In this paper we explore opportunities, challenges, and obstacles that Functional Size Measurement (FSM) experts assume to be in automatically derived functional size, directly from the software project code itself. We designed a structured survey, that was answered by 336 FSM specialists. A majority of the respondents consider FSM to be an important tool for decision making. No indications are found for any perceived impact of agile methodology on the difficulty of applying FSM. Respondents overall think of automated FSM as important, but also difficult to realize. 54% of the respondents think that automated FSM will help measurement specialists, while 44% thinks that it will help decision makers too. The most preferred FSM method for automation is COSMIC (25%), followed by IFPUG (21%) and Nesma (16%). Respondents perceive automated FSM to be most suitable for baselining, benchmarking, and maintenance and legacy purposes.

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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.@en