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Top1. Introduction
A wider use of productivity measures started with the industrialization era. The complexity of the measures has increased since those days. And measures have to take into account now more items than just the manufactured objects and the labor effort. These items that compose productivity measures are twofold: inputs (the elements needed in the production process) and outputs (the elements produced in the production process). In addition, factors, which are not considered neither inputs nor outputs, influence the productivity results (Lagerström, Würtemberg, Holm, & Luczak, 2012; Trendowicz & Münch, 2009). A relationship between these elements (inputs and outputs) is commonly established in order to construct a productivity measure. Typically, this relationship is a ratio, i.e. how much output is produced per unit of input.
Software Engineering (SE) is a new industry in comparison with traditional manufacturing sectors where the productivity measurement was born. In this area, productivity measurement has been present in research since the late 70s and beginning of the 80s (Brooks Jr., 1985). The productivity measures used in those years followed a ratio approach between outputs and inputs, and new approaches started to be considered due to the increase on the area research. The most used inputs were time and effort while the most used outputs were source code, and later functionality. Nowadays, the most considered inputs are still the same. Nevertheless, the functionality is more used than the source code, which has been shelved, in the most commonly used measures (Petersen, 2011).
However, SE practitioners can be classified as knowledge workers which are significantly different than the workforce that was employed at the beginning and middle of the last century. These practitioners handle other type of inputs, in addition to the resources given by the manufacturing workers (their skills, strength, and time). They give to the employers their knowledge, experience, and social competences among others. On the other hand, they do not produce a single output, as industrial age workers did. They produce an amalgam of outputs including intangible elements such as quality or added value.
Any worker performs a job position regardless of the industry in which he/she works. Each job position represents a unique definition of a job within an organization. These positions can be generalized in order to make them applicable to other more specific job positions. For example, a definition of the software engineer job could be obtained from O*Net (“Software Engineer (O*Net Definition),” 2010); nevertheless this position has be characterized to each organization in order to adapt it to their reality, missions and goals. Each job position requires a set of inputs in order to produce a set of outputs within a production process and with the interaction of other job positions. So, if the job positions are different among themselves, then the productivity measures considered, and later used, for different jobs can be different. One example within SE is represented by the use of lines of code or functionality as the solo output in productivity measures. So, do all the jobs in SE produce these outputs? Are there any other possible outputs that could be considered when measuring SE practitioners’ productivity?
In addition, the level of measurement has to be taken into account. Within SE, there is a lack of research on the lower levels (e.g., team and individual level) for productivity measures (Petersen, 2011). Also, the same measures are used in various levels (e.g., using a project level measurement to measure a programmer productivity) despite of their differences (Hernández-López, 2012; Hernández-López, Colomo-Palacios, & García-Crespo, 2013). Considering that the granularity of possible measures for inputs and outputs is not the same at different levels of measurement, then using the same measures at different levels do not make sense (e.g., project and worker levels). Thus, using the same measures at job level and in higher levels can be seen as controversial.