Fully-funded PhD scholarship

Department of Computer Science, University College London

Artificial Intelligence & Analytics for Software Engineering

A fully-funded PhD scholarship is available under the supervision of Dr. Federica Sarro at the University College London (UCL), London, United Kingdom.

The successful applicant is expected to research novel methods equipping software engineers with analytics techniques and intelligent automated tools to support their day-to-day decisions/tasks and to switch from a “gut feel” to an “evidence-based” approach in Software Engineering activities including, but not limited to, project management, requirements elicitation, and software testing.

This PhD scholarship represents an exciting opportunity to delve into an important and timely research area on a border of software engineering, optimisation, artificial intelligence and data science. Thus, it is well-suited to students with a strong interest and aptitude in the application of artificial intelligence, predictive analytics, machine learning and optimisation techniques to software engineering problem. A PhD within this area will prepare the candidate to undertake academic research career and industrial research and development IT career in Software Engineering.

Skills and Prerequisites

We look for a highly motivated candidate with a bachelor’s degree with first or upper second-class Honours, and/or a distinction at master’s level in Computer Science, Software Engineering, Machine Learning or a closely related subject, and preferably with a strong interest and background in software engineering, data analytics or optimisation, and solid programming skills. Applicants with other qualifications and sufficient relevant experience and background knowledge may be considered. All the applicants should meet the admissions criteria for the UCL Department of Computer Science (CS) PhD programme listed here.

What We Offer

The successful PhD candidate will receive a strong career development support, will have access to a robust (doctoral research training programme), dedicated research resources, training in transferable skills, visiting speaker seminar programme, conference allowance, and will be associated with well-renowned research centres and groups at UCL (CREST,SSE,UCLAppA). In addition, PhD students will be encouraged to undertake training and development in teaching and deliver teaching/research assistantship duties on a paid basis to further enhance their experience in preparation for their future career.
The student will also enjoy a very welcoming and multicultural community: UCL is proud of its longstanding commitment to equality and to providing a learning, working and social environment in which the rights and dignity of its diverse members are respected. For more information about UCL and its CS Department, you can watch short videos about the (life as a student at UCL) and (our research vision), and have a look at the (CS web site ).

How to Apply

Applications should be made following the UCL admission process.
Read the instruction carefully and clearly indicate in your application (and cover letter) Dr. Federica Sarro as the potential supervisor and this studentship. Applicants are strongly encouraged to include in their personal statement a description of the research they aim to carry out in any of the following areas: Predictive Models and Data Analytics for Software Engineering, App Store Mining and Analysis, Search Based Software Engineering. Also, any co-authored publications can be attached to the application.
Informal enquiries and expression of interest can be made by e-mail to Dr. Sarro (f.sarro@ucl.ac.uk).


I review applications on a rolling basis. If you intend to submit (or have submitted) an application send me an e-mail at f.sarro[at]ucl.ac.uk.

Suggested reading on Software Analytics

T. Menzies, T. Zimmermann, Software Analytics: So What?, IEEE Software 2013, Article pre-print
F. Sarro, A. Petrozziello, M. Harman, "Multi-Objective Effort Estimation", in Procs. of the 38th International Conference on Software Engineering (ICSE 2016), pp. 619-630. Article pre-print
W. Martin, F. Sarro, M. Harman, Y. Jia, Y. Zhang, "A Survey on App Store Analysis for Software Engineering" IEEE Journal of Transactions on Software Engineering (2017). Article pre-print
D. Bowes, T. Hall, M. Harman, Y.Jia, F. Sarro, F. Wu "Mutation-aware Fault Prediction", in Procs. of the 25th International Symposium on Software Testing and Analysis, (ISSTA 2016), pp. 330-341 Article pre-print