System Design: Using UML Use Cases

In this lecture we cover how UML Use Cases can be used for requirements capture. We look at the anatomy of a Use Case Description, and the way in which use cases can be brought together in a use case diagram. We also look at the way that use cases can be derived from problems using noun verb analysis.

System Design: UML Activity and Sequence Diagrams

In this session we look at how to model flow of control and interactions between components using UML Activity and Sequence Diagrams. This is an introductory session and so for Activity Diagrams we only cover branching, forks and joins and swim lanes, and for Sequence we cover lifelines, messages and returns, and alt, par and opt frames.

System Design: UML Class Diagrams

In this session we look at UML Class Diagrams and how they fit into both the family of UML models, and also the software engineering process. We look at some basic features of class diagrams including properties, operations, associations, generalisation, aggregation and composition.

Case Study Envisioning and Engagement

This is a presentation that introduces the envisioning (set up) stage of a project or case study. it sets envisioning in a framework of software engineering and agile methodologies. The presentation also covers techniques for engaging with stakeholders in the domain of the project: building a co-designing team; information gathering; and the ethics of engagement. There is a short section on sprint planning and managing the project backlog (agile using a burndown chart.

SEG Deliverable 4: Application Increment 3

This is the specification for the 4th deliverable of the Agile Software Engineering Group Project. The 4th deliverable is code increment 3 of the application built in the 3rd sprint

COMP2211 SEG Final (5th) Deliverable

Lecture slides about the final deliverable of the Software Engineering Group project. This covers product evaluation, Teamworking experience evaluation, and a personal reflection

Introduction to Soft Systems Modelling

Two lectures that introduce the idea of modelling in the large, and contrasts hard system and soft system modelling. The second lecture goes into detail on a number of specific methods for analysing a system (CATWOE and CSH) and on modelling a system (Systems Diagrams and Personas).

Data-Driven Text Generation using Neural Networks & Provenance is Complicated and Boring — Is there a solution?

Title: Data-Driven Text Generation using Neural Networks Speaker: Pavlos Vougiouklis, University of Southampton Abstract: Recent work on neural networks shows their great potential at tackling a wide variety of Natural Language Processing (NLP) tasks. This talk will focus on the Natural Language Generation (NLG) problem and, more specifically, on the extend to which neural network language models could be employed for context-sensitive and data-driven text generation. In addition, a neural network architecture for response generation in social media along with the training methods that enable it to capture contextual information and effectively participate in public conversations will be discussed. Speaker Bio: Pavlos Vougiouklis obtained his 5-year Diploma in Electrical and Computer Engineering from the Aristotle University of Thessaloniki in 2013. He was awarded an MSc degree in Software Engineering from the University of Southampton in 2014. In 2015, he joined the Web and Internet Science (WAIS) research group of the University of Southampton and he is currently working towards the acquisition of his PhD degree in the field of Neural Network Approaches for Natural Language Processing. Title: Provenance is Complicated and Boring — Is there a solution? Speaker: Darren Richardson, University of Southampton Abstract: Paper trails, auditing, and accountability — arguably not the sexiest terms in computer science. But then you discover that you've possibly been eating horse-meat, and the importance of provenance becomes almost palpable. Having accepted that we should be creating provenance-enabled systems, the challenge of then communicating that provenance to casual users is not trivial: users should not have to have a detailed working knowledge of your system, and they certainly shouldn't be expected to understand the data model. So how, then, do you give users an insight into the provenance, without having to build a bespoke system for each and every different provenance installation? Speaker Bio: Darren is a final year Computer Science PhD student. He completed his undergraduate degree in Electronic Engineering at Southampton in 2012.