Many software development organizations strive to enhance the productivity of their developers. All too often, efforts aimed at improving developer productivity are undertaken without knowledge about how developers spend their time at work and how it influences their own perception of productivity.
To fill in this gap, we deployed a monitoring application at 20 computers of professional software developers from 4 companies for an average of 11 full work day in situ.
Corroborating earlier findings, we found that developers spend their time on a wide variety of activities and switch regularly between them, resulting in highly fragmented work.
Our findings extend beyond existing research in that we correlate developers’ work habits with perceived productivity and also show productivity is a personal matter. Although productivity is personal, developers can be roughly grouped into morning, low-at-lunch and afternoon people. A stepwise linear regression per participant revealed that more user input is most often associated with a positive, and emails, planned meetings and work unrelated websites with a negative perception of productivity.
We discuss opportunities of our findings, the potential to predict high and low productivity and suggest design approaches to create better tool support for planning developers’ work day and improving their personal productivity.
[Keywords: productivity, developer activity, work fragmentation, interruptions, human factors, user studies]
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Corroborating earlier findings, we found that developers spend their time on a wide variety of activities and switch regularly between them, resulting in highly fragmented work. The findings further emphasize how individual developers’ work days are. For example, while some participants tend to span their work days out over as many as 21.4 hours (max), most developers keep more compact work hours, on average 8.4 (SD=1.2) hours per day. From that time, they spend on average 4.3 (SD=0.5) hours on their computer. And surprisingly little of it with development related activities (eg. coding, testing, debugging): only about 30% of that time. The rest of the work day is split up into emails (15%), meetings (10%), web browsing (work related: 11%, unrelated: 6%) and other activities.
A next step was to investigate fragmentation of work in more details: Apart from meetings, developers remain only between 0.3 and 2.0 minutes in an activity before switching to another one. These very short times per activity and the variety of activities a developer pursues each day illustrate the high fragmentation of a developer’s work.
From participant’s self-reported, perceived productivity we found that although there was a lot of variation between individuals, the plots can be categorized into 3 broad groups: morning people, afternoon people, and those whose perceived productivity dipped at lunch. Morning people often come to work a little bit earlier, and get the most important things done before the crowd arrives. Afternoon people usually arrive later and spend most of their mornings with meetings and emails, and get stuff done in the afternoon, thus feeling more productive then. These results suggest that while information workers in general have diverse perceived productivity patterns, individuals do appear to follow their own habitual patterns for each day.
…Can we somehow quantify productivity? [yes, a little] We built explanatory models (stepwise linear regressions) to describe which factors (of the collected data) contributes to the productivity ratings reported by the study participant. We observe that productivity is a personal matter that varies greatly among individuals. There are some tendencies, however, such as that more user input is most often associated with a positive, and emails, planned meetings and work unrelated websites with a negative perception of productivity.
Existing, previous work predominantly focused on a single or small set of outcome measures, eg. the lines of code or function points written. While these measures can be used across developers, eg. for comparisons, they neglect to capture the individual differences in factors that impact the way that developers’ work. This suggests that measures or models that attempt to quantify productivity should take the individual differences into account, and what is perceived as productive or not; and capture the developer’s work more holistically, rather than just by a single outcome measure. Such individual models could then be used to provide better and more tailored support to developers, for instance to foster focus and flow at work. For example, we could help developers avoid interruptions at inopportune moments (see our FlowLight), increase the awareness about work and productivity using a retrospective view or help users to schedule a more productive work day, that avoids unproductive patterns as much as possible.
Finally, we examined if we can predict high and low productivity sessions based on the collected data for individual participants, using logistic regression. The results are promising and suggest that even with a relatively small number of reported productivity self-reports, it is possible to build personalized, predictive productivity models.