SERL - Software Engineering Research Laboratory

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https://hdl.handle.net/10292/1680
The Software Engineering Research Lab (SERL) at AUT University undertakes world-class research directed at understanding and improving the practice of software professionals in their creation and preservation of software systems. We are interested in all models of software provision – bespoke development, package and component customisation, free/libre open source software (FLOSS) development, and delivery of software as a service (SaaS). The research we carry out may relate to just one or all of these models.

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Browsing SERL - Software Engineering Research Laboratory by Subject "Accuracy"

Now showing 1 - 3 of 3
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    Fuzzy logic for software metric models throughout the development life-cycle
    (IEEE Computer Society Press, 1999-07-01) Gray, AR; MacDonell, SG
    One problem faced by managers who are using project management models is the elicitation of numerical inputs. Obtaining these with any degree of confidence early in a project is not always feasible. Related to this difficulty is the risk of precisely specified outputs from models leading to overcommitment. These problems can be seen as the collective failure of software measurements to represent the inherent uncertainties in managers' knowledge of the development products, resources, and processes. It is proposed that fuzzy logic techniques can help to overcome some of these difficulties by representing the imprecision in inputs and outputs, as well as providing a more expert-knowledge based approach to model building. The use of fuzzy logic for project management however should not be the same throughout the development life cycle. Different levels of available information and desired precision suggest that it can be used differently depending on the current phase, although a single model can be used for consistency
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    Using prior-phase effort records for re-estimation during software projects
    (IEEE Computer Society Press, 2003) MacDonell, SG; Shepperd, MJ
    Estimating the effort required for software process activities continues to present difficulties for software engineers, particularly given the uncertainty and subjectivity associated with the many factors that can influence effort. It is therefore advisable that managers review their estimates and plans on an ongoing basis during each project so that growing certainty can be harnessed in order to improve their management of future project tasks. We investigate the potential of using effort data recorded for completed project tasks to predict the effort needed for subsequent activities. Our approach is tested against data collected from sixteen projects undertaken by a single organization over a period of eighteen months. Our findings suggest that, at least in this case, the idea that there are 'standard proportions' of effort for particular development activities does not apply. Estimating effort on this basis would not have improved the management of these projects. We did find, however, that in most cases simple linear regression enabled us to produce better estimates than those provided by the project managers. Moreover, combining the managers' estimates with those produced by regression modeling also led to improvements in predictive accuracy. These results indicate that, in this organization, prior-phase effort data could be used to augment the estimation process already in place in order to improve the management of subsequent process tasks. This provides further confirmation of the value of local data and the benefits of quite simple quantitative analysis methods.
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    Visualization and analysis of software engineering data using self-organizing maps
    (IEEE Computer Society Press, 2005) Macdonell, S
    There is no question that accuracy is an important requirement of classification and prediction models used in software engineering management. It is, however, just one of a number of attributes that contribute to a model being 'useful'. Understandably much research has been undertaken with the objective of maximizing model accuracy, but this has often occurred with little regard for these other model attributes, which might include cost-effectiveness, credibility and, for want of a better term, meaningfulness. The research described in this paper addresses both model accuracy and meaningfulness as conveyed by self-organizing maps (SOMs). SOMs are neural-network based representations of data distributions that provide two-dimensional depictions of multi-dimensional relationships. As such they can enable developers and project managers (and researchers) to visualize often complex interactions among and between software measurement data. We illustrate the effectiveness of SOMs by building on two previous empirical studies. Not only are the maps able to portray graphically the distributions of variables and their interrelationships, they also prove to be effective in terms of classification and prediction accuracy. As a result we believe that they could be a useful supplementary tool for researchers and managers concerned with understanding, modeling and controlling complex software projects.
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