Empirical Software Engineering

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Research projects falling under this theme rely on the robust analysis of data to assess, for instance,  the efficacy of new SE methods in relation to project costs, the impact of new deployment tools on release schedules, or the influence pair programming has on code defects.  We have a well-established record of research in this theme addressing a range of challenges facing software practitioners and their managers.

While in many cases the data are in the form of numbers and so may lend themselves to statistical analysis or investigation using machine learning or soft computing methods, we also have extensive expertise in the use of qualitative methods suitable for the interpretation of textual data.

Contact Prof. Stephen MacDonell for more information on work in this theme.

Example Project

Development Effort Modelling
MPhil student Kefu Deng used statistical analysis methods to investigate the accuracy of predictive models for software development effort.  The specific objective of his research was to empirically assess the value and validity of a multi-organization data set in the building of prediction models for several ‘local’ software organizations; that is, smaller organizations that might have a few project records but that are interested in improving their ability to accurately predict software project effort.  The study concludes with recommendations for both software engineering practice – in setting out a more dynamic scenario for the management of software development – and research – in terms of implications for the collection and analysis of software engineering data.

Download the thesis

Related publication:
Deng, K., & MacDonell, S.G. (2008) Maximising data retention from the ISBSG repository, in Proceedings of the Twelfth International Conference on Evaluation and Assessment in Software Engineering (EASE2008). Bari, Italy, British Computer Society, pp. on CD-ROM.

Other Current Projects

Temporal modelling in software engineering data sets.  Prof. Stephen MacDonell, Dr Rahul Premraj (Free University Amsterdam, The Netherlands).

Sensitivity analysis in software data. Prof. Martin Shepperd (Brunel University, UK).

Projects Available

Impact of sampling on empirical modeling outcomes

Optimising estimation accuracy using multiple methods

Assessing the accuracy and sensitivity of recorded effort data

Extent of change in metrics data from project inception to project closure

Theme Papers

Wang, Y., Song, Q., MacDonell, S., Shepperd, M., & Shen, J. (2009) Integrate the GM(1,1) and Verhulst models to predict software stage effort, IEEE Transactions on Systems, Man and Cybernetics - Part C: Applications and Reviews 39(6), pp.647-658.

MacDonell, S.G., & Shepperd, M.J. (2007) Comparing local and global software effort estimation models – reflections on a systematic review, in Proceedings of the 1st International Symposium on Empirical Software Engineering and Measurement. Madrid, Spain, IEEE Computer Society Press, pp.401-409.

Connor, A.M., & MacDonell, S.G. (2006) Using historical data in stochastic estimation of software project duration, in Proceedings of the Nineteenth Annual Conference of the National Advisory Committee on Computing Qualifications (NACCQ'06). Wellington, New Zealand, NACCQ, pp.53-59.

MacDonell, S.G. (2005) Invited Speaker - Effort-based re-estimation during software projects. Presented at the Meta-Level Learning (MeLLow) Workshop 2005, Bournemouth, United Kingdom.

Connor, A.M., & MacDonell, S.G. (2005) Stochastic cost estimation and risk analysis in managing software projects, in Proceedings of the ISCA 14th International Conference on Intelligent and Adaptive Systems and Software Engineering (IASSE-05). Toronto, Canada, ISCA, pp.140-144.

MacDonell, S.G. (2005) Visualization and analysis of software engineering data using self-organizing maps, in Proceedings of the 4th International Symposium on Empirical Software Engineering (ISESE05). Noosa Heads, Australia, IEEE Computer Society Press, pp.115-124.

MacDonell, S.G. (2003) Software source code sizing using fuzzy logic modeling, Information and Software Technology 45(7), pp.389-404.

MacDonell, S.G., & Shepperd, M.J. (2003) Combining techniques to optimize effort predictions in software project management, Journal of Systems and Software 66(2), pp.91-98.

MacDonell, S.G., & Shepperd, M.J. (2003) Using prior-phase effort records for re-estimation during software projects, in Proceedings of the Ninth International Symposium on Software Metrics (Metrics'03). Sydney, Australia, IEEE Computer Society Press, pp.73-86.

MacDonell, S.G., & Gray, A.R. (2003) Applying fuzzy logic modeling to software project management. In Software Engineering with Computational Intelligence. T.M. Khoshgoftaar (ed.) Boston MA, USA, Kluwer, pp.17-43 [ISBN 1-4020-7427-1].

Kitchenham, B.A., Pickard, L.M., MacDonell, S.G., & Shepperd, M.J. (2001) What accuracy statistics really measure, IEE Proceedings - Software 148(3), pp.81-85.
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Gray, A.R., & MacDonell, S.G. (1999) Software metrics data analysis - exploring the relative performance of some commonly used modeling techniques, Empirical Software Engineering 4(4), pp.297-316.

Gray, A.R., MacDonell, S.G., & Shepperd, M.J. (1999) Factors systematically associated with errors in subjective estimates of software development effort: the stability of expert judgement, in Proceedings of the Sixth International Symposium on Software Metrics (Metrics'99). Boca Raton FL, IEEE Computer Society Press, pp.216-227.

                       

Last updated: 24-Nov-2016 8.30am

The information on this page was correct at time of publication. For a comprehensive overview of AUT qualifications, please refer to the Academic Calendar.