Research Associate in Machine Learning for Software Engineering

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Detail information about job Research Associate in Machine Learning for Software Engineering. Terms and conditions vacancy

Full-time fixed term contract until 30 April 2019.

Together our staff and students are discovering innovative ways to change the world for the better and there has never been a more exciting time for you to join us. We are ranked in the top 1% of universities worldwide and have an international reputation for excellence in teaching and research. We are led by discovery and innovation, with the synergy between research and learning at the core of our activity.

In this role you will research, design, implement and evaluate machine learning techniques and tools for predicting software defects, as part of the EPSRC-funded project “Stable Prediction of Defect-Inducing Software Changes (SPDISC)”.

Software systems have become ever larger and more complex. This inevitably leads to software defects, whose debugging is estimated to cost the global economy 312 billion USD annually. Reducing the number of software defects is a challenging problem, and is particularly important considering the strong pressure towards rapid delivery. Such pressure impedes different parts of the software source code to all receive equally large amount of inspection and testing effort.

With that in mind, machine learning approaches have been proposed for predicting defect-inducing changes in the source code as soon as these changes finish being implemented. Such approaches could enable software engineers to target special testing and inspection attention towards parts of the source code most likely to induce defects, reducing the risk of committing defective changes.

However, the predictive performance of existing approaches is unstable, because the underlying defect generating process being modelled may vary over time (i.e., there may be concept drift). This means that practitioners cannot be confident about the prediction ability of existing approaches -- at any given point in time, predictive models may be performing very well or failing dramatically.

The SPDISC project aims at creating more stable models for predicting defect-inducing changes, through the development of a novel machine learning approach for automatically adapting to concept drift. When integrated with software versioning systems, the models will provide early, reliable and automated defect-inducing change alerts throughout the lifetime of software projects. This approach will involve online learning, class imbalance learning and transfer learning.

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Informal enquiries are welcome and should be made to Dr Leandro Minku on [email protected] or 0116 252 3905.

The closing date for this post is midnight on 25 September 2017.

We anticipate that assessments will take place during the week starting 9 October 2017.