Making use a new open-multipurpose framework for more realistic estimation process in project management
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AbstractThe current turbulent times call for adaptability, especially in non-repetitive endeavours being a vital characteristic of project management. The research organized along five objectives commenced in the autumn of 2008 with a pilot study. Then it proceeded through an inductive research process, involving a series of interviews with well-recognized international experts in the field. In addition conceptualized long-running observation of forty-five days was used, before proposal of a new framework for improving the accuracy of estimates in project management. Furthermore, the framework’s “know-how to apply” description have been systematically reviewed through the course of four hundred twenty-five days of meetings. This achieved socially agreed understanding assured that it may be possible to improve accuracy of estimates, while having flexible, adaptable framework exploiting dependency between project context and conditioned by it, use of tools and techniques.
CitationHussain ZI and Lazarski AB (2016) Making use a new open-multipurpose framework for more realistic estimation process in project management. In: Proceedings of the British Academy of Management Annual Conference. BAM2016. 6-8 Sep 2016. Newcastle University, Newcastle, UK.
Link to publisher’s versionhttp://conference.bam.ac.uk/BAM2016/htdocs/conference_papers.php?track_name=%20Organisational%20Transformation,%20Change%20and%20Development
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Analogy-based software project effort estimation. Contributions to projects similarity measurement, attribute selection and attribute weighting algorithms for analogy-based effort estimation.Neagu, Daniel; Cowling, Peter I.; Azzeh, Mohammad Y.A. (University of BradfordDepartment of Computing School of Computing, Informatics & Media, 2010-10-01)Software effort estimation by analogy is a viable alternative method to other estimation techniques, and in many cases, researchers found it outperformed other estimation methods in terms of accuracy and practitioners¿ acceptance. However, the overall performance of analogy based estimation depends on two major factors: similarity measure and attribute selection & weighting. Current similarity measures such as nearest neighborhood techniques have been criticized that have some inadequacies related to attributes relevancy, noise and uncertainty in addition to the problem of using categorical attributes. This research focuses on improving the efficiency and flexibility of analogy-based estimation to overcome the abovementioned inadequacies. Particularly, this thesis proposes two new approaches to model and handle uncertainty in similarity measurement method and most importantly to reflect the structure of dataset on similarity measurement using Fuzzy modeling based Fuzzy C-means algorithm. The first proposed approach called Fuzzy Grey Relational Analysis method employs combined techniques of Fuzzy set theory and Grey Relational Analysis to improve local and global similarity measure and tolerate imprecision associated with using different data types (Continuous and Categorical). The second proposed approach presents the use of Fuzzy numbers and its concepts to develop a practical yet efficient approach to support analogy-based systems especially at early phase of software development. Specifically, we propose a new similarity measure and adaptation technique based on Fuzzy numbers. We also propose a new attribute subset selection algorithm and attribute weighting technique based on the hypothesis of analogy-based estimation that assumes projects that are similar in terms of attribute value are also similar in terms of effort values, using row-wise Kendall rank correlation between similarity matrix based project effort values and similarity matrix based project attribute values. A literature review of related software engineering studies revealed that the existing attribute selection techniques (such as brute-force, heuristic algorithms) are restricted to the choice of performance indicators such as (Mean of Magnitude Relative Error and Prediction Performance Indicator) and computationally far more intensive. The proposed algorithms provide sound statistical basis and justification for their procedures. The performance figures of the proposed approaches have been evaluated using real industrial datasets. Results and conclusions from a series of comparative studies with conventional estimation by analogy approach using the available datasets are presented. The studies were also carried out to statistically investigate the significant differences between predictions generated by our approaches and those generated by the most popular techniques such as: conventional analogy estimation, neural network and stepwise regression. The results and conclusions indicate that the two proposed approaches have potential to deliver comparable, if not better, accuracy than the compared techniques. The results also found that Grey Relational Analysis tolerates the uncertainty associated with using different data types. As well as the original contributions within the thesis, a number of directions for further research are presented. Most chapters in this thesis have been disseminated in international journals and highly refereed conference proceedings.
The importance of contextual factors on the accuracy of estimates in project management. An emergence of a framework for more realistic estimation processHussain, Zahid I.; Lazarski, Adam (University of BradfordSchool of Management, 2014)Successful projects are characterized by the quality of their planning. Good planning that better takes into account contextual factors allows more accurate estimates to be achieved. As an outcome of this research, a new framework composed of best practices has been discovered. This comprises an open platform that project experts and practitioners can work with efficiently, and that researchers can develop further as required. The research investigation commenced in the autumn of 2008 with a pilot study and then proceeded through an inductive research process, involving a series of eleven interviews. These consisted of interviews with four well-recognized experts in the field, four interviews with different practitioners and three group interviews. In addition, a long-running observation of forty-five days was conceptualized, together with other data sources, before culminating in the proposal of a new framework for improving the accuracy of estimates. Furthermore, an emerging framework – and a description of its know-how in terms of application – have been systematically reviewed through the course of four hundred twenty-five days of meetings, dedicated for the most part to improving the use of a wide range of specific project management tools and techniques and to an improvement in understanding of planning and the estimation process associated with it. This approach constituted an ongoing verification of the research’s findings against project management practice and also served as an invaluable resource for the researcher’s professional and practice-oriented development. The results obtained offered fresh insights into the importance of knowledge management in the estimation process, including the “value of not knowing”, the oft-overlooked phenomenon of underestimation and its potential to co-exist with overestimation, and the use of negative buffer management in the critical chain concept to secure project deadlines. The project also highlighted areas of improvement for future research practice that wishes to make use of an inductive approach in order to achieve a socially agreed framework, rather than a theory alone. In addition, improvements were suggested to the various qualitative tools employed in the customized data analysis process.
Temporal estimation in prediction motion tasks is biased by a moving destinationFlavell, Jonathan; Barrett, Brendan T.; Buckley, John G.; Harris, J.M.; Scally, Andy J.; Beebe, Nathan B.; Cruickshank, Alice G.; Bennett, S.J. (2018-02)An ability to predict the time-to-contact (TTC) of moving objects that become momentarily hidden is advantageous in everyday life and could be particularly so in fast-ball sports. Prediction motion (PM) experiments have sought to test this ability using tasks where a disappearing target moves towards a stationary destination. Here, we developed two novel versions of the PM task in which the destination either moved away from (Chase) or towards (Attract) the moving target. The target and destination moved with different speeds such that collision occurred 750, 1000 or 1250ms after target occlusion. To determine if domain-specific experience conveys an advantage in PM tasks, we compared the performance of different sporting groups ranging from internationally competing athletes to non-sporting controls. There was no difference in performance between sporting groups and non-sporting controls but there were significant and independent effects on response error by target speed, destination speed and occlusion period. We simulated these findings using a revised version of the linear TTC model of response timing for PM tasks (Yakimoff et al. 1987, 1993) in which retinal input from the moving destination biases the internal representation of the occluded target. This revision closely reproduced the observed patterns of response error and thus describes a means by which the brain might estimate TTC when the target and destination are in motion.