Header

UZH-Logo

Maintenance Infos

Using Biometric Sensors to Increase Developers' Productivity


Müller, Sebastian. Using Biometric Sensors to Increase Developers' Productivity. 2016, University of Zurich, Faculty of Economics.

Abstract

The development of software is a cost- and people-intensive process. For years, the software development industry has been coping with a shortage of software developers. Besides just training even more software developers, an alternative and particularly promising way to tackle this problem, is to boost the productivity of every single developer. Traditionally, research on developers’ productivity has primarily focused on assessing their output using certain metrics and has therefore suffered from two major drawbacks: most of these approaches do not take into account the individual differences that exist between software developers, and the metrics used for these approaches can, in most cases, only be calculated once the work is done.
Emerging biometric sensors offer a new opportunity to gain a better understanding of what developers perceive during their work and thereby a new way to better understand what aspects are affecting developers’ productivity. The basic idea behind biometric sensing is to measure a person’s physiological features that in turn can be linked to a person’s psychological states. A multitude of studies in psychology have already shown that biometric measurements can be used to assess the emotional and cognitive states of a developer.
In our research, we investigate the use of biometric measurements to assess a developer’s perceived difficulty, progress and emotions while working on a change task. Based on the assumption that more difficult code has a higher likelihood to contain a bug compared to code that is perceived as being easier, we also investigate the use of biometric measurements to identify code quality concerns in the code developers are changing. Our vision is to gain a better understanding of what every individual developer experiences, feels or perceives during his/her work, and how these aspects affect his/her productivity, to suggest approaches which increase every individual developer’s productivity.
In our research, we conducted three studies, ranging from lab experiments to a two-week field study, to investigate the use of biometric sensors in a software development context. The results of our studies provide initial evidence that biometrics can be used to better understand what a developer perceives in realtime, while s/he is working on a change task. In particular, using biometric data, we were able to distinguish between positive and negative emotions, phases of high and low progress and to predict a developer’s perceived difficulty while working on a change task with high accuracy. Additionally, we were able to use biometrics to predict code quality concerns that were identified in peer code reviews. These findings open up many opportunities for better supporting developers in their work, for instance by automatically and instantaneously detecting potential quality concerns in the code, before they are committed to the code repository, or by avoiding costly interruptions when a developer is in the flow and making a lot of progress.

Abstract

The development of software is a cost- and people-intensive process. For years, the software development industry has been coping with a shortage of software developers. Besides just training even more software developers, an alternative and particularly promising way to tackle this problem, is to boost the productivity of every single developer. Traditionally, research on developers’ productivity has primarily focused on assessing their output using certain metrics and has therefore suffered from two major drawbacks: most of these approaches do not take into account the individual differences that exist between software developers, and the metrics used for these approaches can, in most cases, only be calculated once the work is done.
Emerging biometric sensors offer a new opportunity to gain a better understanding of what developers perceive during their work and thereby a new way to better understand what aspects are affecting developers’ productivity. The basic idea behind biometric sensing is to measure a person’s physiological features that in turn can be linked to a person’s psychological states. A multitude of studies in psychology have already shown that biometric measurements can be used to assess the emotional and cognitive states of a developer.
In our research, we investigate the use of biometric measurements to assess a developer’s perceived difficulty, progress and emotions while working on a change task. Based on the assumption that more difficult code has a higher likelihood to contain a bug compared to code that is perceived as being easier, we also investigate the use of biometric measurements to identify code quality concerns in the code developers are changing. Our vision is to gain a better understanding of what every individual developer experiences, feels or perceives during his/her work, and how these aspects affect his/her productivity, to suggest approaches which increase every individual developer’s productivity.
In our research, we conducted three studies, ranging from lab experiments to a two-week field study, to investigate the use of biometric sensors in a software development context. The results of our studies provide initial evidence that biometrics can be used to better understand what a developer perceives in realtime, while s/he is working on a change task. In particular, using biometric data, we were able to distinguish between positive and negative emotions, phases of high and low progress and to predict a developer’s perceived difficulty while working on a change task with high accuracy. Additionally, we were able to use biometrics to predict code quality concerns that were identified in peer code reviews. These findings open up many opportunities for better supporting developers in their work, for instance by automatically and instantaneously detecting potential quality concerns in the code, before they are committed to the code repository, or by avoiding costly interruptions when a developer is in the flow and making a lot of progress.

Statistics

Downloads

86 downloads since deposited on 26 Oct 2016
86 downloads since 12 months
Detailed statistics

Additional indexing

Item Type:Dissertation
Referees:Fritz Thomas, Gall Harald, Murphy-Hill Emerson
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Language:English
Date:2016
Deposited On:26 Oct 2016 12:00
Last Modified:26 Oct 2016 12:01
Other Identification Number:merlin-id:13927

Download

Preview Icon on Download
Preview
Filetype: PDF
Size: 36MB

TrendTerms

TrendTerms displays relevant terms of the abstract of this publication and related documents on a map. The terms and their relations were extracted from ZORA using word statistics. Their timelines are taken from ZORA as well. The bubble size of a term is proportional to the number of documents where the term occurs. Red, orange, yellow and green colors are used for terms that occur in the current document; red indicates high interlinkedness of a term with other terms, orange, yellow and green decreasing interlinkedness. Blue is used for terms that have a relation with the terms in this document, but occur in other documents.
You can navigate and zoom the map. Mouse-hovering a term displays its timeline, clicking it yields the associated documents.

Author Collaborations