Efficient Co-Evolution of Software Architectures and Artificial Intelligence Components

Artificial intelligence (AI) is pervading the world around us. It is the driving enabler for innovations and products from autonomous vehicles, to enhanced health care, to efficient crisis response, to simple things like recommending the right movie for inspiration. However, often AI does not deliver services alone but is embedded inside the architecture of software systems.

One great benefit of software is its means for adaptation. However, the embedding of AI components in software architectures introduces new challenges. AI components are often not easily adapted and changing interfaces might mean costly retraining of the AI or it might require the augmentation or creation of new datasets. Nevertheless, some evolution steps can be efficiently supported for AI components in software architectures. As an example, as part of the software of a self-driving car we might have an AI component that detects pedestrians, cyclists, and traffic lights. A software evolution step might change component interfaces to detecting dangers instead of the previously more detailed classification. Here the creation of new training data and costly retraining might not be necessary if we can compose the existing AI component with one that extracts dangers from classified objects.

This project aims to enable efficient co-evolution of software architectures and AI components by providing a catalogue of typical evolution patterns and to suggest suitable adaptations both on the software architecture level and on the AI level to make evolution more efficient. It is expected that this holistic view will be able to leverage techniques from software architecture and from AI that complement each other to provide timely answers to existing and emerging software evolution challenges.

The research will be evaluated in the context of autonomous vehicles in urban environments (see the department’s project DriverLeics https://ringert.blogspot.com/search/label/DriverLeics) and on a drone for search and rescue missions (also available at the department). We are building links with industrial partners in the area of autonomous driving to apply our research.

Entry requirements
Applicants are required to hold/or expect to obtain a UK Bachelor Degree 2:1 or better in a relevant subject. The University of Leicester English language requirements apply where applicable.

How to apply
The online application and supporting documents are due by Monday 21st January 2019.

Any applications submitted after the deadline will not be accepted for the studentship scheme.
References should arrive no later than Monday 28th January 2019.

Applicants are advised to apply well in advance of the deadline, so that we can let you know if anything is missing from your application.

Required Materials:

1. Online application form
2. Two academic references
3. Transcripts
4. Degree certificate/s (if awarded)
5. Curriculum Vitae
6. CSE Studentship Form
7. English language qualification

Applications which are not complete by the deadline will not be considered for the studentship scheme. It is the responsibility of the applicant to ensure the application form and documents are received by the relevant deadlines.

All applications must be submitted online, along with the supporting documents as per the instructions on the website.

Please ensure that all email addresses, for yourself and your referees, are correct on the application form.

Project / Funding Enquiries
Application enquiries to [Email Address Removed]
Closing date for applications – 21st January 2019