Paper detail

Towards a Theory of Affect and Software Developers' Performance

For more than thirty years, it has been claimed that a way to improve software developers' productivity and software quality is to focus on people. The underlying assumption seems to be that "happy and satisfied software developers perform better". More specifically, affects-emotions and moods-have an impact on cognitive activities and the working performance of individuals. Development tasks are undertaken heavily through cognitive processes, yet software engineering research (SE) lacks theory on affects and their impact on software development activities. This PhD dissertation supports the advocates of studying the human and social aspects of SE and the psychology of programming. This dissertation aims to theorize on the link between affects and software development performance. A mixed method approach was employed, which comprises studies of the literature in psychology and SE, quantitative experiments, and a qualitative study, for constructing a multifaceted theory of the link between affects and programming performance. The theory explicates the linkage between affects and analytical problem-solving performance of developers, their software development task productivity, and the process behind the linkage. The results are novel in the domains of SE and psychology, and they fill an important lack that had been raised by both previous research and by practitioners. The implications of this PhD lie in setting out the basic building blocks for researching and understanding the affect of software developers, and how it is related to software development performance. Overall, the evidence hints that happy software developers perform better in analytic problem solving, are more productive while developing software, are prone to share their feelings in order to let researchers and managers understand them, and are susceptible to interventions for enhancing their affects on the job.

preprint2016arXivOpen access

Signal facts

What is known right now

Open access1 author2 topics

Next steps

Decide what to do with this paper

Use like or dislike for the fast social read. The more specific scholarly feedback stays available below when needed.

Log in to curate

Reading frame

Keep the important context close to the paper

Keep the important signals around this paper in one place: votes, save state, collection context, reviews and the metadata you need before deciding what to do next.

Institutions

Add specific reaction

Move through the context

Research map

Open full explorer

Move through nearby people, institutions, topics and adjacent work without leaving the paper page.

Building this map preview

BZPEER is loading the nearby papers, people, topics and institutions for this page.

Structured reviews

0 review(s)

ContributeLeave structured feedbackUse the review template when you have a concrete strength, concern or method question.Open review form

No structured reviews yet. High-signal critique starts here.

Work discussion

0 comment(s)

DiscussAdd a high-signal commentKeep quick notes, caveats and replication pointers separate from formal reviews.Open comment form

No discussion yet. The first strong comment sets the tone.