There is growing interest in applying Scrum practices in Global Software Development to leverage the advantages of both. However, the effective use of Scrum practices largely depends on close interactions between project stakeholders. The distribution of project stakeholders in GSD provides significant challenges related to project collaboration processes that may limit the use of Scrum. However, project managers increasingly seek to use the Scrum model in their distributed projects. While there is an emerging body of industrial experience, there are limited empirical studies that discuss Scrum tailoring in GSD. The paper reports a multi-case study that investigates the impact of key project contextual factors on the use of Scrum practices in GSD. This study is relevant to researchers and practitioners who are seeking ways to use Scrum in GSD and improve project effectiveness.

This paper investigates the manner in which Barry Boehm and his work have had an influence upon the research at UNSW into software development cost modeling and estimation. During the period 1979 to the present, faculty and students have investigated cost models for the commercial software development environment, project elapsed time compression, software sizing, estimation accuracy, and analysis of project history databases. This investigation reveals the foresight contained within the Boehm publications and the significant contribution made by these over the last three decades.

Software process improvement has been a focus of industry for many years. To assist with the implementation of process improvement, we provide an approach to recover process enactment data. The goal of our method is to uncover the actual process used and thereby provide evidence for improving the quality of a planned software process that is followed by an organization in the future. The recovered process model (or patterns) is presented at the same level of abstraction as the planned process model. This allows an easy and clear method to identify the distance between a planned process model and the actual project enactment. We investigate the enactment of a defined software process model from the view of understanding the opportunity for process model improvement from the viewpoint of the project managers in the context of a small software development organization. We collected data from one of our collaboration organizations and then applied our method to a case study. The consistencies between a planned process model and the project enactment were measured. The outcomes of our method provide precise information including qualitative and quantitative data to assist project managers with process improvement in future practice. The main contribution of our work is to provide a novel approach to assist software process improvement by recovering a model from process enactment data. © 2008 Springer-Verlag Berlin Heidelberg.

Data-intensive analogy has been proposed as a means of software cost estimation as an alternative to other data-intensive methods such as linear regression. Unfortunately, there are drawbacks to the method. There is no mechanism to assess its appropriateness for a specific data set. In addition, heuristic algorithms are necessary to select the best set of variables and identify abnormal project cases. We introduce a solution to these problems based upon the use of the Mantel`s correlation randomization test called Analogy-X. We use the strength of correlation between the distance matrix of project features and the distance matrix of known effort values of the data set. The method is demonstrated using the Desharnais data set and two random data sets, showing 1) the use of Mantel`s correlation to identify whether analogy is appropriate, 2) a stepwise procedure for feature selection, as well as 3) the use of a leverage statistic for sensitivity analysis that detects abnormal data points. Analogy-X thus provides a sound statistical basis for analogy, removes the need for heuristic search, and greatly improves its algorithmic performance.

Software process modeling and simulation hold out the promise of improving project planning and control. However, purely quantitative approaches require a very detailed understanding of the software project and process, including reliable and precise project data. Contemporary project management defines the success of project as a cube, rather than the traditional single point, which allows the management of software project semi-quantitatively with uncertainty-tolerance. This paper introduces semi-quantitative simulation into software project planning and control, and develops a practical approach to enhance the confidence of project success under uncertainty and contingency. We illustrate its value and flexibility by an example implementation with a simplified software process model.

Software process simulation models hold out the promise of improving project planning and control. However, quantitative models require a very detailed understanding of the software process. In particular, process knowledge needs to be represented quantitatively which requires extensive, reliable software project data. When such data is lacking, quantitative models must impose severe constraints, restricting the value of the models. In contrast qualitative models are able to cope with imprecise knowledge by reasoning at a more abstract level. This paper illustrates the value and flexibility of qualitative models by developing a model of the software staffing process and comparing it with other quantitative staffing models. We show that the qualitative model provides more insights into the staffing process than the quantitative models because it requires fewer constraints and can thus simulate more behaviors. In particular, the qualitative model produces three possible outcomes: adding staff can increases project duration (i.e. Brooks` Law), adding staff may not affect duration, or adding staff may decrease duration. The qualitative model allows us to determine the conditions under which the different outcomes can occur. © 2006 IEEE.