Resource estimation models

In the last three decades, many quantitative software cost estimation models have been developed. They range from empirical models to analytical models. An empirical model uses data from previous projects to evaluate the current project and derives the basic formulae from analysis of the particular database available. An analytical model, on the other hand, uses formulae based on global assumptions, such as the rate at which developer solves problems and the number of problems available.

Most cost models are based on the size measure, such as LOC and FP, obtained from size estimation. The accuracy of size estimation directly impacts the accuracy of cost estimation. Although common size measurements have their own drawbacks, an organization can make good use of any one, as long as a consistent counting method is used.

A good software cost estimate should have the following attributes, namely being conceived and supported by the project manager and the development team, accepted by all stakeholders as realizable, based on a well-defined software cost model with a credible basis, be also based on a database of relevant project experience - similar processes, similar technologies, similar environments, similar people and similar requirements, and finally, it should be defined in enough detail so that its key risk areas are understood and the probability of success is objectively assessed.

Software cost estimation historically has been a major difficulty in software development. Lack of a historical database of cost measurement, software development involving many interrelated factors, which affect development effort and productivity, and whose relationships are not well understood, lack of trained estimators and estimators with the necessary expertise, and little penalty which is often associated with a poor estimate, altogether can trigger development obstacles.

Cost estimation remains a complex problem, which continues to attract considerable research attention. LCAG is the Task Force Group, which struggles for achieving positive results in this problem. Its researchers have attempted different approaches up to involving artificial intelligence to help in evaluation process. Today this Group can confidently state that the risk of incorrect software estimation is negligibly small due to its newly introduced research techniques.