DAASE (Dynamic Adaptive Automated Software Engineering) is a five site project between UCL, Birmingham, Stirling, York and Queen Mary University. The lead at each site is, respectively, Professors Harman, Yao, Burke and Clark and Dr Ochoa, with Professor Harman as the overall project director. The project also has a growing list of industrial partners, which currently includes Air France - KLM, Berner and Mattner, BT Laboratories, Dstl, Ericsson, GCHQ, Honda Research Institute Europe, IBM, Microsoft Research and VISA UK.

DAASE builds on two successful longer larger projects, funded by the EPSRC and which were widely regarded as highly successful and ground breaking. The project also draws inspiration and support from and feeds into the rapidly growing worldwide Search Based Software Engineering (SBSE) community. A repository of SBSE papers and people can be found here.

Current software development processes are expensive, laborious and error prone. They achieve adaptivity at only a glacial pace, largely through enormous human effort, forcing highly skilled engineers to waste significant time adapting many tedious implementation details. Often, the resulting software is equally inflexible, forcing users to also rely on their innate human adaptivity to find "workarounds". Yet software is one of the most inherently flexible engineering materials with which we have worked, DAASE seeks to use computational search as an overall approach to achieve the software's full potential for flexibility and adaptivity. In so-doing we will be creating new ways to develop and deploy software. This is the new approach to software engineering DAASE seeks to create. It places computational search at the heart of the processes and products it creates and embeds adaptivity into both. DAASE will also create an array of new processes, methods, techniques and tools for a new kind of software engineering, radically transforming the theory and practice of software engineering.

Leveraging multi-source information to build statement-level defect prediction models is a two site project between UCL, and Nanjing University. Most research on defect predictions are at module-level which require developers to inspect the whole module for finding faults. However, only a very small percentage of statements in a module is defective. Therefore, it will waste a large amount of inspecting effort. In order to help developers locate defective statement more quickly, this project try to leverage multi-source information to build statement-level defect prediction models. Specifically, we try to leverage the following information to build statement-level defect prediction models: the dependencies between statements, the warnings by static analysis tools, and the information from developer quality. The main research contents of this project are as follows: 1) statement-level feature extraction from multi-source information; 2) modeling techniques by using the features from multi-source information; 3) prediction effectiveness analysis for statement-level defect prediction models. The objective of this project is to make the defect prediction model more practical.