Estimating Software-Intensive Projects in the Absence of Historical Data

Aldo Dagnino

ABB Research, USA

Track: Software Engineering in Practice
Session: Metrics and Evaluation
This paper describes a software estimation technique that can be used in situations where there is no reliable historical data available to develop the initial effort estimate of a software development project. The technique described incorporates a set of key estimation principles and three estimation methods that are utilized in tandem to deliver the estimation results needed to have a robust initial estimation. An important contribution of this paper is bringing together into ONe Software Estimation Tool-kit (ONSET) multiple concepts, principles, and methods in the software estimation field, which are typically discussed separately in the estimation literature and can be employed when an organization does not have reliable historical data. The paper shows how these principles and methods are applied to derive estimates without the need of using complex or expensive tools. A case study is presented using ONSET which was carried out as an estimation pilot study conducted in one of the software development Business Units of ABB. The results of this pilot project provided insights on how to implement ONSET across ABB software development business units. Practical guidance is offered in this paper on how an organization that does not have reliable historical data can begin to collect data to use in future projects using ONSET. In contrast to many papers that describe estimation approaches, this paper explains how to use a combination of judgment-based and model-based methods such as the Planning Poker, Modified Wideband Delphi, and Monte Carlo simulation to derive the initial estimates. Once an organization begins collecting reliable historical data, ONSET will provide even more accurate estimation results and a smoother transition to the use of model-based estimation methods and tools can be achieved.