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Details of Grant 

EPSRC Reference: EP/L011751/1
Principal Investigator: Hall, Professor T
Other Investigators:
Counsell, Professor SJ Bowes, Dr DH
Researcher Co-Investigators:
Project Partners:
Sky UK Limited
Department: Computer Science
Organisation: Brunel University London
Scheme: Standard Research
Starts: 28 April 2014 Ends: 27 April 2017 Value (£): 394,316
EPSRC Research Topic Classifications:
Software Engineering
EPSRC Industrial Sector Classifications:
Information Technologies
Related Grants:
Panel History:
Panel DatePanel NameOutcome
24 Oct 2013 EPSRC ICT Responsive Mode - Oct 2013 Announced
Summary on Grant Application Form
SIGNIFICANCE: Faults in software code are a significant cost to companies, as well as a risk to human safety and business success. Finding and fixing faults in code costs the UK software industry billions of pounds every year. Significant cost savings are available with even small improvements in our capability to find faults before systems are delivered to users.

BACKGROUND: Our previous work shows that during the last 10 years, 208 studies have published hundreds of different fault prediction models. These studies are usually typified by researchers applying one or more of the many modeling techniques to one or more of the many available data sets, then applying performance measures to report how well that model predicts faults.

PROBLEM: Models do not perform consistently above the current predictive performance ceiling of about 80% recall. We propose that an important contributor to this underperformance is that models treat all faults as homogeneous. No previous attempt has been made to understand what characteristics make a fault predictable or what features a model needs in order to predict faults with particular characteristics.

AIM: To build a fault prediction model ensemble which is focused on the characteristics of faults and which consistently performs above the current performance ceiling.

METHOD: This 36 month project is based on analysing the code and fault data from six commercial systems and from six open source systems. We will conduct detailed quantitative and qualitative analysis of the characteristics of the faults in these systems, identifying for example whether the characteristics of faults are problems in code interfaces, algorithmic problems, structural problems, typographic problems, etc. We will construct a set of prediction models with a large variety of features (e.g. different modeling techniques, different independent variables, etc.). We will use these models to empirically identify relationships between fault characteristics and the features of individual models. This means that we will identify what features of prediction models predict faults with particular characteristics. We will build ensembles of models with features that cover the widest range of fault characteristics. We will evaluate those models on industrial systems in collaboration with a company.

Key Findings
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Potential use in non-academic contexts
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Date Materialised
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Organisation Website: http://www.brunel.ac.uk