survey | s.e.a.l. Blog

Among the benefits provided by Continuous Integration (CI), increased team productivity and integration frequency are perceived as the main advantages. However, changes that contain defects or that suffer from a poor-quality can lead to build failures that stop a team from delivering. The recent Report on the State of DevOps states: “When failures occur, it can be difficult to understand what caused the problem” and previous work found that developers spend on average one hour to fix build breaks!

In our group at the University of Zurich (Switzerland), we are developing new strategies to provide developers with the right assistance to solve build failures faster and more efficiently. To achieve this, we first need to understand the state of practice from real developers and we would like to learn about your personal experience with build failures in this survey.

We would really appreciate if you could find the time to fill out the following survey to help us in our research.

Filling out the survey will take you about 7 minutes. Please note that participating in the questionnaire is completely anonymous, but we will publish the anonymized answers as part of a scientific publication.

If you have any questions about the questionnaire or our research, please do not hesitate to contact us.

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Image from “Arduino Jenkins CI build monitor using car lights”, Gordons Garage, YouTube, 2016.

This work has been conducted by André Meyer (UZH), Thomas Zimmermann (Microsoft Research) and Thomas Fritz (UBC). This research has been published to the industrial papers track at the ESEM’17 in Toronto. Thomas Zimmermann will present it on Thursday, November 9th, 2017 at 1pm in Session 4B: Qualitative Research. Download Pre-Print

Studying Developers’ Perceptions of Productivity instead of Measuring it

To overcome the ever-growing demand for software, we need new ways of optimizing the productivity of software developers. Existing work has predominantly focused on top-down approaches for defining or measuring productivity, such as lines of code, function points, or completed tasks over time. While these measurements are valuable to compare certain aspects of productivity, we argue that they miss the many other factors that influence the success and productivity of a software developer, such as the fragmentation of their work, their experience, and so on. A developer who spends the workday with writing a high-quality test-case or helping a co-worker would have a bad productivity-score with said measurements. Hence, in our previous work we looked at productivity from the bottom-up, looking at developers’ individual perceptions of productivity contrary to what was done in previous work. We found that while perceptions of productivity are indeed very individual, they follow certain habitual patterns each day (e.g. Morning-People, Low-At-Lunch People, and Afternoon-People) and there are activities that most developers consider as unproductive or productive.

Similar Perceptions of Productivity

This previous work however, left us questioning if there are possibly more people with similar perceptions of productivity that can be clustered together. To investigate this, we run an online survey with 413 professional software developers who currently work at Microsoft (average experience 9.6 years) and asked them four questions asking them to describe productive (Q1) and unproductive (Q2) workdays, to rate their agreement with statements on factors that might affect productivity (Q3) and to rate the interestingness of productivity measures at work (Q4).

We found out that developers can roughly be clustered into six groups with similar perceptions: the lone, focused, balanced, leading, and goal-oriented developer. This allows us to abstract and simplify the variety of individual perceptions into groups and optimize productivity for these groups instead of individuals. In the following, I will describe the specific characteristics of these groups:

Some just love creative tasks with no clear goal, while others prefer measurable tasks.

The social developers feel productive when helping coworkers, collaborating and doing code reviews. To get things done, they come early to work or work late and try to focus on a single task.
The lone developers avoid disruptions such as noise, email, meetings, and code reviews. They feel most productive when they have little to no social interactions and when they can work on solving problems, fixing bugs or coding features in quiet and without interruptions. To reflect about work, they are mostly interested in knowing the frequency and duration of interruptions they encountered. Note that this group of developers is almost the opposite of the first group (the social developer) in how productive they feel when encountering social interactions.
The focused developers feel most productive when they are working efficiently and concentrated on a single task at a time. They are feeling unproductive when they are wasting time and spend too much time on a task, because they are stuck or working slowly. They are interested in knowing the number of interruptions and focused time.
The balanced developers are less affected by disruptions. They are less likely to come early to work or work late. They are feeling unproductive, when tasks are unclear or irrelevant, they are unfamiliar with a task, or when tasks are causing overhead.
The leading developers are more comfortable with meetings and emails and feel less productive with coding activities than other developers. They feel more productive in the afternoon and when they can write and design things. They do not like broken builds and blocking tasks, preventing them (or the team) from doing productive work.
The goal-oriented developers feel productive when they complete or make progress on tasks. They feel less productive when they multi-task, are goal-less or are stuck. They are more open to meetings and emails compared to the other clusters, in case they help them achieve their goals. In contrast to group 3 (the focused developer), goal-oriented developers care more about actually getting stuff done (i.e. crossing items off the todo-list), while the focused developer cares more about working efficiently.

Optimizing Productivity for Different Groups of Developers

The six clusters and their characteristics provide relevant insights into groups of developers with similar productivity perceptions that can be used to optimize the work and flow on the team and the individual level. The differences between software developers’ preferred collaboration and work styles show that not all developers are alike, and that the cluster an individual or team belongs to could be a basis for tailoring actions for improving their work and productivity.

For example, on the team level, we could provide quiet, less interruption-prone office to the lone and focused developers (cluster 2 and 3), and seat social developers (cluster C1) who feel more comfortable with discussions every now and then. Another example is task assignments, assigning an explorative task for a new product that is very open without clear goal might be less suitable for the goal-oriented developer (cluster 6) as opposed to the social and leading developer (cluster 1 and 5) who prefer explorative tasks that require intensive collaboration.

Not everyone feels productive when spending time in meetings.

On the individual level, developers might benefit from tailored user experiences for their (development) tools. Maybe someday, we can build virtual assistants, e.g. Cortana/Alexa for Developers, that recommend (or automatically take) actions, depending on the developers’ cluster. For example, they could block out notifications from email, Slack, and Skype during coding sessions for the lone developer (cluster 2) but allow them for the social developer (cluster 1). Or they could recommend the focused developer (cluster 3) to come to work early to have uninterrupted work time, or suggest the balanced developer (cluster 4) to take a break to avoid boredom and tiredness. Or they could help with scheduling meetings, depending on the users’ preferences.

In the paper (find a pre-print here) you may find more detailed explanations into the study method, and a much more detailed discussion of the clusters.