An Application of Intelligent Data Analysis Techniques to a Large Software Engineering Dataset

Authors:
James Cain;Steve Counsell;Stephen Swift;Allan Tucker
Affiliations:
Quantel Limited, Berkshire, UK RG14 2NX;School of Information Systems, Computing and Mathematics, Brunel University, Uxbridge, UK UB8 3PH;School of Information Systems, Computing and Mathematics, Brunel University, Uxbridge, UK UB8 3PH;School of Information Systems, Computing and Mathematics, Brunel University, Uxbridge, UK UB8 3PH
Venue:
IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
Year:
2009

Citing 9
Cited 0

Refactoring object-oriented frameworks

Refactoring object-oriented frameworks
Refactoring: improving the design of existing code

Refactoring: improving the design of existing code
Finding refactorings via change metrics

OOPSLA '00 Proceedings of the 15th ACM SIGPLAN conference on Object-oriented programming, systems, languages, and applications
Undocumented Windows 2000 secrets: a programmer's cookbook

Undocumented Windows 2000 secrets: a programmer's cookbook
Using Automatic Clustering to Produce High-Level System Organizations of Source Code

IWPC '98 Proceedings of the 6th International Workshop on Program Comprehension
Dynamic bayesian networks: representation, inference and learning

Dynamic bayesian networks: representation, inference and learning
A Survey of Software Refactoring

IEEE Transactions on Software Engineering
Extracting refactoring trends from open-source software and a possible solution to the 'related refactoring' conundrum

Proceedings of the 2006 ACM symposium on Applied computing
Structured design

IBM Systems Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

Within the development of large software systems, there is significant value in being able to predict changes. If we can predict the likely changes that a system will undergo, then we can estimate likely developer effort and allocate resources appropriately. Within object oriented software development, these changes are often identified as refactorings. Very few studies have explored the prediction of refactorings on a wide-scale. Within this paper we aim to do just this, through applying intelligent data analysis techniques to a uniquely large and comprehensive software engineering time series dataset. Our analysis show extremely promising results, allowing us to predict the occurrence of future large changes.