As in more traditional development processes also in agile and iterative methodologies, estimation of development effort without imposing overhead on the project and the development team is of paramount importance. This analysis proposes a new effort estimation model aimed at iterative development environments, which are not suitable for description by traditional prediction methods. We propose a detailed development methodology, discuss a number of detailed architectures of such models (including a wealth of augmented regression models and neural networks) and include a thorough case study of XP carried out in two real semi-industrial projects.
Top13.1 Effort Estimation In Agile Environments Using Multiple Projects
As in more traditional development processes also in agile and iterative methodologies, estimation of development effort without imposing overhead on the project and the development team is of paramount importance. This analysis proposes a new effort estimation model aimed at iterative development environments, which are not suitable for description by traditional prediction methods. We propose a detailed development methodology, discuss a number of detailed architectures of such models (including a wealth of augmented regression models and neural networks) and include a thorough case study of XP carried out in two real semi-industrial projects. The results of this research evidences that in the XP environment under study the proposed incremental model outperforms traditional estimation techniques most notably in later iterations of development. Moreover, when dealing with new projects, the incremental model can be developed from scratch without resorting itself to historic data.
Effort prediction has always been perceived as a major topic in software engineering. The reason is quite evident: many software projects run out of budget and schedule because of an underestimation of the development effort. Since the pioneering work by Putnam (1978), Boehm (1981; 200) and Albrecht & Gaffney (1983), there have been many attempts to construct prediction models of software cost determination. An overview of current effort estimation techniques, their application in industry, and their drawbacks regarding accuracy and applicability can be found in (Lederer & Prasad, 1995; Boehm et al., 2000; Sauer & Cuthbertson, 2003; Moløkken-Østvold et al., 2004). The most prominent estimation model comes in the form of the so-called COCOMO family of cost models (Boehm et al., 1995). While capturing the essence of project cost estimation in many instances, they are not the most suitable when we are faced with more recent technologies and processes of software development such as agile approaches and small development teams. Moreover, models such as COCOMO II depend quite heavily on many project-specific settings and adjustments, whose impact is difficult to assess, collect, and quantify (Menzies et al., 2005). What makes the situation even worse, is the fact that in agile processes an effective collection of such metrics and the ensuing tedious calibration of the models could be quite unrealistic. As in other fields of software engineering a major problem in the development of effort estimation models is scarcity of experimental data. Most case studies, surveys and experiments on effort prediction found in the literature suffer from at one or more of several drawbacks: