DOI

We study whether an assumed positively skewed distribution of effort data prevents software estimators to learn over time; leading to increasing differences between planned and actual effort and a deteriorating (worsening) trend on productivity. We analyze data of 25 software releases of one application, collected over a period of six years in a public sector institution in The Netherlands. We statistically test for distribution, trend on differences between planned versus actual effort over time, and productivity of software portfolios. The key contributions of this paper are that we show that a proposed assumption that assumes any relation between a positively skewed distribution of effort data and a deteriorating productivity is not applicable to the subject dataset. We find that the effort data is to be characterized as positively skewed distributed, and we do see a shift over time from under-estimation to over-estimation. We do not find evidence for a deteriorating productivity; on the contrary productivity improves over time, indicating that estimators in the subject organization did learn.

Original languageEnglish
Title of host publicationWETSoM 2016 Proceedings of the 7th International Workshop on Emerging Trends in Software Metrics,
Place of PublicationNew York
PublisherAssociation for Computing Machinery (ACM)
Pages8-14
Number of pages7
ISBN (Electronic)978-1-4503-4177-6
DOIs
StatePublished - 2016
Event7th International Workshop on Emerging Trends in Software Metrics, WETSoM 2016 - Austin, United States
Duration: 14 May 201622 May 2016

Conference

Conference7th International Workshop on Emerging Trends in Software Metrics, WETSoM 2016
CountryUnited States
CityAustin
Period14/05/1622/05/16

    Research areas

  • Function Point Analysis, Software Economics, Software Estimation

ID: 12627819