Department: School of Electronic Engineering & Computer Science
Salary: £33,615 - £37,411 per annum (Grade 4)
Reference: QMUL17132
Date posted: 07-Dec-2018
Closing date: 06-Jan-2019
Job profile
<https://webapps2.is.qmul.ac.uk/jobs/getfile?fname=QMUL17132_QMUL17132_PDRA_JP_FINAL07122018.docx>
Apply online
<https://ig24.i-grasp.com/fe/tpl_QMUL01.asp?newms=rf&ID=QMUL17132>
Overview:
Software is now at the heart of almost everything we do in the world. This
software remains largely handmade, and as such, is prone to defects.
Testing detects only a subset of software defects with the rest lying
dormant, sometimes for years. When these defects emerge in software systems
the safety and business consequences can be severe. Software failures and
their damaging consequences are regularly reported in the press. Finding
and fixing defects has been an intransigent problem over many years. The
traditional approach to this problem relies on finding defects during
testing then developers manually fixing those defects afterwards.
In this project we establish a new technique to automatically fix predicted
defects in software code before testing. We use machine learning-based
defect prediction information to generate automatic fixes using Genetic
Improvement. Our approach aims to offer developers effective fixes to code
which is predicted as defective. A higher proportion of the fixes our
approach offers to developers should be acceptable, generated quicker and
available earlier in the development cycle than previous attempts at
automated repair. Importantly, our approach targets a wider pool of defects
as it specifically includes targeting those dormant defects which are not
identified by testing.
Using our approach the developer will always remain in control of the code
produced. Fixes are suggested, and the developer is the 'gate-keeper',
deciding if a suggested fix is accepted, rejected, or can itself be
modified to improve the code. One of the tangible outputs of the project
will be a defect fixing tool (Fixie), which will provide support to
developers in their daily coding activities. The tool will be developed in
collaboration with several industrial partners and will be empirically
evaluated throughout the project.
Applicants must have a PhD awarded in Computer Science, Electric/Electronic
Engineering or a related topic, experience conducting research in computer
science to a high standard and a track record of publishing in high-quality
conference venues and journals.
Strong ability to proficiently organise and prioritise own work and
organise research within the project timetable and the ability to present
material verbally and visually in a seminar is also an essential criteria.
The post is full time, fixed term appointment for 2 years, with an expected
start date of 01 March 2019. Starting salary will be in the range of
£33,615 - £37,411 per annum, inclusive of London Allowance. Benefits
include 30 days annual leave, pension scheme and interest-free season
ticket loan.
Candidates must be able to demonstrate their eligibility to work in the UK
in accordance with the Immigration, Asylum and Nationality Act 2006. Where
required this may include entry clearance or continued leave to remain
under the Points Based Immigration Scheme.
Informal enquiries should be addressed to John R. Woodward at
j.woodward@qmul.ac.uk.
Details about the school can be found at www.eecs.qmul.ac.uk
To apply, please click the link below.
Candidates are kindly requested to upload documents totaling no more than
10 pages.
Please note large documents, e.g., PhD thesis/Research papers, are not
forwarded to the interview panel.
The closing date for applications is 6 January 2019. Interviews are
expected to be held shortly thereafter.
*Valuing Diversity & Committed to Equality *
*QMUL is proud to be a London Living Wage Employer*
(excuse terse response - sent from mobile device)
----thank you,
j.woodward@qmul.ac.uk
http://www.eecs.qmul.ac.uk/~jwoodward/
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