclical behaviours in the RE process; thirdly to encourage stakeholders involvement in the RE process; fourthly, to support the management of RE process and fifthly to define a planning phase for the RE process. This research project aims to show that better results will follow when the RE process supports these five KPAs. To address these KPAs, a requirement elicitation, analysis and validation method (REAVM) is proposed. A case study has been conducted in order to test and evaluate the REAVM in the real world environment

Implicit emotion detection in text

In text, emotion can be expressed explicitly, using emotion-bearing words (e.g. happy, guilty) or implicitly without emotion-bearing words. Existing approaches focus on the detection of explicitly expressed emotion in text. However, there are various ways to express and convey emotions without the use of these emotion-bearing words. For example, given two sentences: “The outcome of my exam makes me happy” and “I passed my exam”, both sentences express happiness, with the first expressing it explicitly and the other implying it. In this thesis, we investigate implicit emotion detection in text. We propose a rule-based approach for implicit emotion detection, which can be used without labeled corpora for training. Our results show that our approach outperforms the lexicon matching method consistently and gives competitive performance in comparison to supervised classifiers. Given that emotions such as guilt and admiration which often require the identification of blameworthiness and praiseworthiness, we also propose an approach for the detection of blame and praise in text, using an adapted psychology model, Path model to blame. Lack of benchmarking dataset led us to construct a corpus containing comments of individuals’ emotional experiences annotated as blame, praise or others. Since implicit emotion detection might be useful for conflict-of-interest (CoI) detection in Wikipedia articles, we built a CoI corpus and explored various features including linguistic and stylometric, presentation, bias and emotion features. Our results show that emotion features are important when using Nave Bayes, but the best performance is obtained with SVM on linguistic and stylometric features only. Overall, we show that a rule-based approach can be used to detect implicit emotion in the absence of labelled data; it is feasible to adopt the psychology path model to blame for blame/praise detection from text, and implicit emotion detection is beneficial for CoI detection in Wikipedia articles

Europäische Tagung zur Medienproduktion: Technische Universität Ilmenau, 26. Januar 2007; Tagungsband

Die Kommunikationswissenschaft beschäftigt sich disziplinübergreifend mit Kommunikationsprozessen. Zur Neuorientierung des Faches führte die (Massen-) Verfügbarkeit von sog. „Neuen Medien“. Aber auch die in den 1990er Jahren fortschreitende Digitalisierung der „Alten Medien“ (Fernsehen, Radio, Print) wird thematisiert. Technische Innovationen änderten die Organisationsformen der Medien und in Folge auch ihre Inhalte. Damit rückte die Medienproduktion ins Blickfeld der Forschung. Da die Produktion der Medien stets die Elemente Technik, Organisation und Content in einem Prozess vereint, ist ihre Analyse und Systematik nur interdisziplinär ausgerichtet sinnvoll. Im Tagungsband systematisieren Wissenschaftler und Praxisvertreter aus Deutschland, Polen, Spanien, Ukraine und der Schweiz zum ersten Mal das heterogene Forschungsfeld aus der Sicht unterschiedlicher Disziplinen