Empirical findings on team size and productivity in software development

Abstract

The size of software project teams has been considered to be a driver of project productivity. Although there is a large literature on this, new publicly available software repositories allow us to empirically perform further research. In this paper we analyse the relationships between productivity, team size and other project variables using the International Software Benchmarking Standards Group (ISBSG) repository. To do so, we apply statistical and machine learning approaches to a preprocessed subset of the ISBSG repository to facilitate the study. The results show some expected correlations between productivity, effort and time as well as corroborating some other beliefs concerning team size and productivity. In addition, this study concludes that in order to apply statistical or data mining techniques to these type of repositories extensive preprocessing of the data needs to be performed due to ambiguities, wrongly recorded values, missing values, unbalanced datasets, etc. Such preprocessing is a difficult and error prone activity that would need further guidance and information that is not always provided in the repository.

Stakeholders' Information Needs for Artifacts and Their Dependencies in a Real World Context

“…Also, since the two observed teams exhibit characteristics in terms of size and roles similar to others reported in the research literature, e.g. [24], [25], we believe that the impact of the single company is not a substantial limitation.…”

“…These roles are a subset of the fairly extensive stakeholder roles identified by Acuna et al [23] and Yilmaz et al [24]. The size of the teams, 13 and 16, is comparable to other software development teams in industry as reported by [25] and [26]. Except for one of the line managers and some software testers, all members of each team shared an office space.…”

Stakeholders' social interaction in requirements engineering of open source software

“…Putnam sampled 491 projects and concluded that productivity is higher for smaller teams with an optimal group size of 3-5 staff [161]. The rule of thumb [92] -teams of 9 or more are significantly less productive than smaller teams -defines another empirical threshold which is supported by studies like [85] and [166].…”

Intuition and missing links in Project Management: a case study from the Elbanna (2015) model

“…Individuals use metaphors to help explain their intuition to themselves and to share it with others, since metaphors involve the transfer of information from a relatively familiar domain. Rodríguez et al (2012) analyse the relationships between productivity, team size and other project variables using the International Software Benchmarking Standards Group repository. The results showed that there are statistical correlations between team size, effort, productivity and project duration; however, these correlations are not always what one would expect from the literature or intuition.…”