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Using Machine Learning to find bugs

Full-time PhD studentship funded by Huawei


Much of the time and cost in developing software is spent fixing bugs (errors) reported by users or testers. Due to staff turnover, outsourcing, and the complexity and size of software systems, developers may not be familiar with the code they have to fix, further increasing time and cost. 

Our state-of-the-art search engine (reference below) helps developers find the source of errors more quickly: given a bug report, it recommends (in ranked order, like a web search engine) which files may need to be fixed. Our approach uses heuristics rather than history. The aim of this PhD research is to use machine learning to improve the accuracy of the recommendations, by training the system on the text of past bug reports and on the comments and code of the software system being analysed.

This full-time PhD research will be carried out in collaboration with Huawei, which is funding the Empirical Data-Driven Bug Localisation in Software Development (EDBL) project, of which this PhD is a part of.


Skills Required

  • Strong software development skills, preferably in Python
  • Good communication and documentation skills
  • Knowledge of machine learning techniques
  • Experience of creating datasets and experimental design is useful


Background reading



Queries are welcome. Please email Dr Yijun Yu ( or Dr Michel Wermelinger (


Key dates

Application cut-off date: 31 May 2022 (Extended)

Expected start date: 1 October 2022 or sooner, if possible


How to apply

For application guidance, please visit here. Your research proposal should briefly summarise related work and outline how you would approach the problem of recommending which files to fix for a given bug report, using Machine Learning.





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