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An empirical investigation into the estimation of software development effortHughes, Robert T. January 1997 (has links)
Any guidance that might help to reduce the problems of accurately estimating software development effort could assist software producers to set more realistic budgets for software projects. This investigation attempted to make a contribution to this by documenting some of the practical problems with introducing structured effort estimation models at a site in the United Kingdom of an international supplier of telephone switching software. The theory of effort modelling was compared with actual practice by examining how the estimating experts at the telephone switching software producer currently carried out estimating. Two elements of the estimation problem emerged: judging the size of the job to be done and gauging the productivity of the development environment. Expert opinion was particularly important to the initial process, particularly when existing software was being enhanced. The study then identified development effort drivers and customised effort models applicable to real-time telecommunications applications. Many practical difficulties were found concerning the actual methods used to record past project data, although the issues surrounding these protocols appeared to be rarely dealt with explicitly in the research literature. The effectiveness of the models was trialled by forecasting the effort for some new projects and then comparing these estimates with the actual effort. The key research outcomes were, firstly the identification and validation of a set of relevant functional effort drivers applicable in a real-time telecommunications software development environment and the building of an effective effort model, and, secondly, the evaluation of alternative prediction approaches including analogy or case-based reasoning. While analogy was a useful tool, some methods of implementing analogy were flawed theoretically and did not consistently outperform 'traditional' model building techniques such as Least Squares Regression (LSR) in the environment under study. This study would, however, support analogy as a complementary technique to algorithmic modelling
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