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Code and commit metrics of developer productivity: a study on team leaders perceptions.
- Source :
- Empirical Software Engineering; Jul2020, Vol. 25 Issue 4, p2519-2549, 31p
- Publication Year :
- 2020
-
Abstract
- Context: Developer productivity is essential to the success of software development organizations. Team leaders use developer productivity information for managing tasks in a software project. Developer productivity metrics can be computed from software repositories data to support leaders' decisions. We can classify these metrics in code-based metrics, which rely on the amount of produced code, and commit-based metrics, which rely on commit activity. Although metrics can assist a leader, organizations usually neglect their usage and end up sticking to the leaders' subjective perceptions only. Objective: We aim to understand whether productivity metrics can complement the leaders' perceptions. We also aim to capture leaders' impressions about relevance and adoption of productivity metrics in practice. Method: This paper presents a multi-case empirical study performed in two organizations active for more than 18 years. Eight leaders of nine projects have ranked the developers of their teams by productivity. We quantitatively assessed the correlation of leaders' rankings versus metric-based rankings. As a complement, we interviewed leaders for qualitatively understanding the leaders' impressions about relevance and adoption of productivity metrics given the computed correlations. Results: Our quantitative data suggest a greater correlation of the leaders' perceptions with code-based metrics when compared to commit-based metrics. Our qualitative data reveal that leaders have positive impressions of code-based metrics and potentially would adopt them. Conclusions: Data triangulation of productivity metrics and leaders' perceptions can strengthen the organization conviction about productive developers and can reveal productive developers not yet perceived by team leaders and probably underestimated in the organization. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 13823256
- Volume :
- 25
- Issue :
- 4
- Database :
- Complementary Index
- Journal :
- Empirical Software Engineering
- Publication Type :
- Academic Journal
- Accession number :
- 143784386
- Full Text :
- https://doi.org/10.1007/s10664-020-09820-z