Extreme programming explained: embrace change
Extreme programming explained: embrace change
IEEE Software
How Developers Drive Software Evolution
IWPSE '05 Proceedings of the Eighth International Workshop on Principles of Software Evolution
Does a programmer's activity indicate knowledge of code?
Proceedings of the the 6th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering
Threats on building models from CVS and Bugzilla repositories: the Mozilla case study
CASCON '07 Proceedings of the 2007 conference of the center for advanced studies on Collaborative research
Empirical Software Engineering
A degree-of-knowledge model to capture source code familiarity
Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering - Volume 1
Are developers complying with the process: an XP study
Proceedings of the 2010 ACM-IEEE International Symposium on Empirical Software Engineering and Measurement
Are Heroes common in FLOSS projects?
Proceedings of the 2010 ACM-IEEE International Symposium on Empirical Software Engineering and Measurement
Deriving metric thresholds from benchmark data
ICSM '10 Proceedings of the 2010 IEEE International Conference on Software Maintenance
Heroes in FLOSS Projects: An Explorative Study
WCRE '10 Proceedings of the 2010 17th Working Conference on Reverse Engineering
Modeling the effects of project management strategies on long-term product knowledge
PROFES'12 Proceedings of the 13th international conference on Product-Focused Software Process Improvement
Hi-index | 0.00 |
The Truck Factor is a simple way, proposed by the agile community, to measure the system's knowledge distribution in a team of developers. It can be used to highlight potential project problems due to the inadequate distribution of the system knowledge. Notwithstanding its relevance, only few studies investigated the Truck Factor and proposed ways to efficiently measure, evaluate and use it. In particular, the effective use of the Truck Factor is limited by the lack of reliable thresholds. In this preliminary paper, we present a theoretical model concerning the Truck Factor and, in particular, we investigate its use to define the maximum achievable Truck Factor value in a project. The relevance of such a value concerns the definition of a reliable threshold for the Truck Factor. Furthermore in the paper, we document an experiment in which we apply the proposed model to real software projects with the aim of comparing the maximum achievable value of the Truck Factor with the unique threshold proposed in literature. The preliminary outcome we achieved shows that the existing threshold has some limitations and problems.